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Anthropic has launched Claude Opus 5 at the same price as Opus 4.8 while raising response quality close to Fable 5, a model that costs twice as much. In other words, with near-Fable performance at half the price, most users will likely choose Opus 5 as their default and reserve Fable 5 for the small number of tasks that truly require the highest capability ceiling. What upgrades does Claude Opus 5 bring? According to Anthropic's launch announcement, Claude Opus 5 is the most capable Opus model to date and the first Opus release in the Claude 5 generation. Anthropic describes it as proactive and capable of deep reasoning, approaching the highest intelligence of Claude Fable 5 across many domains while using only half the token budget. The API model ID is claude-opus-5. Like Opus 4.8 and Fable 5, it has a default and maximum context window of one million tokens, a 128,000-token output limit, and thinking enabled by default. It has become the default model on Claude Max and the most powerful model available on Claude Pro. It is also offered through the Claude API, Amazon Bedrock, Google Cloud, Microsoft Foundry, and GitHub Copilot. Why will many users choose Opus 5 over Fable 5? The answer is not limited to price. Four factors make Opus 5 likely to become the default choice for daily work while Fable 5 moves into a specialized role for a small number of exceptional cases. It wins more real-world evaluations than it loses On Frontier-Bench v0.1, Anthropic's automated coding evaluation, Opus 5 scores 43.3% while Fable 5 reaches only 33.7%, a gap of almost ten points in favor of Opus 5. On CursorBench 3.2 at maximum effort, Opus 5 reaches about 70.1%, less than half a percentage point behind Fable 5 while costing only half as much. Across evaluations where both models have published results, Opus 5 wins more often than it loses, and its victories are generally larger than its defeats. The fastest way to verify this is to run the same task on both models at comparable effort levels and compare the output quality instead of relying only on published benchmarks. No mandatory 30-day data retention Fable 5 and Mythos 5 are Covered Models that require prompts and outputs to be retained for 30 days for safety purposes. They do not support zero data retention (ZDR) on any platform, even when an organization already has a ZDR agreement. Opus 5, by contrast, can still operate under ZDR like Opus 4.8. For teams handling legal, medical, or financial data, this difference alone may remove Fable 5 from consideration without any performance comparison. Fewer interruptions from safety filters Anthropic says the cybersecurity classifier intervenes about 85% less often with Opus 5 than with Fable 5. For coding agents that run for hours or overnight, a request being blocked midway because it touches a safety threshold is a real workflow risk, and Opus 5 significantly reduces that frequency. Adjustable effort makes budgets easier to predict Opus 5 supports adaptive thinking with effort ranging from low to maximum. Low or medium works for fast responses and high-volume workloads, while high or maximum suits complex coding, deep research, and multi-step workflows. Because teams pay according to the selected effort instead of being locked into a fixed Fable 5 cost level, they can optimize the budget for each task rather than paying the highest rate on every request. Initial impressions after trying Opus 5 After using Opus 5 for daily writing and coding work, the clearest impression is that it is substantially smarter than Opus 4.8, especially in understanding intent on the first request without repeated explanation. For tasks such as summarizing long documents, writing code with complex branching logic, or preparing a multi-step plan, Opus 5 works smoothly and loses the thread less often than the earlier version. There is still a gap compared with Fable 5, although it is smaller than expected. On work that demands deep reasoning or autonomous execution across many consecutive steps without intervention, Fable 5 remains slightly more dependable and makes fewer mistakes. For most daily work, however, that difference is difficult to notice without placing both models side by side. If you are using Opus 4.8, this is a sensible time to upgrade. If you are choosing between Opus 5 and Fable 5 for ordinary work, Opus 5 is almost certainly sufficient without paying the premium. When is Fable 5 still the right choice? Fable 5 retains an advantage on the hardest work. On SWE-bench Pro, which uses real GitHub issues and is considered one of the strictest measures of practical coding, Fable 5 scores about 80% while Opus 5 reaches roughly 79%, a small gap that still favors Fable. Fable 5 is also the only model Anthropic positions in the Mythos class, meaning its overall capability is designed to exceed Opus. This distinction is clearest in specialized fields such as expert medical analysis and autonomous research that continues for days without supervision. In other words, Opus 5 wins in daily coding and knowledge work, while Fable 5 retains its edge on the hardest problems and fields requiring the highest possible reliability. For most users and small teams, those problems represent a small portion of daily work, making the twofold price difference difficult to justify unless their workload falls directly into that category. Quick comparison: Opus 5 vs. Fable 5 CriterionClaude Opus 5Claude Fable 5 Input price$5/million tokens$10/million tokens Output price$25/million tokens$50/million tokens Context1 million tokens1 million tokens Maximum output128,000 tokens128,000 tokens Frontier-Bench v0.1 (coding agent)43.3%33.7% SWE-bench Pro (practical coding)~79%~80% Data retentionSupports zero data retentionMandatory 30-day retention, no ZDR Safety-filter interventionAbout 85% lowerHigher Best fitDaily work, coding agents, sensitive dataDifficult research, multi-day autonomous projects, specialized medical analysis Can Opus 5 really compete with GPT-5.6? On paper, the answer is yes, but not across every category. Opus 5 leads GPT-5.6 Sol in reasoning about novel situations, computer use, and most public coding evaluations, while GPT-5.6 Sol remains ahead on some command-line and information-retrieval tests. Neither wins outright, but for the first time a mid-priced Anthropic model stands level with, and in several areas ahead of, OpenAI's flagship model. The more useful question is not which model is stronger overall but which one fits your work. If daily tasks center on code, long documents, and multi-step execution, Opus 5 is a compelling choice on both price and quality. If you already rely on the OpenAI ecosystem or need a specific GPT-5.6 strength, the switching cost may not be worthwhile. The most reliable answer is still to run the same job on both models, because benchmark tables do not always reflect real experience.

Google announced Gemini 3.6 Flash on July 21, 2026, with sharp benchmark gains over 3.5 Flash: DeepSWE rose from 37% to 49%, MLE Bench from 49.7% to 63.9%, and OSWorld Verified reached 83%. Yet 4AIVN's hands-on experience tells a very different story. The model handles small jobs reasonably well, but a multi-step plan can make it forget the objective, skip steps, and drift halfway through the work. Stronger benchmarks do not reflect real-world use According to Google's official announcement, Gemini 3.6 Flash uses 17% fewer output tokens than 3.5 Flash on the Artificial Analysis Index, while tests such as DeepSWE show token reductions of up to 65%. Its input window reaches 1,048,576 tokens and its output limit is 65,536 tokens, impressive numbers on paper. The problem is that these figures come from designed tests with a fixed objective and a relatively contained run. That is not how a real plan operates. Production work changes continuously in response to feedback rather than ending after one self-contained attempt. Following a long plan is the critical weakness In hands-on use, Gemini 3.6 Flash performs poorly as soon as it moves beyond a single task. Give it a small job with explicit checks and it can work well with few unnecessary loops. Give it a multi-step plan and it may forget the original objective, skip previously agreed steps, or drift after several turns. When corrected, it sometimes apologizes and then repeats the same mistake instead of actually fixing it. A one million token window describes input capacity, not memory quality. The model may be able to “see” the full context and still miss details during execution; one overlooked constraint can push the entire plan off course. This is not a rare random failure but a repeated weakness that is difficult to ignore. Gemini 3.6 Flash is strong at completing one job quickly, but it is not yet dependable at completing a sequence of jobs correctly. That is the gap the benchmarks do not measure. A 17% price cut may not match the quality Gemini 3.6 Flash is priced at $1.50 per million input tokens and $7.50 per million output tokens, about 17% below the $9 output price of 3.5 Flash. On the surface, this is a sensible improvement: lower cost and higher benchmark scores. But if long tasks are executed poorly, the savings can quickly disappear through repeated reminders, corrections, and complete reruns of the plan. Gemini 3.5 Flash Lite is cheaper still at $0.30 per million input tokens and $2.50 per million output tokens, but it targets simple classification and data transformation workloads that do not require the model to preserve a long plan. What do you gain and lose with Gemini 3.6 Flash? Objectively, this is not a failed upgrade. Google has likely made careful tradeoffs among output quality, speed, and cost, even if real-world behavior does not fully meet the high expectations attached to its engineering team. The improvements are real rather than purely theoretical: responses are faster, output costs are lower, and the model is efficient on short, narrow tasks such as content classification, writing one code function, or answering a specific question. In those cases, it keeps unnecessary loops to a minimum. The cost becomes visible when work extends beyond a few steps. The more constraints and earlier decisions the model must preserve, the more likely it is to drift. For coding agents or long workflows already running reliably on Claude Fable 5 or GPT 5.6, there is not yet a convincing reason to switch to Gemini 3.6 Flash solely because of benchmarks or lower pricing. Gemini 3.5 Pro is still the model to wait for Google says Gemini 3.5 Pro is still being tested with partners and will be released broadly when it is ready. The central story of this launch is therefore the sizeable gap between benchmarks and real work. Anyone looking for a dependable agent for long-running workflows may still need to wait and see whether 3.5 Pro delivers a genuine step forward. If future releases remain underwhelming in practice, Google risks surrendering its advantage to competitors including Anthropic, OpenAI, and Meta.

Spotify is turning music search into an ongoing conversation: listeners can ask for unfamiliar artists, change the mood, save a song, and explore their listening history without leaving the app. The new assistant moves AI from passive recommendations toward a tool that understands requests and performs actions. How does Spotify turn search into a conversation? According to Spotify's official announcement, eligible listeners will see new conversation controls on Home and Now Playing in the mobile app. They can type a question or press the microphone button to speak, then continue through several turns instead of entering a completely new search every time. The assistant does more than return a list of tracks. It can control what is playing, explain related information, and perform actions such as saving a track, adding it to the queue, or following an artist. For example, a listener can request artists they have never heard before and then specify that they want recent releases or something more energetic. What can the new AI assistant do? Spotify groups the experience around choosing content, understanding what is playing, and exploring listening habits. For music, listeners can request a style, artist, or mood and then revise the selection with a follow up question. For podcasts and audiobooks, they can ask about guests, authors, or related programs. The assistant can also use personal context that a general chatbot does not automatically possess. It understands playlists, favorite artists, repeat listens, and account history, so someone can ask when they first heard a track or which genres they have played most recently. That context matters because the answer is connected to actual usage rather than general knowledge alone. One request can be refined across several turns Imagine preparing for a run without knowing which playlist to open. You can request fast music from unfamiliar artists, add a favorite singer, and then limit the results to recent releases. When a suitable track appears, you can save it immediately without moving through several screens. How is this different from AI DJ and ChatGPT? AI DJ mainly acts as a host that selects music and introduces it with a generated voice, while the new assistant expands conversation across Home and Now Playing. Listeners can ask questions, redirect recommendations, and tell the app to complete specific tasks rather than simply accept the sequence chosen by the system. Spotify has also connected its service with ChatGPT, but the new experience runs directly inside the music app. Listeners do not need to leave Spotify, connect another service, and return to play the result. According to TechCrunch, Spotify combines its own AI technology with models from several providers and selects the technology that best fits each task. Spotify has not disclosed the model names or explained how requests are routed. It is therefore too early to judge the assistant's knowledge capabilities, but the use of several models suggests that Spotify does not want the product to depend on one provider. What should listeners know before trying it? The feature is rolling out gradually as a beta for Premium listeners aged 18 and older in the United States, Ireland, and Sweden. It currently works in English on iOS and Android, so listeners in Vietnam are not included in the announced availability. Spotify says responses may not always be accurate during the beta and that feedback will shape future improvements. Listeners should still verify an official source when details such as release dates, song inspiration, or artist biographies are important. Confirm that the account meets the supported market and age requirements. Try both typing and voice to see which method captures intent more accurately. Begin with a clear request and use follow up questions to refine the result. Do not treat a beta response as the only source for facts requiring high accuracy. Spotify is changing how people discover audio The important shift is not that Spotify now has another chatbot. Conversation is becoming a control layer for both content and actions inside the app. When AI understands a listener's library, history, and current track, one spoken request can replace several searches, menus, and queue adjustments. Anyone with beta access should test three situations: discovering unfamiliar artists, asking about listening history, and refining a playlist across several turns. Those tests will reveal whether the assistant truly understands personal taste or merely turns a long instruction into another search.

An AI agent may plan extremely well, yet it still cannot update Notion, read GitHub issues, or retrieve reports from Google Drive without the right connection. By combining Hermes Agent with MCP, users can turn a conversation into a practical workflow while clearly controlling which tools and permissions the agent may use. If you are not yet familiar with Hermes memory and its ability to create skills, our guide to what Hermes Agent is provides the necessary foundation. This article focuses on how MCP extends Hermes beyond the terminal so it can work with everyday data and services. What does MCP add to Hermes Agent? MCP is a connection standard between an AI application and a server that provides tools or data. It can be understood as an adapter layer: Hermes remains the agent responsible for understanding the goal and choosing the next step, while each MCP server contributes specific actions such as searching Notion, reading a pull request, creating an issue, or querying files. According to the Hermes Agent MCP documentation, Hermes supports local servers over stdio and remote servers over HTTP. At startup or after a configuration reload, Hermes discovers the tools exposed by each server and registers them in its normal tool system. Users therefore do not need to write a native Hermes tool for every service that already has a suitable MCP server. MCP does not automatically make a workflow safe. A server may expose tools that read, write, create, and delete data. Hermes supports filtering per server, allowing users to enable only the operations they need instead of exposing every capability to the model. How to connect MCP without granting excessive access The standard Hermes installation already includes MCP support. Users can open the picker with hermes mcp, view the catalog with hermes mcp catalog, and test a connection with hermes mcp test. Nous Research reviews entries before they enter the Hermes catalog, but its documentation still recommends reading the manifest, source repository, and installation commands before use. For a server outside the catalog, users can add an HTTP connection or a stdio command to config.yaml. After completing OAuth or configuring the required environment variables, reload MCP and ask Hermes to list the available tools. This simple check reveals servers that failed to connect or tools that were accidentally filtered out. Begin with read access The safest setup is to connect one server, enable read only tools, and test with nonsensitive data. Add create or update permissions only after results are stable. Deletion, sharing changes, and outbound publishing should require human approval. Notion initially needs only search and page reading access. GitHub can be limited to reading repositories, issues, and pull requests. Google Drive access should be limited by folder, account, and required OAuth scope. Three practical workflows with Notion, GitHub, and Google Drive Turn Notion into a knowledge center The official Notion MCP allows an agent to search, read, and update workspace content under the authenticated user's permissions. A useful workflow lets Hermes collect meeting notes, find relevant decisions, and prepare a summary on the project page. Hermes can create a draft first so a user can review it before updating status or assigning work. Notion MCP uses user based OAuth, so it does not fit every unattended process. For scheduled automation, verify how the server maintains authentication and avoid designing a workflow around operations that OAuth cannot support in a headless environment. Coordinate development work through GitHub The GitHub MCP Server is provided and maintained by GitHub, allowing AI tools to work with software development data according to account permissions. Hermes can read new issues, compare them with repository changes, and draft a progress report. It can then prepare issue text or release notes while waiting for an owner to approve the write operation. This workflow works best with clear criteria. For example, Hermes can summarize only pull requests merged during the previous seven days, group them by label, and connect each change to its related issue. A second MCP server can then send the result to Notion as a weekly report. Summarize files and reports from Google Drive With a compatible Google Workspace MCP server, Hermes can find Drive files, read permitted content, and feed data into a reporting process. For example, the agent can locate a sales report in a fixed folder, extract selected metrics, and create a summary for Notion or a GitHub issue. Google collects its official MCP projects in the Google MCP repository, including a path for Google Workspace integration. However, several community Drive servers have different maintenance histories. Check the source, update history, and OAuth scopes of the specific server instead of installing one based only on its name. Combine multiple MCP servers into a controlled workflow A complete workflow can begin in GitHub, use Drive as a data source, and finish in Notion. Hermes reads an issue labeled for reporting, finds the corresponding spreadsheet in Drive, produces a summary, and updates the project page. Each stage uses a different MCP tool group, while Hermes plans the sequence and passes results between stages. Do not enable parallel execution merely because a server supports it. Hermes documentation allows servers to declare parallel tool support but warns that operations reading and writing shared state can conflict. Independent read operations may run together, while Notion updates, issue creation, and file changes should remain sequential. Important: An MCP server is software that can run commands and receive credentials. Install only trusted servers, keep tokens out of prompts, filter dangerous tools, and require approval for deleting, sharing, or publishing data. How should you start the first workflow? Do not connect Notion, GitHub, and Google Drive on the same day and immediately assign a critical process. Choose one input, one output, and one completion criterion that is easy to verify. A first workflow could read closed GitHub issues and create a draft report in Notion without deletion or publishing permissions. After several stable runs, you can turn the procedure into a reusable Hermes skill and add a schedule. The real value of MCP is not the number of connected servers. It is the ability to complete a recurring workflow with a small permission surface, verifiable results, and a clear data path.

Hermes Agent, OpenClaw, and Claude Cowork are all called AI agents because they do more than answer questions. They can break an objective into multiple steps, call tools, read data, and produce a complete result. However, comparing these three products using only a feature table can easily lead to the wrong choice. Hermes Agent is designed as an agent that can learn how you work. OpenClaw is designed as a personal assistant that is always available through messaging channels, while Claude Cowork is intended for users who want to delegate office work in natural language within an environment managed by Anthropic. Therefore, the important question is not which tool is the most powerful, but how much you want to manage yourself and where you want the agent to appear in your daily workflow. Three products with different designs The differences among these three AI agent tools do not lie only in the model that performs the work. They also come from the framework surrounding the model, which manages tools, memory, access permissions, and the execution loop. This concept is explained in detail in our article What is an agent harness?, which helps explain why three products that are all called AI agents can behave so differently. Hermes Agent prioritizes a learning loop and execution environments The notable point about Hermes is that skills are not merely a list of skills that have already been installed. After completing a task, the agent can extract a useful process, save it, and improve it the next time. Our article What is Hermes Agent? explains this self learning mechanism separately. The accumulated value of this mechanism grows over time when users have recurring tasks such as analyzing projects, monitoring information sources, standardizing reports, or operating a chain of internal tools. Hermes also supports several types of sandboxes, including local execution, Docker, SSH, Singularity, and Modal. A sandbox is an isolated environment in which the agent executes commands and works with files. This flexibility lets users choose among speed, control, and isolation, but it also requires an understanding of infrastructure, access permissions, and secret management. OpenClaw uses the Gateway as its coordination center In OpenClaw, the Gateway is the control layer between the agent, devices, and communication channels. A message can become a request for the agent to read a calendar, process a file, call a service, or respond in the correct conversation. This approach feels natural for people who want to message an assistant from their phone without needing to remember where the server is running. OpenClaw is most suitable when the agent needs to react as soon as work appears, without requiring the user to open a computer or enter a separate application. Instead of waiting for you to start a work session, it remains available in the messaging channels you already use and begins processing as soon as a message arrives or a configured event is triggered. Claude Cowork provides a managed workspace Cowork reduces the amount of infrastructure that users must manage themselves. In the desktop application, users can grant access to a local folder and ask Claude to read, organize, or create files. With remote sessions, work takes place in an isolated environment on Anthropic servers, which suits long tasks that do not require a personal computer to remain active continuously. In return, the level of customization and control over the execution layer is not as broad as in a self hosted project. Cowork is better suited to people who want quick results within the Claude ecosystem and do not want to maintain a server or design a Gateway themselves. How the memory of the three tools works differently Memory in an agent should not be understood simply as storing every conversation. A useful system must know which information is worth retaining, which information matters only in the current session, and when old data should be retrieved. If it stores too little, the agent must ask the same questions repeatedly. If it stores too much, costs will certainly increase and sensitive data can easily be used in the wrong context. Hermes stands out by combining persistent memory with skills that can improve. Memory records preferences and context, while a skill records how to complete a type of task. These two layers make the agent feel as if it increasingly understands the user, but quality still depends on whether the user reviews what has been stored and removes processes that are no longer appropriate. OpenClaw runs across several channels at once, and that is also its most complicated aspect. Remembering conversation content is only one part of the problem. The harder issue is distinguishing who is speaking, which channel they are using, and which scope the work belongs to. A command sent in a company Slack group should not automatically pull in private context previously discussed on Telegram. If session configuration and identity policies should be established clearly from the beginning, even a strong model cannot rescue a system when everything remains ambiguous. Cowork limits context to each work session, reads only the files for which you grant access, and uses only the connections you allow. For people who are not accustomed to building systems, this approach is easier to control because the boundaries of each task are relatively clear. However, clear boundaries do not mean automatic understanding. You still need to explain what you want, what completion should look like, and where the data should come from. Cowork cannot infer your company context unless you actively provide it. Which type of work each tool automates best Hermes includes web tools, terminal access, MCP, scheduled runs, and subagents. MCP is a connection standard that helps an agent communicate with external data sources or applications through a consistent interface. By combining MCP with skills, users can turn an experiment into a repeatable process, such as collecting data each morning, analyzing changes, and sending a summary. OpenClaw is strong at workflows that begin with a message or an event. For example, a user can send an invoice to a private channel, after which the agent extracts the information and updates a storage system. Another example is receiving a service alert, gathering additional diagnostic data, and returning a summary directly to the operations group. Its value comes from reducing the gap between the moment a need appears and the moment the agent begins acting. Cowork suits structured office outputs. It can research a topic, synthesize data, create a document, and continue revising it according to feedback. Long running or scheduled tasks help Cowork move beyond short question and answer interactions. Even so, organizations need to inspect each connector and its access permissions before allowing the agent to work with real data stores. When deep integration with private infrastructure is required, Hermes and OpenClaw generally provide more room. When the priority is reducing the time from a request to a finished document, Cowork usually has an advantage. This is the difference between a platform intended for assembly and a product that has already been packaged. How secure are these three AI agents? There is no simple answer to the question of which one is safer because the security risks of each tool come from completely different areas. Hermes Agent: Self hosting does not automatically mean safety. The greatest risk comes from automatically generated skills because, in essence, they are pieces of code that the agent writes and then runs by itself. If they are not reviewed before scheduled execution, a skill with terminal access or permission to send data externally can do things without your knowledge. In addition, API keys and sensitive folders should not appear in prompts or be mounted directly into a sandbox when the skill does not actually need them. OpenClaw: The more channels you connect, the wider the attack surface becomes. The point most easily overlooked is sender authentication. If the Gateway trusts only a display name or a channel that has not been properly secured, a compromised messaging account may be enough for someone to issue commands to your agent. The list of people allowed to send commands and the permissions of each bot need to be reviewed whenever you add a new channel. Claude Cowork: The most concerning risk is prompt injection, which occurs when the agent reads a document or webpage containing hidden instructions intended to redirect it away from your original request. Anthropic provides safeguards and asks for confirmation before sensitive actions, but those measures do not replace your own review of the results or the need to avoid granting broader permissions than the task actually requires. Note: With any agent, do not grant permission to delete files, send external messages, or perform sensitive transactions. Start with read only mode, enable complete logging, and retain human approval for actions that require human judgment. Should you choose Hermes Agent, OpenClaw, or Claude Cowork? Every tool has its own strengths and weaknesses, so selecting the most suitable one depends on the user and the work that needs to be done. Choose Hermes Agent when you want the agent to understand how you work increasingly well Hermes suits developers, researchers, and technical teams that want an agent to learn their own processes and run on flexible infrastructure. It is particularly worth considering when tasks recur often enough for skills to create accumulated value. You need to be prepared to read logs, review skills, and manage execution environments. Best suited when: You want the agent to remember and improve work processes through repeated use. You can manage sandboxes, select models, and control access permissions yourself. Choose OpenClaw when work requires continuous communication through messages OpenClaw is suitable when the assistant needs to be present on Telegram, WhatsApp, Slack, Zalo, or similar channels. It is useful for alerts, rapid collection of requests, and automation that begins with a conversation. In return, you must manage identity, channel permissions, and Gateway stability. Best suited when: Requests usually arrive as messages or automated alerts. You need one coordination point for several different communication channels. Choose Claude Cowork when you need quick results without building a system Cowork suits content creators, analysts, and managers who need complete documents, spreadsheets, and slides without wanting to think about servers or Gateways. In return, you should understand the limits of your plan, where data travels, and which connections are enabled before introducing real work. Best suited when: You want to describe the required outcome in natural language and receive a complete output. You prioritize the convenience of a managed service over full control of the infrastructure.

Is anyone else using Codex and Claude Code side by side? I only recently discovered the Codex plugin for Claude Code, published by OpenAI itself. The useful part is not simply having another AI available. It is being able to call Codex from the current Claude Code session for a code review, an adversarial design challenge, or a separate delegated task without constantly switching tabs and sessions. What makes the Codex plugin for Claude Code useful? The openai/codex-plugin-cc plugin is intended for developers who already work in Claude Code and want to add Codex to that workflow. Instead of allowing both agents to edit the same file at the same time, you can assign clear roles: Claude Code implements and Codex reviews, or Claude Code keeps the main thread while Codex investigates an independent problem in the background. The official plugin provides review commands such as /codex:review and /codex:adversarial-review, delegation through /codex:rescue, and job or session management through /codex:transfer, /codex:status, /codex:result, and /codex:cancel. Codex therefore becomes a collaborator inside the Claude Code workflow rather than a separate window. It does not create a separate Codex runtime The plugin uses the Codex CLI and Codex app server installed on the same machine. It also reuses the local authentication state, current repository checkout, and existing config.toml settings. Integration is straightforward, but each request still contributes to the user's Codex usage limits. Requirements before installation You need Node.js 18.18 or later and either a ChatGPT subscription, including Free, or an OpenAI API key. If Codex CLI is missing, /codex:setup can offer installation guidance. You can also install it manually with npm install -g @openai/codex and sign in with !codex login. How to install the Codex plugin in Claude Code Run these commands in Claude Code: /plugin marketplace add openai/codex-plugin-cc /plugin install codex@openai-codex /reload-plugins /codex:setup The last command checks whether Codex is installed and authenticated. Once setup is complete, the Codex slash commands should appear in Claude Code, along with the codex:codex-rescue agent under /agents. Try a background review first A low-risk first run is /codex:review --background. Use /codex:status to monitor it and /codex:result to retrieve the final review. Multi-file reviews can take time, so background mode keeps Claude Code available for other work. Three effective Codex and Claude Code workflows The value of the plugin comes from role design. If both agents modify the same area without boundaries, the result may be conflicting edits, repeated analysis, and wasted context. The following workflows make ownership clearer. Let Claude implement and Codex review After Claude Code completes a feature, run /codex:review for a read-only review. It can inspect current uncommitted changes or compare the branch against a base with /codex:review --base main. Because Codex does not edit files in this mode, the developer keeps control of what is accepted. For example, after Claude adds a payment flow across several modules, Codex can inspect logic errors, edge cases, and cross-file side effects. Claude Code can then evaluate the findings and apply only the changes that make sense. Delegate an entire task to Codex Use /codex:rescue for a problem that can be isolated, such as /codex:rescue --background investigate why the integration test is flaky. Claude Code can continue working on the interface or documentation while Codex investigates in the background. Rescue supports --background, --wait, --resume, and --fresh. Define the expected output and file scope before delegating. A vague instruction to fix everything while Claude Code is also editing the repository can still create collisions. A good task has a specific goal, completion criteria, and a clearly owned part of the codebase. Use adversarial review to challenge the project direction /codex:adversarial-review is designed to question implementation and design decisions rather than merely find bugs. For example, /codex:adversarial-review --base main challenge the caching and retry design asks Codex to inspect assumptions, trade-offs, alternatives, and risks such as data loss, race conditions, rollback, or reliability. This is where the two agents may appear to argue, but the debate only helps when a human sets a narrow question, requests evidence, and defines a decision rule. Otherwise, the review can become a chain of opinions with no practical outcome. Transfer sessions and manage background jobs /codex:transfer creates a persistent Codex thread from the current Claude Code session and prints a codex resume <session-id> command. It is useful when a discussion has grown beyond a short review and you want to continue directly in the Codex App or TUI without manually rewriting the context. Monitor, retrieve, and cancel work For background tasks, /codex:status shows progress, /codex:result returns the stored output and session ID, and /codex:cancel stops an active job. These commands prevent multi-agent work from becoming a black box. When a task drifts from its goal, canceling early is usually cheaper than waiting and starting over. Watch for review loops and usage limits Important: OpenAI explicitly warns that the optional review gate can create a long-running Claude/Codex loop and drain usage limits quickly. When enabled with /codex:setup --enable-review-gate, the plugin uses a Stop hook, which is an automated trigger that runs when Claude is about to finish its response, to start a targeted Codex review. If it finds an issue, Claude's response is blocked so Claude can address it first. This can be valuable before shipping, but it should not be left unattended. A practical safety checklist Assign roles before running: one agent implements while the other reviews, or each owns a separate task. Limit the scope by naming the branch, files, risk area, and completion criteria. Use background mode for large reviews and check progress periodically. Enable the review gate only while actively monitoring it, then disable it with /codex:setup --disable-review-gate. Do not let Claude review all Codex output and then ask Codex to review every Claude revision without a clear stopping rule. Use /codex:cancel when a task moves in the wrong direction. How can Codex and Claude Code work well together? The official OpenAI plugin offers a cleaner alternative to keeping Codex and Claude Code open in separate tabs or letting both agents edit the same file. Claude Code can remain the coordinator while Codex reviews, challenges a design, or owns a separate task. A sensible starting point is one small /codex:review --background run, followed by status, result, and cancel. Try rescue, transfer, and the review gate only after the basic workflow is familiar. The two systems can complement each other well, provided a person still sets the boundaries, budget, and stopping point.

A useful report usually consumes time in three places: finding sources, organizing evidence, and checking whether conclusions are actually supported. Hermes Agent fits this work because web search, browser automation, files, memory, delegation, and skills can live inside one workflow. Once configured well, a manual process can become a reusable routine for weekly reporting, content audits, and keyword research.The practical method shared here is simple: research first, save evidence, draft last, and run SEO as a separate review stage. This is slower than asking an agent to write immediately, but the output is easier to verify and less likely to blur facts with assumptions.Why Hermes Agent fits reporting and SEOAccording to the official Hermes Agent documentation, the system includes more than 60 tools covering web search, browser automation, terminal, files, memory, cron, and delegation. Hermes also has a learning loop that can create skills from experience and improve them during use. That matters when a report or SEO checklist must run repeatedly.If you are new to the platform, start with our introduction to Hermes Agent and its self learning system. This guide focuses on execution: turning a business question into a sourced report and turning a URL into an actionable SEO review.The rule used in this workflow: Hermes may search, extract, organize, and recommend. A human still approves sources, conclusions, primary search intent, and every change that can affect a live website.Four tool groups to enableWeb and search: discover sources and extract evidence.Browser: inspect rendered pages and interact when extraction is insufficient.Files: save briefs, source tables, drafts, and final reports.Memory and skills: retain brand rules, report formats, and proven procedures.Use hermes tools to inspect and configure toolsets. For web research and file writing, enable only the relevant groups instead of granting broad terminal access. A smaller permission scope reduces accidental actions and produces cleaner logs.Set up Hermes Agent for the first runInstall and choose a modelFollow the official quickstart, then run hermes setup to select a provider, model, and tools. Hermes supports Nous Portal, OpenRouter, OpenAI, and custom endpoints, so the reporting workflow does not depend on one model provider.Use a model with sufficient context for source review, but do not spend flagship model pricing on every stage. Discovery, title normalization, and table formatting can use a cheaper model. Contradiction analysis and final synthesis deserve the stronger option.Create a clear workspaceGive each project its own folder with brief.md, sources.md, notes.md, report.md, and seo-audit.md. This structure tells Hermes which file defines the request, which stores evidence, and which is the final output. It also makes later comparisons easier.In a competitor report, I create one folder per month, list five sites in the brief, and require every conclusion to point to a row in sources.md. If a claim lacks evidence, Hermes must mark it as unverified instead of filling the gap.Use Hermes Agent to create a sourced reportStep 1: Write a brief with a completion testA useful brief answers five questions: who will read the report, which decision it supports, what time range matters, which sources are acceptable, and what sections the output must contain. A broad request such as researching the AI market encourages unnecessary scope.The prompt I use is: “Read brief.md. Find no more than 12 official or reputable sources published within the last 90 days. Save the URL, access date, main evidence, and the passage supporting that evidence in sources.md. Do not write the report until the source table is complete. Create a conflict section when sources disagree.”Step 2: Research before writingHermes provides web search and extraction, while browser automation is better for rendered pages and interactions. The Web Search and Extract documentation recommends browser navigation when summarization is not enough. Search broadly first, then open only important sources in the browser.The best output at this stage is not polished prose. It is a clean source table containing title, publisher, URL, date, evidence, and confidence. Missing dates and authors should remain missing rather than being guessed.Step 3: Synthesize around the business questionAfter sources.md is approved, ask Hermes to write report.md and separate facts, interpretation, and action. Readers can then see which statements are supported and which are analytical judgments.During one content report, Hermes found that traffic declined when several pages lost internal links. Instead of claiming a search penalty, the report preserved two hypotheses: weaker internal architecture and lower search demand. The first action was restoring links and observing the result, not rewriting the entire site.A useful verification prompt: ask Hermes to add a section called “What this report does not prove.” It is often the most valuable part of the document.Use Hermes Agent for practical SEOStart with search intentProvide the URL, audience, and conversion goal, then ask Hermes to identify the primary intent, supporting questions, related entities, and page type visible in search results. A keyword list alone often creates content that repeats phrases without satisfying the reader.A practical prompt is: “Inspect this URL with the browser. Identify one primary intent and three supporting intents. Compare the title, headings, introduction, topic coverage, internal links, and schema with five relevant results. Do not recommend keyword density. Every recommendation must name the location to change and the benefit for the reader.”Run an on page checklistHermes can review titles, descriptions, canonicals, hreflang, headings, alt text, internal links, structured data, and duplicate content. Browser automation is useful because it sees the rendered page. Keep audit permissions separate from editing permissions so a review cannot silently modify production.Does the title describe the intent without duplication?Does the introduction answer the main question early?Do H2 and H3 headings create a clear reading path?Do internal links lead to deeper, relevant explanations?Does schema match content visible to users?Are image URLs valid and alt descriptions useful?Create a content brief before draftingDo not ask Hermes to write the SEO article immediately after research. Require a content brief with persona, intent, angle, H2 and H3 structure, evidence, internal links, and unresolved questions. Approve the brief before drafting.For this Hermes Agent guide, the brief includes more than the phrase “Hermes Agent SEO.” It requires an explanation of toolsets, real prompts, browser permission warnings, a link to the foundational article, and a checklist for measuring the result.Turn the workflow into a reusable skillHermes is distinguished by its skill system. The quickstart describes skills as instruction documents loaded when a task matches, while the learning loop can suggest preserving procedures after complex work. Once a reporting workflow succeeds two or three times, convert it into a skill instead of maintaining a long prompt.What a reporting skill should containTrigger conditions and accepted inputs.Required folder and file names.Source selection rules and conflict handling.Report format, source table, and unverified section.A review checklist before delivery or publication.For an SEO skill, add rules that prevent production edits, forbid unsupported schema, and protect canonical URLs. These safeguards are more important than a keyword list because they stop plausible but harmful changes.Schedule reports with cronHermes supports scheduled automation and delivery to messaging platforms. A weekly report can run on Monday morning, collect new evidence, compare it with the previous week, and send a summary. Automate reading and synthesis, but keep publication and SEO changes behind approval.Protect sensitive data: sanitize customer information, limit file and browser access, and use command approval or isolation when Hermes can access a terminal.Mistakes I encounteredThe objective is too broad“Analyze the entire website” produces a long output with little direction. “Find three evidence backed causes for an impressions decline in tool articles” produces a smaller report that supports action. Scope should connect to a decision.Writing begins before source reviewPolished prose can hide weak sources. Separating sources.md from report.md makes errors visible. Review the evidence table before synthesis.SEO is reduced to one scoreA single score does not explain what to change. Hermes should return an issue, evidence, priority, effort, and a validation method. Recommendations that cannot be measured should rank lower.The workflow never becomes a skillIf the same prompt needs correction every week, the learning loop is being wasted. Record mistakes, update the checklist, and improve the skill after every run.How should you start?Choose one small report, no more than five sources, and one clear decision. Create a brief, ask Hermes for a source table, approve the evidence, and only then permit report writing. Use the same evidence for a content brief or SEO audit, but do not let the agent publish automatically.Once the process is stable, save it as a skill and add a final review checklist. Hermes Agent is most valuable not because it writes one report faster, but because it turns a good method into a procedure that can be repeated, inspected, and improved over time.

I'd heard a lot about the gstack repo from the CEO of Y Combinator, so I got curious and installed it to try. What surprised me most wasn't the polished workflows — it was the genuinely different mindset behind them. That mindset shows up in the very first command: /office-hours, with six questions that don't ask about code at all, only the things most people haven't thought through before they start building. What is gstack and why did Garry Tan build it gstack is an open-source toolkit by Garry Tan, CEO of Y Combinator, built primarily for Claude Code. The core idea: instead of using AI as a plain code writer, Garry Tan wanted to turn Claude into a small AI agent team, where each member handles a different role — from product direction and security review to testing and release. The entire workflow runs in an ordered loop: Think → Plan → Build → Review → Test → Ship → Reflect. More specifically, gstack splits Claude Code into 23 specialized roles, and the output of each step is automatically passed to the next — no manual handoff needed. Some of the standout commands: /office-hours 6 questions that force you to rethink your feature before writing a single line of code /plan-ceo-review checks whether you're overbuilding or underbuilding relative to what's actually needed /review catches serious bugs that standard automated checks miss /qa opens a real browser, performs real interactions, finds real bugs /cso runs an automated security audit against international standards /ship syncs, tests, pushes code and opens a pull request in a single command How effective is gstack? Garry Tan says his working speed in 2026 is roughly 810 times faster than in 2013, measured by lines of completed code per day (11,417 vs 14). In 60 days, he shipped 3 production services and over 40 features — all while running Y Combinator full-time. Andrej Karpathy, co-founder of OpenAI, confirmed a similar trend, sharing that he hasn't typed a single line of code himself since December 2025 thanks to AI agents. But among all those commands, /office-hours stands out for the opposite reason from the rest, it doesn't help you work faster and it helps you avoid building the wrong thing from the start. Why Garry Tan puts /office-hours first Garry Tan placed /office-hours at the top of the workflow based on a simple observation: most products fail not because of poor code, but because they build the wrong thing. Teams spend weeks on a feature nobody needs, or build the right feature for the wrong audience, or solve a problem users already handle better another way. The command has two modes: Startup mode for founders and people building real products with real users, and Builder mode for side projects, hackathons, and open source. This article focuses on Startup mode, where the 6 questions are most directly applicable. 6 questions that stop you from building the wrong thing These aren't 6 questions to answer quickly and move on. They're designed to make you think honestly, because the more truthful your answers, the more accurately Claude can match what you actually need — saving you a significant amount of time later. You can read the full original prompts at office-hours/SKILL.md.tmpl. Demand reality: Is there a real need? Original question: "Who specifically has this problem? How are they solving it today?" Not "users in general" or "the marketing team" — the goal is to name one real person, ideally by name, who is actively struggling with a specific problem. If you can't name someone like that, you don't yet understand what they actually need. Concrete example: Instead of "users want better task management," it should be: "Minh, a project manager at a 20-person company, copy-pastes between Notion and Google Sheets every Monday morning because the two tools don't sync." Apply this to your own situation accordingly. Status quo: What are they using instead? Original question: "What is their current workaround? How much better do you need to be for them to switch?" Everyone is already solving their problem somehow — whether with Excel, sticky notes, or a WhatsApp group. If their current solution is good enough, they have no reason to migrate their data and learn an entirely new platform. Your solution needs to be meaningfully better before they'll even consider switching. Desperate specificity: Who needs this badly enough? Original question: "Who needs a solution badly enough to use your ugly beta version today?" This is the question that separates nice-to-have from must-have. If you can't find anyone willing to use an incomplete, rough, buggy version right now, the problem you're solving isn't urgent enough. Real early users are people who need a solution badly enough to tolerate an unpolished product — as long as it's moving in the right direction. Narrowest wedge: What is the smallest possible piece? Original question: "What is the smallest thing you could launch tomorrow? Not the full vision — the smallest piece." Not the first full-featured version — something even smaller than that. This question typically cuts 80% of the scope people add because they think "might as well do it while I'm here." It's a trap many builders fall into, including myself. Launch the smallest meaningful piece first, listen to real users, then decide whether to expand. Common mistake: Many people confuse "smallest piece" with "first full-featured version." The narrowest wedge truly means one small thing that solves one specific problem for one specific group of users — nothing more. Observation and surprise: Have you watched real people use it? Original question: "Have you watched real people use your product? Did they use it in ways you didn't expect?" This question is best saved for the second iteration onward, once you have something to test. Rather than asking for feedback through messages or surveys, sit and watch directly — or review screen recordings. The most valuable insights usually don't come from what users say, but from what they do that you didn't design for, or what they skip that you thought was important. Note: If you're in your first iteration and don't have a product yet, you can skip this question and come back after launching the smallest piece in step 4. Future-fit: The 2 to 3 year view Original question: "In 2-3 years, will what you're building still be relevant — or is the trend moving against you?" This isn't about predicting the future precisely. It's about avoiding building something that's already fading. If the trend is making your problem less urgent over the next two years, that's a clear signal to reconsider from the start. That said, if your goal is to move fast and capture the market before big tech ships something similar, this question can reasonably be set aside. A real example: a simple idea completely flipped In the gstack documentation, Garry Tan walks through a practical example. You open /office-hours and say: "I want to build an app that summarizes my daily work calendar." Claude doesn't agree and start executing. Instead, it pushes back: what you just described isn't a calendar summary app — it's actually a full personal AI chief of staff. These are entirely different in scope, technical complexity, and user expectations. From that single opening description, /office-hours helps you see: 5 features you were describing without realizing it 4 assumptions that need to be validated before building 3 different implementation directions with varying levels of complexity 1 recommendation: launch the smallest piece first, treat the rest as a long-term roadmap All of this happens before you write a single line of code. The output is saved as a document that subsequent steps in the workflow automatically pick up and continue from. These 6 questions work even without gstack The 6 questions from /office-hours don't require Claude Code or a gstack installation. They're a way of thinking — the same framework YC partners use to evaluate startups — and you can apply them right now with any AI tool you already have. The difference when using them through gstack is that Claude won't let you give vague answers. It pushes for specifics and won't move forward until your response is grounded enough to be useful. That's why /office-hours tends to be the most uncomfortable command in the entire toolkit — not because it's difficult to use, but because it asks exactly what you've been avoiding. Try it today: Before starting your next project, paste these 6 questions into Claude, Gemini, or ChatGPT along with your idea. Ask it to go through each question one at a time and not let you skip any. The results are often more surprising than you'd expect — even for ideas you've already thought through carefully. gstack currently has over 117k stars on GitHub and is still growing. For me, the most valuable part isn't the technical commands like /review or /ship — it's /office-hours, because it's the only command in the entire toolkit that forces you to stop and think before doing anything else.

You're out and suddenly remember a small detail in your project that needs fixing — you don't have to open your laptop or remote desktop in. With the right connection set up, ChatGPT app on your phone can become a control panel for Codex, while your computer at home or the office keeps running the actual code. ChatGPT app doesn't run Codex on your phone The easiest thing to misunderstand is thinking Codex is running directly on your phone. In reality, your phone only sends prompts, replies, approvals and follow-up messages, while the actual working environment lives on your Mac or Windows machine running Codex. In other words, ChatGPT app is the remote controller, and the host machine is where your repo, terminal, credentials, plugins, MCP servers and other tools actually live. This makes complete sense because codebases typically live on your development machine, not your phone. When you send a request like fixing a TypeScript error, running tests or checking a diff, Codex processes it inside the selected project on the host and sends results back for you to review. If you want to understand the foundation before using remote access, check out What is Codex and how to use Codex to get a clear picture of where this tool fits in your workflow. What do you need before connecting ChatGPT app to Codex? According to the latest Codex documentation from OpenAI, ChatGPT app supports controlling Codex on both macOS and Windows, though Linux is not supported yet. Notably, this feature works with all ChatGPT account types, including Free and Go — no paid plan required. You only need to make sure you're signed into the same account or workspace on both devices: ChatGPT mobile (latest version on iOS or Android) and Codex (latest version on your host machine, online and running). Your host machine must stay on and Codex must keep running for the entire time you're controlling it remotely. If the machine goes to sleep, loses its connection or Codex is closed, the connection from your phone drops immediately and any tasks in progress may be interrupted. What's worth noting is that the entire setup process starts from Codex App on the host machine and is surprisingly simple — just scan a QR code and you're done. Inside Codex App, select the mobile setup option in the sidebar, scan the QR code with your phone, then complete the confirmation in ChatGPT app. For enterprise workspaces, an admin may need to enable Remote Control permissions before you can connect. This QR code grants control over your computer, so keep it private and never share it with anyone to avoid unauthorized access to your machine. To summarize, connecting ChatGPT app to Codex is straightforward: Host machine must be online and running Codex ChatGPT app and Codex must be signed into the same account or workspace Generate the QR code in Codex on the host and complete setup on your phone MFA, SSO or passkey requirements may still apply depending on your workspace What can you do once connected? Once the host appears in Codex on your phone, you can start a new thread inside a project on the host or pick up an existing one. This is where the experience becomes genuinely useful: you can send follow-ups, answer Codex's questions, approve commands, view output, check diffs, review test results and even receive notifications when a task finishes or needs your attention. A real example: you're at a coffee shop and remember the login form has a validation bug. You open ChatGPT app, select the connected host, and ask Codex to check the auth flow, fix the email validation error and run the related tests. Codex works directly on the repo sitting on your host machine, while you review the results, approve actions when needed and decide whether to request further changes. This is also why people are starting to think of Codex and other AI-powered IDEs as a colleague working inside a real environment, not just a code suggestion tool anymore. Its strength lies in reading files, running commands, editing code and maintaining context across multiple rounds of back-and-forth. Limitations to keep in mind when using Codex from your phone Remote control depends entirely on the host machine — if your computer goes to sleep, loses its connection, closes Codex or gets signed out of the workspace, your phone loses its working environment immediately. That said, if Codex is mid-task when the connection drops, it will continue running on the host and notify you once your phone reconnects, so there's less to worry about if your phone suddenly loses signal during a running task. One more thing to note: on Windows, tasks using Computer Use require an appropriate foreground session, so this setup is not a complete replacement for sitting directly in front of your machine. It also helps to draw a clear line between handing off a focused task and reviewing large changes. Your phone works well for small bugs, running tests, quick questions about a specific file, reviewing short tasks or checking task status. However, anything requiring a high level of attention should still be reviewed on a larger screen to avoid missing details. How to use it effectively in practice The most effective approach is to hand off tasks with a clear scope and specific expected outcomes. Instead of saying "fix the login", describe exactly where the error occurs, what the expected behavior should be after the fix, which tests to run and which parts of the codebase to leave untouched. Codex performs better when it knows the boundaries of a task, especially since remote mobile means each feedback loop takes longer than when you're sitting right at your machine. A clean working rhythm might look like this: describe the task in detail whether small or medium-sized, ask Codex to read the relevant files, let it propose a solution, only approve when necessary and wait for the result report. Once you get used to this rhythm, you'll find that idle time outside can handle real work — while keeping the final decision firmly in your hands. Compared to Claude Code Remote and Telegram bot There are many ways to control an AI coding agent from your phone, though the three most common approaches each serve a different need. Criteria ChatGPT app + Codex Claude Code Remote Telegram + Codex Natural conversation ✅ Excellent ✅ Good ❌ Requires exact syntax Granular control Moderate Highest Low Connection stability Stable Stable Frequent drops Mobile UI Well optimized Not fully optimized Uses existing Telegram app Initial setup Easy, scan QR Easy Requires manual bot configuration Computer must stay on ✅ Required ✅ Required ✅ Required Claude Code Remote Control offers the strongest level of control — you get direct terminal output, can intervene mid-task and generally feel much closer to what the agent is doing. That said, the UI on small phone screens isn't fully optimized yet, and some interactions are still difficult to perform without a physical keyboard. Telegram bot has the advantage of not requiring a separate app and is easy to get started with, but the real-world experience has clear limits: it's prone to slowdowns, occasional silent disconnections mid-task, and because it lacks genuine AI context, anything slightly more complex than a simple command quickly falls apart — forcing you to type precise instructions rather than describe what you need naturally. ChatGPT app + Codex sits at the best balance point for most users — smooth enough, smart enough, quick to set up with a QR scan and no new syntax to learn before you can get to work. Connecting ChatGPT app to Codex doesn't turn your phone into a development machine — it turns your phone into a control surface for a development machine that's already ready to work. As long as the host stays on, permissions are configured correctly and the task is scoped tightly enough, this is the most practical way to handle real coding work when you're away from your laptop.

Learning more makes you better, a principle long assumed to apply only to humans, turns out to hold true for Hermes Agent too, an open-source AI agent from Nous Research. Every time you work with it, Hermes Agent doesn't forget, it remembers, understands you more deeply, and gets better with each session, thanks to a memory system that can recall everything about you even after the machine has been off for a week. What Is Hermes Agent? Hermes Agent is an open-source AI agent developed and released under the MIT license by Nous Research, the lab behind the Hermes, Nomos, and Psyche model lines. Unlike Antigravity or Codex, which depend on an IDE environment, or ordinary chatbots that ultimately remain a thin wrapper calling a single API, Hermes Agent is built to run continuously on a user's own infrastructure, from a cheap VPS to a GPU cluster or serverless infrastructure, and it operates in a way fairly similar to OpenClaw. The core difference in Hermes Agent lies in how it manages long-term memory and converts experience into real skills. Instead of merely storing raw information or passively remembering preferences the way AI like Gemini or Claude do, Hermes runs a closed "learning loop," meaning that after every work session, it actively distills the process into new tools it can use the next time. This system is run by a background "Curator" agent that automatically scores, prunes, and merges accumulated knowledge, combined with FTS5 search technology that retrieves old memories roughly 4,500 times faster without spending any tokens. As a result, Hermes doesn't just respond and forget, it genuinely becomes a collaborator that grows more knowledgeable and capable over time. Four Features That Set Hermes Agent Apart Nous Research doesn't call Hermes Agent a chatbot or a copilot, it positions it as an agent with a built-in learning loop. The four feature groups below explain why that label isn't just marketing. Memory That Persists Across Sessions The biggest weakness of most AI today is that memory only stores raw chat text rather than how work actually gets done. Hermes Agent addresses this through three combined mechanisms: Fast retrieval: Uses FTS5 full-text search to pull up old memories roughly 4,500 times faster than conventional search, without spending extra tokens the way Gemini or Cowork do. User understanding: Integrates Honcho's dialectical user-modeling approach, helping the agent understand preferences, habits, and personal context in depth across thousands of sessions. Continuity: The agent picks up work exactly where you left off, even if that was a project from weeks earlier. Self-Generating and Self-Improving Skills This is the feature that makes Hermes Agent behave like a collaborator that accumulates experience, rather than just a tool that answers on request: Learning from real use: After completing complex tasks, Hermes Agent distills the process into new skills and stores them in a library to be reused automatically next time. Open agentskills.io standard: These skills follow an open standard, so they can be packaged, shared, and reused across different AI systems without being rewritten from scratch. The Curator mechanism: A background administrative agent periodically scores, prunes, and merges duplicate skills, which keeps the skill library from bloating and becoming disorganized over time. Present on More Than 23 Messaging Platforms Hermes Agent isn't confined to a computer, it integrates directly into the messaging channels people already use on their phones every day: Multiple channels, one brain: You can command Hermes Agent through Telegram, Discord, Slack, WhatsApp, Signal, email, or SMS. Context retained: Whether you message via Telegram in the morning or switch to Discord at night, the agent keeps a single thread of memory, never fragmented by channel. Multimodal interaction: Supports sending voice messages, images, and video, along with the ability to analyze multimodal content. Flexible Runtime Infrastructure Hermes Agent supports six backend types for executing commands: local machine, Docker, SSH, Daytona, Singularity, and Modal. With Daytona and Modal, the environment can hibernate when idle and cost almost nothing while waiting, waking up only when there's work to process. This is why Nous Research describes Hermes Agent as an always-on agent that doesn't require users to keep a server running 24/7 at high cost year-round. Hermes Agent can be installed with a single curl command, supporting Linux, macOS, and Windows via WSL2, or, as of June 5, 2026 with version v0.16.0 "The Surface Release," through an official Native Desktop app for Windows, macOS, and Linux with a fully polished GUI, making it accessible to everyday users without needing a terminal. Built-In Toolset and Limitations to Know 40-Plus Built-In Tools, From Web Search to Schedule Automation Hermes Agent ships with more than 40 built-in tools, including web search, browser actions, file handling, and Python script execution via RPC to run sub-tasks without consuming the main agent's context window. A natural-language scheduling system lets you set recurring tasks like daily reports or data backups, then leaves the agent to run them without being reminded. For tasks that need full isolation, Hermes Agent also supports sub-agents with their own conversation, terminal, and scripts, allowing multiple jobs to run in parallel without diluting the main memory. Challenges and Security Considerations Despite rapid updates, Hermes Agent still has a few points users should keep in mind before deploying it: Stability of the self-learning mechanism: The ability to self-improve skills boosts success rates, with a Tencent Cloud report recording gains of up to 52% along with token savings of up to 61%. However, since this is a self-evolving mechanism, real-world effectiveness still depends on the underlying model chosen and still requires human oversight rather than full trust. Risk from high-level permissions, with security responsibility falling on the user: Hermes Agent can intervene deeply in a system (excessive agency), so connecting it directly to multiple messaging platforms requires users to manage their own API keys and set up guardrails. Unlike closed AI services, Hermes Agent hands full control over to the user, which means the user also bears greater responsibility for configuring access permissions to avoid information leaks. Why Is Hermes Agent Growing So Fast? Hermes Agent's growth could be attributed to Nous Research's marketing, but in our view it comes down to three main factors. A Frictionless Migration Path From OpenClaw Recognizing OpenClaw's large user base, Nous Research built a migration tool that lets users carry over their persona, API keys, the entire skill set, and memory to Hermes Agent with a single command, without losing old data and, of course, without having to reconfigure anything from scratch. If you're currently using OpenClaw and want to try Hermes Agent without losing your old data, look for the hermes claw migrate migration tool built into Hermes Agent before considering a fresh install. Betting on a Closed Learning Loop Instead of a Feature Race While many other agents compete on the number of tools they offer, Hermes Agent positions itself as a self-evolving entity, one that distills experience into new skills and retains long-term memory to understand users more deeply over time. This approach creates lasting value, and the community has already put it to use for projects such as automating large-scale content production with high consistency across many sessions. A Role as a Training Data Engine Beyond serving as a personal assistant, Hermes Agent also functions as a capable research tool. It can generate thousands of parallel tool-calling trajectories and compress them into training data for other AI models. By turning the agent's real-world experience into training data, Hermes becomes a platform that developers building the next generation of autonomous AI can't easily do without. How Is Hermes Agent Different From an Agent Harness? People new to the space often confuse Hermes Agent with the concept of an agent harness, which is the framework that decides how a model calls tools, handles the reasoning loop, and coordinates execution steps internally. If a harness is the engine and chassis that determine how a car drives, then Hermes Agent is like a car that already has that engine installed, plus seats, a navigation system, and the driver's own trip memory. In other words, a harness is the technical architecture layer underneath, while Hermes Agent is a complete end-user product that already packages memory, a skill system, communication channels, and a choice of runtime infrastructure. A developer can build their own harness to control every small detail, but most users don't need to go that deep, they just need an agent that runs right away and gets smarter through use. For a closer look at this underlying architecture layer, read more at What Is Agent Harness? The Framework That Makes AI Work Efficiently, which explains in detail how this type of framework operates. Is Hermes Agent Worth Trying Right Now? Being fully open source, collecting no user data, and supporting complete self-hosting, Hermes Agent is one of the few agents today that lets users keep full control over their own data while still getting a continuous assistant experience with real memory, not the simulated memory that only exists within a single chat. After v0.16.0, the biggest technical barrier for users unfamiliar with terminals has largely been removed, as the native desktop app for Windows, macOS, and Linux has fully replaced the pure CLI approach used before. What's left to judge about Hermes Agent isn't whether it runs, but what it learns after a few real weeks of use. The fastest way to find out is to install the desktop app or run the CLI on a cheap VPS, connect it to a familiar messaging channel like Telegram, then watch what skills the agent forms on its own from how you use it every day. That's also the groundwork for comparing Hermes Agent with other options on the market, from Agent Harness to OpenClaw and Claude Cowork, in the next part of this series.

Gemini has won big in the most literal sense, right as Messi scored his first hat-trick at the 2026 World Cup, leading Argentina to a crushing 3-0 victory over Algeria and equaling Miroslav Klose's record of 16 World Cup goals. That historic moment became the perfect launchpad for Gemini. Back in March 2026, Google and the Argentine Football Association (AFA) made a bold decision: rather than simply printing a logo on training kits, they signed a deal for the AI to actively support tactical preparation and professional decision-making. That bet has now proven to be the right call. From training kit to the tactical meeting room The agreement between AFA and Google was unveiled at Times Square, New York, a venue deliberately chosen to capture global media attention. The Gemini logo appears across all training apparel for Argentina's men's, women's and youth squads, sitting alongside Adidas and American Express in AFA's top sponsorship tier. But the interesting part isn't the jersey. According to Inside World Football, Argentina's coaching staff will use Gemini for three specific purposes: tactical analysis, injury prevention and decision support. In other words, Gemini now has a seat in meetings that previously belonged only to Scaloni and his assistants. Google has not publicly disclosed which specific Gemini tools have been integrated into AFA's workflow. What is clear is that they are using the World Cup to bring Gemini into the reality of professional football, and the results will be graded in public. What is Gemini actually doing in the dressing room? Argentina arrives at the 2026 World Cup as the reigning champion. Every decision Scaloni makes, from the squad list to the starting eleven, is scrutinized more closely than any other team, and that is precisely why Argentina has become the most ideal testing ground Google has ever had for Gemini in professional football, especially at a major tournament. Tactical analysis Gemini is used to process match data for both Argentina and their opponents, covering movement statistics, attacking patterns and defensive vulnerabilities. Instead of the coaching staff spending hours reviewing footage, AI synthesizes the data and generates tactical diagrams automatically, saving significant preparation time before each match. Injury prevention This is a problem every major team wants to solve, especially when Messi and several key players are at an age that requires careful management of training loads. Gemini analyzes biometric data and injury history to issue early warnings, helping the coaching staff adjust intensity before problems actually occur. That is part of the reason why, immediately after completing his hat-trick, Scaloni chose to substitute Messi off, prioritizing fitness and safety for the matches ahead. AI in injury prevention is nothing new. Premier League clubs have had Microsoft as a partner for similar purposes. What is different this time is that Gemini is integrated directly into the workflow of a national team competing at a major tournament, not just at club level. For fans: create Messi content, follow scores without unlocking your screen Alongside supporting the coaching staff, Gemini has also rolled out a range of features aimed at fans, and this is the side that hundreds of millions of people will actually experience. Gemini lets you create content about players directly Users can generate images, songs and digital content featuring Argentina players like Messi directly inside the Gemini app. The feature is designed to bring the World Cup experience closer to those who cannot attend matches in person. Real-time scores and automated daily briefings On Google Search, live match scores can be pinned to the lock screen and update in real time, with dedicated animations for goals and red cards, all without needing to unlock the phone. For paid Gemini users, the Scheduled Actions feature allows an automated daily football briefing to be set up, covering scores, news and fixtures, delivered at a chosen time without needing to prompt it each day. Match-day infrastructure Google has updated Street View at all 16 host stadiums and optimized routing on Waze for match days. Waze also surfaces live scores when the car is stopped at red lights, so drivers do not need to pick up their phones while on the move. The 2026 World Cup is the real test for AI in sport Google is not sponsoring Argentina alone. Gemini also appears on the kits of France, Morocco, Iraq, Turkey and the United States, while Pixel is the official phone of the French squad, which is also using Gemini for internal communications. This is clearly a comprehensive strategy from Google, not a one-off deal. What makes the 2026 World Cup particularly significant is that it will answer a question no lab environment can: what do users actually do with AI when a World Cup runs for six weeks across 104 matches? Features that run on initial novelty will fade after the group stage. Whatever users keep coming back to all the way through the final is the honest answer to where AI actually fits in everyday life, and Google knows it. Google's communications director for Latin America, Flor Sabatini, stated that the 2026 World Cup will mark a before and after in the history of football because of AI. It sounds like marketing, but the reality is that this is the first time a major AI model has been integrated into the preparation of the reigning world champions, right in the middle of the most-watched sporting event on the planet. The 2026 World Cup is Gemini's real test The most significant part of this entire story is not the Gemini logo on Messi's jersey. It is the fact that Argentina, still the most expected to win and the most scrutinized team, carrying the pressure of defending the title, has committed part of its preparation process to AI. If Argentina succeeds, Gemini will have a case study that no advertising budget can buy. If Argentina falls short and the coaching staff attributes any part of it to AI, the narrative will flip entirely. Either way, this is the first time AI has been held accountable on a stage that genuinely matters, not a benchmark, not a demo, but the World Cup. For AI users, what is worth watching is not just whether Argentina wins, but whether Gemini actually changes how a football team operates, or whether it turns out to be nothing more than a logo on a training kit that looks better than previous years.

The Adidas Trionda match ball, three dimensional player models accurate to the millimeter, robot dogs patrolling stadiums, and Google Gemini sitting on the touchline with the Argentina national team. World Cup 2026 is not only the largest tournament in history with 104 matches across 16 cities in the United States, Canada, and Mexico, but also the most extensive deployment of AI ever seen in sports. How the Adidas Trionda smart ball works The official match ball named Adidas Trionda is equipped with an Inertial Measurement Unit IMU sensor operating at 500Hz, which means it collects 500 data points every second on movement, spin, and the exact moment the ball makes contact with a player foot. This is particularly important for offside situations, as the sensor will determine the precise moment the ball leaves the passer foot down to the millisecond. The timestamp from the sensor is synchronized immediately with the player tracking system, helping to lock the position of every player on the pitch at that exact moment instead of relying on the naked eye which can be off by up to half a second. As a result, offside decisions are made faster and more accurately than ever before. This advanced technology immediately rescued the Swedish team by identifying the precise moment of contact from striker Alexander Isak. Before that, the joy of scorer Svanberg was temporarily dampened when the VAR team stepped in to review. In a play that occurred at a breakneck speed, he appeared to be standing behind the Tunisian defense when the ball was delivered into the penalty area, leading many to believe the goal would be disallowed. However, the data from the motion sensor mounted inside the Adidas Trionda ball proved that Svanberg moved back to a valid position in time, bringing a legitimate goal for Sweden to the delight of the fans. Semi automated offside technology with 3D player avatars Semi automated offside technology SAOT has been upgraded significantly for World Cup 2026, highlighted by the 3D avatar of each player. Every player participating in the tournament is digitally scanned across the entire body in about one second, creating a 3D model with detailed body dimensions for every part. When a situation requires VAR review, the system overlays these 3D models onto real time tracking data from more than 12 specialized cameras at each stadium. This approach completely resolves the long standing issue of two dimensional offside lines, where a player arm, shoulder, or foot might be obscured from a certain camera angle. The 3D model fills those gaps using realistic anatomical data, and the result is displayed as a complete 3D animation on the pitch and on television, entirely replacing the flat red and green lines that once confused spectators. Football AI Pro: analytics platform for all 48 teams FIFA collaborated with Lenovo to build Football AI Pro, an analytics platform developed on the FIFA Football Language foundation model, which has been trained on hundreds of millions of football data points over decades of competition. This is the first time in World Cup history that all 48 participating teams have access to the same analytics platform, rather than wealthier federations holding an advantage due to better data tools. This platform outputs results in multiple formats, including text summaries, video clips, interactive charts, and 3D tactical visualizations. Teams can use it before and after matches to analyze opponent tactics, detect set piece patterns, track player workload intensity, and analyze head to head history. However, FIFA bans its use during match time, and coaching staff can only access it during halftime and after the match. Referee chest cameras with AI image stabilization For the first time in history, referees in all 104 World Cup matches wear chest cameras. The raw images from the camera when the referee runs at high speeds are shaky and cannot be used for broadcasting, but FIFA runs an AI image stabilization model in real time on every frame, creating broadcast quality video. The result is the Referee View perspective that offers a subjective experience from the pitch, quickly becoming one of the most popular broadcasting innovations. This viewpoint not only serves entertainment but also provides analysts with a new data source, which is the exact vision that the referee had when making decisions. Google Gemini on the touchline and fan experience In March 2026, the Argentine Football Association announced Google as an official global sponsor, with the Gemini logo appearing on training jerseys for the men, women, and youth teams. However, this partnership goes far beyond brand advertising, because the Argentina technical staff uses Gemini directly for tactical analysis from match videos, tracking player workload and injury recovery, querying historical data on specific matchup scenarios, and creating individual opponent briefings for each player. Notably, Argentina players and coaches use Gemini through the standard application rather than any customized interface, reflecting the maturity of general purpose AI tools in professional sports applications. Additionally, Google also deployed a series of features for fans, including live scores pinned to the Android lock screen, AI match summaries on the Gemini app, on demand tactical diagrams, jersey templates on Google Photos, stadium navigation via Google Maps, and match statistics on Google Search. Robot dogs, facial recognition, and AI security At the host venues, FIFA deployed Boston Dynamics Spot robot dogs for outer perimeter security patrols and facility inspections. These robots perform automated patrols in restricted areas, with onboard cameras connected to the stadium security AI system, which is particularly effective in spaces that are difficult to monitor continuously, such as tunnels, underground technical corridors, and stadium perimeters at night. The biometric layer is equally notable, as some stadiums use facial recognition for entry, where your face is your ticket, processed against the database in less than one second. However, the widespread presence of AI surveillance also raises questions about privacy in large scale sporting events. AI predictions for the champion: every model has a different answer Before the tournament kicked off, many AI systems simulated all 104 matches to predict the champion, and the results were completely inconsistent. ChatGPT predicted Spain, the FanDuel research model chose France to defeat Argentina 3 to 2 in the final, while Yahoo Sports and DataCamp both bet on Brazil. This disagreement is worth reflecting on, as every model was provided with the same public data sources including FIFA rankings, ELO scores, qualifying form, and injury reports, but different weighting methods created entirely different results. And of course, no model can calculate Messi left foot shot in the 89th minute of a knockout match. That is still football. AI is no longer an experiment but infrastructure What makes World Cup 2026 different from previous tournaments does not lie in any single technology, but in the fact that AI has transitioned from the experimental phase to operational infrastructure. The smart ball, the 3D offside system, the referee cameras, and the analytics platform are not pilot projects. They are the basic operational foundation for every match. The 500Hz sensor inside the ball does not understand football, as it only measures spin. However, the decision it enables, accurate to the millimeter, displayed in 3D, and returning results in seconds, with the Swedish team situation being a prime example, will change how football is operated. That is the true shape of AI when running at a large scale.

Anthropic just dropped what may be its biggest release yet with Claude Fable 5, and it has quickly become the most talked-about model this week. Not just because of its raw power, but because of how Anthropic brought it to the world: this is the first time a Mythos-class model has been made available to general users, after two months under lock and key for safety reasons. What is Fable 5 and why is it different from previous models? At its core, Fable 5 is not a model built from scratch. It is a "safety-hardened" version of Mythos 5, the most powerful model Anthropic has ever built. Back in April 2026, Mythos Preview was only accessible to a very small group of organizations including AWS, Apple, Google, Cisco, and JPMorgan Chase through Project Glasswing, because its ability to detect and exploit software vulnerabilities was simply too powerful to release broadly. Anthropic had also launched Claude Opus 4.8 beforehand as a stepping stone in the development roadmap toward this new model generation. To get Mythos out the door, Anthropic spent two more months building classifiers running in parallel. These are specialized AI systems that analyze requests before the main model processes them, and when a sensitive topic is detected, the system automatically routes to Claude Opus 4.8 at no additional charge. Anthropic says this mechanism only activates in fewer than 5% of sessions, meaning most general users will notice no difference compared to raw Mythos 5. Fable 5 and Mythos 5 share the same pricing: $10 per million input tokens and $50 per million output tokens, which is less than half the cost of Mythos Preview. Users on Pro, Max, Team, and Enterprise plans can use Fable 5 for free through June 22, 2026. Starting June 23, Anthropic will shift to consumption-based billing until infrastructure capacity allows the model to return to fixed subscription plans. How does Fable 5 differ from Mythos 5 on safety? Despite sharing the same underlying model, Fable 5 and Mythos 5 are two distinct products by design. The difference lies entirely in the safety classifiers layered on top of the base model. Three classifiers Fable 5 has that Mythos 5 does not Fable 5 is equipped with three safety classification layers running alongside the main model, covering: Cybersecurity, Biology and Chemistry, and Distillation. When a user submits a request in any of these areas, Fable 5 automatically falls back to Claude Opus 4.8 instead of the main model, and notifies the user accordingly. Mythos 5 has none of these filters. It retains the full software exploitation and biological research capabilities that Anthropic considers too dangerous for wide distribution, which is why Mythos 5 remains restricted to a limited group within Project Glasswing, including vetted cybersecurity professionals, critical infrastructure organizations, and approved biology researchers. How does this affect real-world performance? The classifier difference leads to meaningfully different benchmark results in specialized tasks. On ExploitBench, a benchmark focused on cybersecurity, Mythos 5 scores 78% while Fable 5 lands near the 40% range of Opus 4.8, because the fallback mechanism triggers as soon as it detects attack-related requests. For scientific research, Mythos 5 can design proteins and generate novel hypotheses at roughly 10 times the speed of previous methods, while those same capabilities are restricted in Fable 5 for safety reasons. If you are a researcher or work in legitimate cybersecurity, be aware that Fable 5 may automatically redirect some of your requests to Opus 4.8, even when the context is entirely valid. Anthropic acknowledges this and is actively working to improve classifier accuracy. Real-world performance: what do the numbers say? On SWE-Bench Pro for coding tasks, Fable 5 scores 80.3%, compared to 69.2% for Opus 4.8 and 58.6% for GPT-5.5. But perhaps the more striking number comes from a real deployment: Stripe used Fable 5 to migrate an entire 50-million-line Ruby codebase in a single day, a task that would have taken a full engineering team more than two months to complete manually. On business analytics, Fable 5 is the first model to cross the 90% threshold on Hex's complex analytics benchmark, outperforming Opus 4.8 by 10 percentage points. IMC, a quantitative trading firm, reported that the model scored near-perfect on their internal evaluation covering fact lookup, causal reasoning, and expected value calculations. The biggest shift from previous models is the ability to sustain focus across multi-day tasks without needing human oversight at every step. Rather than executing commands one at a time, Fable 5 can take on a large project, self-plan, run tests, and handle errors in a loop, behaving far more like an engineer than a question-answering tool. Fable 5 is now available on the Claude API under the model ID claude-fable-5, with support on Amazon Bedrock and Google Vertex AI for enterprise consumption-based plans. Notion integrates Fable 5: from scattered notes to a complete action plan Notion is one of the first applications to integrate Fable 5, and the reason is straightforward. The tasks Fable 5 handles best, specifically reading multiple fragmented data sources, synthesizing them, and producing a logical structure, are exactly what Notion users need most in their daily work. Simon Last, co-founder of Notion, described the primary use case as turning messy meeting notes into a task board with assignments and priorities. Instead of users having to re-read entire transcripts, summarize, and manually create tasks, Fable 5 handles the entire chain without needing to be prompted at each step. There has been no official announcement from Notion about Fable 5 pricing after June 22. It remains to be seen whether Notion AI will pass the consumption cost directly to users or absorb it into existing subscription tiers. If the rate ends up lower than going directly through Anthropic, that would be a meaningful advantage for Notion subscribers. A few things to keep in mind before diving in Fable 5 is powerful, but there are two things worth considering before building it into your workflow. First, the $50 per million output tokens price point is high relative to the current market, making it well-suited for complex engineering or analytical tasks but not necessarily for simpler jobs that Sonnet or Haiku can handle at a fraction of the cost. Second, the safety classifiers work well in the vast majority of cases but can trigger incorrectly in some legitimate research contexts, something Anthropic openly acknowledges and is continuing to refine. For individual users on Pro or Max plans, the remaining days before June 22 are a reasonable window to evaluate whether Fable 5 actually generates enough value at that price point before committing to pay-per-use billing.

Thariq Shihipar's post from the Claude Code team at Anthropic has drawn significant attention in the AI user community. He revealed Dynamic Workflows, a feature that allows Claude to design its own workflows instead of just waiting for commands, and this is considered the most important upgrade since Claude Code gained skills and subagents. This feature uses the harness concept as its foundation to handle technical requirements. Three fatal errors that cause AI agents to fail at complex tasks Before discussing the solution, Thariq points out an uncomfortable reality: most AI agents today face serious problems when handling complex, multi-step tasks within a single context window. He categorizes them into three core failure modes that nearly every agent system encounters. Agentic laziness: when AI declares done after finishing only half the work This is the phenomenon of Agentic Laziness, where an agent completes part of the work and then self-reports as finished. A specific example: you ask an agent to review 50 code files, but it only looks through 20 files and concludes that everything is fine. The cause lies in context window limitations, and when the amount of information is too large, the agent tends to take shortcuts to finish faster. Will an agent be biased toward itself? An agent being biased toward itself is called Self-Preferential Bias, and this occurs when you ask an agent to review its own results. Like asking a student to grade their own exam, the agent tends to favor the results it already produced, leading to uncritical validation and overlooking potential errors. This is particularly dangerous in tasks requiring high accuracy. How to prevent an agent from losing its original intent step by step Goal Drift is the phenomenon where an agent gradually forgets its original goal after many processing steps or after context compaction. Specific constraints like "don't do X" or important edge cases can be dropped when memory is summarized, so the final result deviates from the original requirement without the agent ever realizing it. Dynamic Workflows helps Claude write its own work orchestration framework Anthropic's solution is not to make the model smarter, but to change how Claude organizes work. Dynamic Workflows transforms Claude from a code-writing agent into an agent that designs operational workflows for complex tasks. The core concept here is self-organization: Claude can analyze goals on its own, choose the appropriate working mode, and create an internal workflow before starting execution. Custom harness instead of a fixed workflow Instead of operating within a fixed environment, Claude writes a harness framework in JavaScript designed specifically for each task. This harness acts like a project manager: it breaks down the work, initializes specialized sub-agents for each part, assigns appropriate tools, routes work to different models, and performs adversarial verification to ensure quality. How does a harness work? To understand more clearly, imagine the harness as a theatrical script that Claude writes for itself before performing. When given a complex task, Claude does not dive straight in but pauses to write a JavaScript snippet describing the entire workflow: how many sub-agents are needed, what each agent does, what order things happen in, and how results from one agent are passed to the next. A concrete example: if you ask Claude to audit 1,000 Slack messages to find recurring incidents, the harness might look like this logically: Agent 1 (classification): reads all messages and assigns labels by topic Agent 2, 3, 4 (parallel processing): each agent deeply analyzes one topic group Agent 5 (synthesis): collects results from the three agents above and removes duplicates Agent 6 (cross-check): re-reads the synthesized results and provides independent critique The important point is that Claude writes this harness based on the specific characteristics of each task, not according to a rigid template. Different tasks produce different harnesses, and that is exactly why this feature is called "dynamic." The harness is written in JavaScript and runs within the Claude Code environment. You can activate Dynamic Workflows by saying "use a workflow," however this phrase is easily confused with regular workflows, so it is recommended to use the keyword "ultracode" in your prompt to clearly distinguish between a regular workflow and a Dynamic Workflow and save more tokens. Context isolation to prevent context degradation One of the smartest design choices in Dynamic Workflows is the Isolation feature. Each sub-agent is given its own separate context window, completely independent from other agents. This prevents the phenomenon of context rot, meaning the quality degradation that occurs when a context window becomes overloaded, while also eliminating both Agentic Laziness and Goal Drift since each agent focuses only on its assigned piece of work. Six reusable orchestration patterns Claude can combine six available orchestration patterns to handle a wide variety of situations: Classify and act: classifies input then selects the appropriate action Fan out and synthesize: splits work into multiple parallel branches then synthesizes the results Cross-check verification: uses a separate agent to cross-check results Generate and filter: generates multiple options then filters for the best one Tournament: puts options into direct head-to-head elimination rounds Loop until done: repeats until a quality threshold is reached Can you optimize costs when using Dynamic Workflows? Running multiple sub-agents in parallel might sound expensive, but Dynamic Workflows is actually designed to optimize costs in several specific ways. Smart routing to the right model Not every step in a workflow needs the most powerful model. The harness allows Claude to route each task to a model that matches its complexity: simple classification steps can run on smaller, cheaper models, while only steps requiring deep reasoning need a large model. The result is that total costs are often lower than running the entire workflow on a single model. Context isolation helps reduce token consumption Because each sub-agent only receives the portion of context it actually needs for its work, total token consumption across the entire workflow is often significantly lower compared to the traditional approach, where the full conversation history gets stuffed into a single context window that keeps growing larger. Avoiding rework through early checkpoints The harness can install quality checkpoints between steps. If a step produces a result that does not meet requirements, the system stops and reprocesses just that step rather than running the entire workflow to completion before discovering an error at the end. This approach saves significant costs for long multi-step tasks. If you are concerned about costs, start with moderate-volume tasks to observe actual token consumption before scaling up. What are the real-world applications of Dynamic Workflows? What excites Thariq most is not the coding capability, but the way Dynamic Workflows extends Claude Code into non-technical tasks. The feature can be activated with natural language (for example: "use a workflow") or the keyword "ultracode." Real-world applications include: Auditing thousands of Slack messages to find recurring incidents Systematically ranking and screening large candidate pools Running automated live elimination tournaments to choose the best name for a CLI tool Handling high-precision operational tasks that previously only humans could perform The design philosophy is architectural constraints rather than raw intelligence The most notable aspect of Anthropic's approach is the design philosophy: rather than trying to increase the raw intelligence of the model, they build architectural constraints into the workflow. In other words, instead of hoping the model will naturally know how to avoid mistakes, they design the system so that errors are hard to occur in the first place, and the harness is the tool that enforces that philosophy. Dynamic Workflows shows that the next step forward for AI agents does not lie in smarter models but in the ability to design workflows on their own. Just as a good manager divides work among a team rather than doing everything alone, Claude can now organize its own team of sub-agents, and this is a clear signal that the future of AI coding is no longer just about writing code faster but about organizing work better.

Microsoft just dropped seven new AI models at Build 2026, with MAI-Thinking-1 boasting 35 billion active parameters and trained entirely on clean data. For the first time, the software giant is openly challenging the position of its own strategic partner, OpenAI, on the AI model battlefield. MAI-Thinking-1 and Microsoft's reasoning ambitions The centerpiece of Build 2026 was MAI-Thinking-1, Microsoft's first reasoning AI model developed entirely in-house. With approximately 35 billion active parameters, the model is designed to handle multi-step reasoning tasks, work with long contexts, and support complex coding, all at a lower cost than many large-scale AI models currently available. The most notable claim is that Microsoft trained MAI-Thinking-1 on clean data without using distillation from third-party AI models. In other words, this is a clear statement that Microsoft has the independent AI research capability to build competitive models without "borrowing" knowledge from GPT or any other model. According to Microsoft's published evaluations, MAI-Thinking-1 achieves competitive performance on coding benchmarks and is rated on par with many leading AI models in blind evaluation tests. The 35-billion parameter count also signals that Microsoft is prioritizing efficiency over raw scale, as many competitor models have significantly more parameters but may not necessarily deliver better output quality. From coding to voice: a complete AI ecosystem Beyond reasoning, Microsoft introduced six additional AI models to build a complete AI ecosystem serving both individual users and enterprises. From coding and image generation to voice synthesis, every piece of the puzzle now has a dedicated model. Smarter coding with MAI-Code-1-Flash For developers, MAI-Code-1-Flash is significant news. This model specializes in code generation and software development support, optimized for real-world programming tasks. More importantly, it will be integrated directly into GitHub Copilot and Visual Studio Code, two tools used daily by millions of developers. This means code suggestions and automated coding experiences will be significantly upgraded within familiar development environments. Images and voice: the missing pieces In the creative content space, Microsoft announced MAI-Image-2.5 alongside MAI-Image-2.5-Flash. These are next-generation image creation and editing models, with the Flash version optimized for fast response times, making it suitable for real-time applications like live photo editing or on-demand illustration generation. In the audio domain, Microsoft introduced two important models: MAI-Voice-2 with more natural voice synthesis capabilities and support for additional languages MAI-Transcribe-1.5 for speech-to-text conversion with significantly faster processing speeds than the previous generation Additionally, Microsoft has developed optimized variants specifically for the Microsoft Foundry platform, helping enterprises easily build and deploy their own AI applications. The strategy to reduce OpenAI dependence Where Microsoft was previously seen mainly as an infrastructure partner and deployment platform for OpenAI, Build 2026 shows the company is steadily acquiring all the essential components of a full AI ecosystem. Microsoft now has its own reasoning model, coding model, image generation model, voice synthesis model, and speech recognition model, all connected directly to the Azure, Copilot, and Microsoft Foundry ecosystem. This strategy gives Microsoft greater autonomy in developing core technology while reducing risk from dependence on external partners. More specifically, owning proprietary AI models allows Microsoft to control its product roadmap, optimize operational costs, and customize models for specific service needs without waiting for or negotiating with third parties. Where does the AI model race go from here? The simultaneous launch of seven new AI models shows Microsoft is investing heavily in foundational technologies to compete directly with major players like OpenAI, Google, and Anthropic. When OpenAI's largest partner decides to build its own AI models, that is the clearest signal that the AI race has entered a new phase where no one wants to place the future of their technology in someone else's hands. For developers and enterprises, now is the time to closely watch Microsoft Foundry and the Azure AI ecosystem, as tools that were previously only available through OpenAI will soon appear within Microsoft's familiar ecosystem. Build 2026 may well be remembered as the moment Microsoft officially declared its vision for an independent, comprehensive AI ecosystem with its own distinctive identity.

Many people still do research manually: opening a dozen tabs, watching videos, reading articles, taking notes in scattered places, and then spending even more time trying to synthesize the result. A long-form post by monokern on X suggests a different pattern: use Claude Code to orchestrate the workflow, NotebookLM to analyze sources, and Obsidian to store long-term memory. Done correctly, this is not just a search session. It becomes an AI workflow that compounds over time. The core idea is practical: Claude Code does not need to do everything inside an expensive context window. It can call tools, run skills, create files, and offload heavy source processing to NotebookLM. The output is then saved back into Obsidian as markdown, giving the next research session better context. According to the original post, the initial setup can be completed in under 30 minutes if the required tools are already available. Why does this stack work? The strength of the workflow is that each tool owns a clear layer. Claude Code acts as the execution engine: it receives plain-language instructions, calls skills, runs commands, manages files, and coordinates the pipeline. Instead of forcing the user to operate each step manually, Claude Code becomes the system operator. NotebookLM is the analysis layer. Google's research tool can read sources, summarize them, generate analysis, flashcards, mindmaps, infographics, or audio overviews. When Claude Code sends source processing to NotebookLM, the user benefits from Google's processing layer rather than spending Claude tokens on every piece of long-form digestion. Obsidian is the memory layer. Every analysis result is saved as markdown in a personal vault. Over time, that vault becomes a structured knowledge base of topics, sources, observations, patterns, and conclusions. Claude Code can read those files later to understand what the user cares about, what formats they prefer, and how they tend to evaluate a topic. Skill Creator turns the workflow into a reusable tool The first major step in the guide is installing Skill Creator inside Claude Code. This layer lets users describe a new capability in natural language, after which Claude Code creates the skill structure, installs it, and makes it available as a reusable command. In other words, instead of rebuilding the research prompt every time, the user packages the workflow as a dedicated skill. The first example is a YouTube search skill. It uses yt-dlp to search videos by query and return metadata such as title, channel, views, duration, upload date, URL, and a views-to-subscribers ratio. For content or market research, this is more useful than a plain list of links because it shows which sources are actually attracting attention. NotebookLM handles the heavy analysis The post proposes connecting Claude Code to NotebookLM through notebooklm-py because NotebookLM does not currently provide an official public API. After installation and Google account authentication, Claude Code can use a custom skill to create a new notebook, add sources such as YouTube URLs, text, or files, and then ask NotebookLM to generate analysis or deliverables. The key point is that NotebookLM is not only a summarizer. In a real research pipeline, it can receive 10 videos on a topic, analyze which frameworks are gaining traction, which ones are overhyped, where the community disagrees, and what content gaps remain uncovered. That processing takes time, but most of the work happens on the NotebookLM side. The full pipeline: one command for a complete research task Once the YouTube search skill and NotebookLM skill exist, the next step is to create a pipeline skill that combines both. The user gives a topic, such as researching AI agent frameworks in 2026, and the pipeline searches for relevant sources, creates a notebook, adds those sources, runs the analysis, and returns the result as markdown. In monokern's example, the pipeline finds 10 video sources, sends them into NotebookLM, generates analysis, creates an infographic, and saves the result into Obsidian. The total processing time is described as around 6 minutes, most of which is NotebookLM processing. The practical value is that the user does not need to open every tab, copy every link, or manually combine the metadata. The final output is more than a chat answer. It includes full analysis, source lists, engagement metrics, trend observations, a visual deliverable, and a markdown file saved into the vault. That is what separates this workflow from a normal chatbot interaction. Obsidian makes the system smarter over time Obsidian is the most interesting part. If the workflow runs only once, it already saves time. But if it runs regularly, every new markdown file makes the personal knowledge base richer. After a month, Claude Code can see recurring topics, the types of insights the user values, and the preferred format for results. The post also highlights the role of the claude.md file inside the vault. This can become a configuration file describing working conventions, analysis style, and output preferences. After several research sessions, the user can ask Claude Code to read recent work and update that file so it better reflects the user's current process. The real value is the structure, not YouTube YouTube is only the data source in the example. The pipeline structure is the valuable part. Users can replace YouTube with academic PDFs, industry reports, public documentation, web pages, local files, transcripts, or Google Drive documents. As long as Claude Code can access the source and pass it into the analysis layer, the operational template stays the same. This opens many practical uses: researching a crypto ecosystem through whitepapers and public documentation, analyzing an emerging technology through conference talks, mapping content gaps in a niche, or tracking market dynamics from public reports. In every case, the same three layers remain: collect sources, analyze them, and store knowledge. What should you watch out for? This workflow is powerful, but it is not for everyone. It assumes the user is comfortable with Claude Code, has an Obsidian vault, can install CLI tools such as yt-dlp, and is willing to use an unofficial library to connect to NotebookLM. Also, because NotebookLM and YouTube can change access patterns, these skills should be treated as maintained tools rather than install-and-forget automation. Still, the underlying idea is important: instead of using AI as a disconnected chat box, turn it into a research system with memory, a pipeline, and the ability to learn from your own work history. For people who regularly analyze markets, technology, or content, this is far more practical than opening 10 tabs and manually stitching everything together.

Imagine having an AI assistant that is incredibly smart but forgets everything between sessions and cannot check the quality of its own work. To solve this problem, developers created a protective management layer around AI models called an agent harness. This is what enables AI agents to complete complex, multi-step tasks autonomously without requiring constant human intervention. What is an agent harness? Think of an AI model as a brilliant new employee with no long-term memory and zero familiarity with the workplace. They can solve complex problems in seconds but will just as easily forget what they were working on, or accidentally send a confidential document to the wrong client. In that scenario, an agent harness acts as the experienced manager sitting right beside them, keeping things on track. Put simply, an agent harness is the software layer wrapping around an AI model that handles all administrative and logistical work so the model itself can focus entirely on reasoning and problem-solving. It connects the AI to external tools, maintains a complete record of work across sessions, and verifies results before considering a task done. In practice, an agent harness handles the following: Connecting the AI model to external tools such as web search, email, and calendars Persisting progress across sessions so the AI never has to start from scratch Filtering out irrelevant information and supplying only the data the AI actually needs at each step Monitoring AI actions to prevent dangerous mistakes Logging activity in detail so humans can audit what happened when needed Origin of the term: The concept of "agent harness" was formally named by technology engineer Mitchell Hashimoto in early 2026. Before that, many development teams had built similar systems but had no shared term for this layer of infrastructure. Why do AI agents fail at long-running tasks? The biggest weakness in today's AI models is the complete absence of long-term memory. Every new conversation starts from zero with no recollection of anything that happened before. Imagine hiring an employee who wakes up every morning having forgotten every agreement, every deadline, and every piece of progress from the day before. When Anthropic tested Claude building a complex web application without harness support, the results were consistently disappointing. Two failure modes kept appearing: The AI tried to do everything at once, ran out of working memory midway through, and left the project unfinished. The next session wasted time trying to figure out what had already been done. The AI declared the task complete without actually running the result to verify it worked. Beyond those two core failures, long-horizon tasks expose three additional problems: Context clog: Accumulated conversation history and tool outputs crowd out the original instructions, causing the AI to gradually lose focus on the actual goal Tool misuse: The AI sometimes searches for information that does not exist or submits incorrect inputs to forms, and without anything to stop it, repeats the same error in a loop Total progress loss on failure: Any network error or system crash wipes out whatever was stored in temporary memory, forcing a full restart Stanford research (2023): AI models tend to overlook information buried in the middle of long text, even when that text is not particularly long. This is why feeding too much data to an AI all at once often backfires without a filtering layer in place. How does an agent harness work in practice? An agent harness operates in two distinct phases to keep work flowing continuously without interruption. Setup phase (runs once) The harness prepares the full working environment before the AI begins: building a structured task list, initializing storage, and recording the starting point. Think of it as the manager drawing up a detailed project plan before handing anything off. This phase only needs to happen once. Execution phase (repeats) Each time the AI begins a new session, the harness automatically reloads all saved progress and assigns only the next relevant task. When the AI wants to take an action such as searching for information or sending a notification, the harness checks whether that request is valid, executes it safely, cleans the returned result, and passes it back to the AI. The model never touches external systems directly without going through this control layer first. The four core components of an agent harness For an AI to operate reliably over extended periods, a standard agent harness needs four essential components: External tool gateway: Allows the AI to interact with the real world by reading documents, searching the web, or sending messages. The harness acts as an intermediary, validating every request before execution and ensuring returned results are clean and usable. Layered memory management: Maintains three types of memory serving different needs: short-term working memory for the current session, a task log recording what has been completed and what remains, and a long-term knowledge store that accumulates across multiple projects over time. Intelligent context filter: Summarizes long conversation histories down to key points and supplies only the data relevant to the current step rather than loading everything at once, keeping the AI focused on the right task at the right moment. Safety checker and human approval gate: Automatically verifies results before marking a task as complete. For sensitive actions such as deleting important data or sending bulk emails, the harness pauses and waits for human confirmation before proceeding. Note on accumulated knowledge: If an AI agent's memory is stored entirely within a closed third-party platform, all the knowledge it builds up over time belongs to that platform. Switching to a different system means starting from zero. This is worth thinking through carefully when choosing a long-term AI agent solution. Harness engineering and the secret behind millions of lines of code Harness engineering is the practice of treating every AI failure as a system problem to fix permanently rather than something to retry or ignore. As Mitchell Hashimoto put it: if the agent makes a mistake, redesign the environment so that mistake becomes physically impossible to repeat. In practice, when OpenAI built large software projects with three engineers producing 3.5 pull requests each per day without typing a single line of code, they had set up automatic verification checks after every AI action. When the AI produced something incorrect, the system returned error messages written in a specific structure so the AI immediately understood what needed to change on the next attempt. Every error message became a learning signal, not just a warning. A study presented at ICML 2025 further confirmed that the same AI model equipped with a harness consistently outperformed itself running without one, even with identical training weights and identical prompts. The environment surrounding the AI matters just as much as the model itself. A telling data point: Anthropic's Claude Code has grown past 512,000 lines of code and continues to expand. More capable models do not make the harness simpler. They make it larger, because there is more capability to orchestrate and more failure modes to guard against. When do you actually need an agent harness? For simple one-off tasks like summarizing a document or answering a specific question, calling an AI directly is perfectly fine. But the moment work extends beyond a single conversation, requires memory from a previous session, or involves multiple steps that need to happen in a specific order, a harness becomes necessary. One thing worth reflecting on: the built-in web search in ChatGPT and Gemini is itself a form of harness. When AI automatically looks something up, there is infrastructure behind the scenes making the tool call, processing the result, and feeding clean information back into context. The harness is invisible to the user but indispensable to the system. Agent harness is not a short-term technical trend. It is the answer to fundamental limitations that AI cannot resolve on its own: no long-term memory, finite working context, and a tendency to misuse external tools without guardrails. 4AIVN has also started applying harness to our own workflows — and what we have found is that it does not just help AI finish tasks. It turns AI into a system that learns from failure and gets more reliable over time.

Anthropic has introduced Claude Opus 4.8, a release the company describes as its strongest generally available model. The update is not only about stronger reasoning for complex work; it also adds practical changes for developers building AI agent, coding assistants, and long-running automation workflows. The important point is that Claude Opus 4.8 is not just a renamed Opus 4.7. Anthropic is focusing on three practical areas: more stable long-context handling, more reliable tool use, and better cost control in agent loops. With the model ID claude-opus-4-8, it is already available for Claude API and supported cloud platforms. What is Claude Opus 4.8? Claude Opus 4.8 is targets multi-step reasoning, long-running agentic coding, and work that requires a higher level of autonomy. According to Anthropic's documentation, the model supports a default 1 million token context window on Claude API, Amazon Bedrock, and Google Vertex AI, while Microsoft Foundry supports 200,000 tokens. The model also supports up to 128,000 output tokens, adaptive thinking, and the same core tool capabilities as Claude Opus 4.7. This means teams already using Opus 4.7 can likely test the upgrade with limited changes, but they should still review behavior shifts and API constraints before moving production traffic. Key new features Claude Opus 4.8 introduces several updates that directly affect prompt design, long conversation management, and API cost optimization. These are especially relevant if you run deep chatbots, coding assistants, or multi-step agents. System messages during a conversation One major change is support for adding a message with role: "system" after a user turn in the messages array, as long as Anthropic's placement rules are followed. This lets developers update instructions during a long conversation without resending the entire original system prompt. In practice, this is useful for agents that run through many steps. Instead of breaking prompt cache efficiency by repeating a large instruction block, an application can add new instructions at the right moment, preserve cache for prior conversation context, and reduce input cost across long workflows. Fast mode for Claude API Anthropic is also bringing fast mode to Claude Opus 4.8 as a research preview on Claude API. By setting speed: "fast", users can receive higher output token throughput, with Anthropic describing speedups of up to 2.5 times under supported conditions. Fast mode is especially useful for products that need lower latency while staying on the same powerful Opus model. However, the documentation also notes that this mode carries premium pricing, so engineering teams should reserve it for high-value paths or workflows where response speed clearly matters. Prompt caching becomes easier to use With Claude Opus 4.8, the minimum prompt size for caching drops to 1,024 tokens. This small change has a practical impact: many prompts that were previously too short to create a cache entry on Opus 4.7 can now be cached without code changes. For products with stable system prompts, long internal documentation, or repeated API calls, prompt caching can reduce cost significantly. Combined with mid-conversation system messages, Claude Opus 4.8 is better suited for agents that need to preserve state across many steps. Documented refusal stop details Anthropic has also documented the stop_details object for refusal responses. When the model cannot complete a request, the application can receive not only a refusal stop reason but also more structured information about why the refusal happened. This helps products handle the user experience more gracefully. Instead of showing a generic error, an application can distinguish different refusal categories and guide users toward a more appropriate next step. API constraints to watch Although Anthropic says these constraints carry over from Claude Opus 4.7 and are not breaking changes for code that already works with the previous model, developers should still check them carefully. On the Messages API, Claude Opus 4.8 does not support non-default values for temperature, top_p, or top_k. Passing these sampling parameters will return a 400 error. Another point is that adaptive thinking is the only supported thinking mode. Older configuration patterns that set a fixed thinking token budget are no longer the right approach for Opus 4.8. Anthropic recommends using thinking: {"type": "adaptive"} and controlling reasoning depth through the effort parameter. On Claude Opus 4.8, the default effort is high across all surfaces, including Claude API and Claude Code. If an application already sets effort explicitly, the current configuration remains in place; if not, the default behavior may differ from prior expectations and should be tested. Why it matters for coding agents and long workflows Anthropic says Claude Opus 4.8 targets improvements in long-running coding agents, including better long-context handling, less frequent compaction, and stronger recovery after compaction. These are hard problems for large models: after many rounds of reading files, editing code, running tests, and summarizing state, agents can lose focus or miss important details. The new model is also optimized to trigger tools at the right time more reliably. For systems that need to call search, databases, terminals, browsers, or internal APIs, fewer missed tool calls can make a large difference in reliability. This matters more than a single benchmark score because real agent quality depends heavily on knowing when to use the right tool. Should you upgrade to Claude Opus 4.8? If you already use Claude Opus 4.7 for complex reasoning, programming, or autonomous agents, Opus 4.8 is worth testing early. Changes such as the 1 million token context window, lower prompt caching threshold, and mid-conversation system messages all target real production problems, not only short prompt quality. Still, engineering teams should not upgrade blindly. Review sampling parameters, thinking configuration, default effort expectations, and cost implications if you plan to use fast mode. For products handling sensitive data or critical workflows, run an A/B test on representative tasks before moving all traffic to Claude Opus 4.8. Conclusion Claude Opus 4.8 shows that Anthropic is putting more weight behind the agent and developer market. The improvements are not only about reasoning quality; they also cover operational details such as caching, mid-conversation system messages, output speed, and refusal classification. For teams building serious AI products, this is a release worth watching because it addresses real deployment issues in long-term AI applications.

Artificial intelligence (AI) is fundamentally changing how people build applications. You no longer need to be a professional developer. With a smart AI assistant, you can turn any idea into a real product. Google AI Studio is the clearest proof of that shift. The platform lets anyone, even without coding knowledge, build their own app. With the latest update, creating an AI app is as simple as having a natural conversation: describe your idea in plain language, and let AI handle the rest. Google AI Studio: Build AI apps without code and create Android apps with ease Google AI Studio is a browser-based development environment designed to simplify prototyping and building applications on top of Google's powerful AI models. Notably, the platform now supports direct creation of complete Android applications, opening the door for anyone who wants to ship a mobile product without writing a single line of code. If Gemini was once described as the "brain" of an application, Google AI Studio now gives it "hands and feet" through direct connections to APIs and SDKs within Google's ecosystem (via the "Supercharge your apps with AI" section). This makes expanding functionality incredibly easy, and you can make your app behave exactly as intended without manually configuring APIs or SDKs from scratch. Third-party APIs and SDKs still require manual input, but Google's vast ecosystem including Nano Bananas, Veo 3, Text-to-Speech, Google Search, and especially Google Maps covers nearly every common need out of the box. Through personal testing, Google Maps works reliably for mini apps in Vietnam, such as navigation tools or real-time traffic viewers. When pulling data from Google Search, the quality of results is impressive enough to eliminate the need for third-party scraping tools entirely. Another major advantage: Google AI Studio is currently completely free to use. The free credits Google provides are generous enough to comfortably explore Gemini 3, Nano Banana Pro, Veo 3.1, and many other tools for personal use without spending a thing. Step-by-step guide to creating a mini AI app Building an app in Google AI Studio is straightforward. Just follow these steps: Step 1: Access and set up Visit: Go to the Google AI Studio tool page. Sign in: Log in with your Google account. Start building: Open the "Build" tab. Under the Start tab, you can choose an AI model (default is Gemini 3.5 Flash) and select a programming language: React, Angular, or Android. If you skip this, AI defaults to React. Step 2: Come up with an app idea If you don't have a specific idea yet, browse the App Gallery to see sample apps built by Google and the community. It's the fastest way to find inspiration and understand what's possible. If you want something even more hands-off, just click the I'm feeling lucky button in the Start tab. Google AI Studio will instantly suggest interesting ideas, complete with example API and SDK integrations (under the Supercharge your apps with AI section) and the prompts AI uses to build them. It saves time and teaches you how AI thinks when creating apps. If you already have a clear idea, move straight on to the next step. Step 3: Write a specific prompt If you don't have a detailed prompt covering all the functionality, language, and interface requirements like the samples in the I'm feeling lucky button, that's completely fine. You can create an app with just a single sentence, for example: "Create a photo collage app for me." From there, AI will automatically make all the decisions and carry out the remaining steps for you. That said, the more detail you provide, the closer the result will be to your vision, which means less time editing afterward. If possible, include reference images or mockups from tools like Figma or Canva, since AI can understand and recreate interfaces almost exactly from those references. Don't forget to add extras in the Supercharge your apps with AI section to let AI automatically connect the APIs or SDKs you need, or even enable intelligent reasoning mode for your app. Here's an example of a detailed prompt you can reference: "Create an AI Web App that allows users to: Upload 2 images (1 & 2) so the app combines them into 1 composite image. Support multiple aspect ratios: 1:1, 16:9, 4:3, 3:2. Include image preview and a Download button. Save creation history (including result image, prompt, and timestamp)." Once your prompt is ready, just click Build and wait a few seconds to see the result. Step 4: AI automatically handles the build Build process: AI Studio runs through several stages, including: Defining the UI Scope. Developing the React App. Planning the app structure. Integrating Gemini API. Auto fix errors. Preview and edit via conversation: A live preview of your mini app appears directly in the browser, so you can see it in action right away. Developers can edit the code directly in the code panel. But if you're not technical, that's no problem at all. Just chat with AI to add, remove, or adjust features without touching a single line of code. For example, you could say: "Add images 3 and 4 so I can merge four photos into one" or "Switch the interface to dark mode." If you didn't add APIs or SDKs in the "Supercharge your apps with AI" section earlier, don't worry. With a simple prompt, AI will automatically integrate the necessary APIs or SDKs into your mini app quickly and with minimal effort. You can even request advanced features like: Generate video from images using Veo 3, and the app will automatically connect to the Veo API. Add a speech-to-text button to make the app more interactive. And the most exciting part: you can edit your app visually, just like working in Canva or Figma, using the Annotate app button where you can draw, add text, change colors, and more, all in the most intuitive way possible. Step 5: Test and deploy Action How to do it Test in browser Click the "Run" button or view the live preview. Share app via link Click "Share" and copy the link. Download source code Click "Download" (ZIP file containing React + TypeScript code). Deploy to cloud Click "Deploy" and select Google Cloud Run (requires a Google Cloud account). Can you build a complete app with Google AI Studio? For personal use or quick idea testing, Google AI Studio is an excellent choice: easy to use and nearly zero cost. However, if you want to build a full-stack application with a proper backend, UX, and UI without any coding knowledge, you'll want to consider more suitable platforms. Comparison with Google Antigravity IDE While Google Antigravity is an IDE focused on helping professional developers write code faster through asynchronous background agents, Google AI Studio targets non-technical users in the no-code/low-code space. With AI Studio, there's no software to install and no environment to configure. Everything happens through natural language descriptions right in the browser. Antigravity, on the other hand, offers deeper control over source code, multi-model support (Claude, GPT), and is better suited for complex projects that require refactoring an existing codebase. Goal Recommended tool Personal use, rapid prototyping, idea testing Google AI Studio Commercial app development, full-stack products, scalability needs Google Firebase, Lovable, Bolt, Replit, Antigravity Google AI Studio is not the optimal choice for large-scale products or applications requiring high security. Instead, you can download the source code from AI Studio and upload it, or sync it directly via GitHub, to continue building on platforms like Firebase Studio (within the Google ecosystem), Lovable, Replit, Bolt, or Antigravity. These platforms help you complete your app with powerful backend features while still leveraging the AI foundation built in Google AI Studio.

Google isn't just adding a new model to Flow. At Google I/O 2026, the company is turning Flow into an agentic AI creative studio — complete with custom tools, conversational video editing, and a mobile app. For video creators, the signal is clear: the race is no longer about generating a beautiful clip from a single prompt, but about the ability to edit, iterate, and refine ideas like a real production pipeline. Gemini Omni turns Flow into a conversational video editing studio According to Google's announcement on May 19, 2026, Flow has been upgraded with Gemini Omni, with Omni Flash being the first model introduced to the experience. Google describes Omni Flash as a model capable of generating content from multiple input types — starting with video — while combining Gemini's intelligence with Google's generative media models. The simplest way to understand it: think of Omni Flash as the video equivalent of what Nano Banana did for images. If Nano Banana made photo editing feel more natural and conversational, Omni Flash brings that same approach to video — where users can pull from real-world inspiration, existing footage, and iterative prompts to keep refining their work. Critically, Google says Omni Flash improves character consistency, meaning identity and voice can be preserved across multiple scenes. Flow Agent and Tools bring AI into the entire creative workflow The second major upgrade is Google Flow Agent. Rather than simply accepting a prompt and returning a result, this agent is designed as a creative collaborator capable of planning, reasoning through complex tasks, and supporting users at multiple stages of the process. Google gives examples like the agent suggesting dialogue for a specific scene or proposing story development directions. As a project deepens, Flow Agent can generate multiple variations simultaneously to give users more options, and supports batch editing so changes are applied across many assets at once. Once enough material is gathered, the agent can also organize assets into collections and rename them in more intuitive ways. This feature is now available to all Flow users globally. The more interesting part is Google Flow Tools, where users can build their own tools and workflows using natural language. If you want a custom image preset, a video resize tool, or a personalized shader, Flow Tools lets you describe what you need rather than writing code. In other words, the vibe coding concept is moving into the content creation environment — not just sitting inside a developer's IDE. All Flow users globally can access pre-built Tools Google AI users can create and remix their own Tools Custom tools can be shared for others to remix Flow Music also gets meaningful upgrades for music creators Google Flow Music received a set of new features as well, with the most significant being the ability to edit songs at the section level. Users can select a specific portion of a track to rewrite lyrics, translate them, change the beat drop, or sample a passage and develop it in a different direction — all without affecting the rest of the track. The covers feature lets users transform the style of an entire song while preserving its original melody and structure. For example, a track could be shifted into a lo-fi study aesthetic for a study playlist or background content. For creators who are newer to AI music tools, this approach is far more accessible than having to regenerate from scratch every time they want to change the sonic character of a piece. Gemini Omni also appears in Flow Music to support music video creation. Users can work conversationally with the agent, directing style, subjects, and shots to match the story and rhythm of the underlying track. This feature is available to Google AI users, and it signals Google's intent to connect three layers of creative work: audio, visuals, and narrative. A mobile app takes Flow beyond the desktop Google also announced mobile apps for both Flow and Flow Music. The web version remains the most capable environment, but the mobile app lets users capture ideas, run quick tests, or make fast edits when they're away from their computers. Conclusion The biggest takeaway from this round of upgrades isn't any single feature. Google is connecting Gemini Omni, Flow Agent, Tools, and Flow Music into a more complete end-to-end workflow — from ideation and asset creation, through batch editing and resource organization, to publishing both music and video content. If you work with video, music, or short-form content, the most practical starting point is to bring in a real asset of your own and see how well Omni Flash holds character consistency, voice, and editing continuity across multiple rounds. If it handles that reliably, Flow will no longer be just an AI video generation tool — it becomes a content production environment worth watching closely through the rest of 2026.

At the Google I/O 2026 event, the search giant stunned the entire developer community by officially announcing Antigravity 2.0. No longer a conventional AI-integrated IDE, Antigravity has now transformed into a standalone desktop application powered by Gemini 3.5 Flash, accompanied by an AI Ultra subscription package priced at $100/month. However, the complete removal of the integrated source code editor in favor of a minimalist Codex-like interface is generating intense controversy. How Antigravity 2.0 Has Transformed The decision to completely separate the source code editor from Antigravity 2.0 marks a bold move by Google in reshaping the future of software development. Instead of attempting to integrate AI features into a traditional IDE, this new version functions as a dedicated AI agent orchestration hub. This means users will focus entirely on setting up tasks and monitoring workflows rather than directly editing individual lines of code. This change is most clearly demonstrated by the launch of the AI Ultra service package, priced at $100 per month. This premium subscription offers 5 times the usage limit compared to the current AI Pro package, targeting businesses and professional developers who need to operate a large number of autonomous agents simultaneously to solve complex problems. Power from Gemini 3.5 Flash and Asynchronous Execution Workflow At the heart of Antigravity 2.0 is the Gemini 3.5 Flash large language model, specially optimized for high-speed agentic tasks. Thanks to its superior processing capabilities, the new system supports highly complex multi-agent workflows, allowing multiple subagents to collaborate on a large project. More specifically, these subagents will run entirely asynchronously in the background. This mechanism ensures that the application's main interface never freezes or is interrupted during processing, helping developers maintain a smooth workflow. This is a significant improvement over its predecessor, which often experienced delays when processing large codebases. New Tool Duo: Antigravity CLI and SDK Antigravity CLI, written in Go, completely replaces the old Gemini CLI, delivering high performance and extremely fast response times in the terminal. Gemini CLI and Gemini Code Assist IDE extensions will cease service from June 18, 2026. Google AI Pro and Ultra users need to switch to Antigravity CLI before this deadline. Antigravity SDK, written in Python, allows developers to build, customize configurations, and deeply integrate autonomous agents into their projects. Minimalist Codex-like Interface and Community Controversy Despite boasting numerous powerful technological upgrades, Antigravity 2.0 is facing a wave of criticism from the user community due to radical interface changes. The new interface is now merely a minimalist console focused on a chat window for issuing commands to agents, completely eliminating the familiar IDE workspace. Many opinions suggest that this design looks exactly like a replica of the Codex or Claude Desktop application. This excessive minimalism has left many developers feeling disappointed and empty, as they no longer have the ability to quickly view and modify files directly as before. Having to switch back and forth between Antigravity and an external editor significantly reduces their actual work efficiency. How to Restore the Traditional IDE Experience for Users To appease the negative reactions from the community, Google has offered some temporary solutions for those not yet ready to adapt to the new interface. Users can visit the official Antigravity homepage to download a separate IDE version. This version will help restore the familiar integrated workspace with traditional source code editing features. However, Google also issued a warning that this is only a temporary solution. In future updates, the agent management interface will be completely removed from the IDE as the company focuses all development resources on the standalone 2.0 application. Therefore, familiarizing oneself with the new working model is inevitable for developers in the long term. The Rapid Evolution of Tools like Antigravity and Codex The separation between traditional code editors and agent control interfaces is clear evidence that AI is shifting from a supportive tool to an autonomous partner. Developers need to proactively familiarize themselves with new control tools like CLI and SDK to gradually transition their role from direct code writers to managers and orchestrators of intelligent agent ecosystems.

Have you ever opened a 3,000-word article on your phone and instantly debated whether to read it or just leave? Mozilla has an answer: shake your phone. The "Shake to Summarize" feature — named one of TIME's best inventions of 2025 — has officially launched on Android alongside Firefox 150. What is Shake to Summarize and how does it work? Shake to Summarize is an AI feature built directly into Firefox that lets users get an instant summary of any webpage without leaving the browser or opening another app. There are three ways to trigger it: Shake your phone while viewing a page Tap the lightning bolt icon in the address bar Go to the three-dot menu → Summarize Page Within seconds, Firefox opens a small panel displaying the key points of the page. What makes it stand out is how the summary adapts to content type — recipes get the actionable steps, sports articles focus on scores and stats, and news pieces highlight the key developments. The feature works with pages under 5,000 words. For longer pages, Firefox will not be able to generate a summary. The journey from iOS to Android Shake to Summarize first launched on iOS in September 2025, initially available only to US users in English. The response was strong enough that Mozilla received a special mention in TIME Best Inventions 2025 — a recognition rarely given to a browser feature. The Android version went through careful testing on Firefox Nightly before making it into the official Firefox 150 release in April 2026. Prior to that, trying it on Android required going to Settings → About Firefox Nightly → tapping the logo three times to enter "Secret Settings" and manually enabling it — a process clearly meant for technical users only. What AI powers this feature? Mozilla doesn't use a single model — it splits the work by device: On iPhone 15 Pro and later running iOS 26+, summaries are generated entirely on-device via Apple Intelligence, meaning data never leaves the phone. On all other devices, page content is sent to Mozilla's AI servers, processed, and returned to the user. On Mozilla's end, the engineering team tested several models — including Mistral Nemo, Mistral Small, Jamba 1.5 Mini, Gemini Flash 2.0, and Llama 4 Maverick — before settling on Mistral Small as the primary model. The reasoning: Mistral Small has open weights, fast inference, and significantly lower cost compared to alternatives, while still delivering high-quality summaries. Mozilla provides Shake to Summarize for free and covers all inference costs itself, with no charge to users. What if users don't want AI? This is where Mozilla handled things fairly well. After facing pushback from long-time users concerned that Firefox was abandoning its core privacy values, Mozilla added a setting to disable all AI features entirely. On desktop, a "Block AI enhancements" option lets users turn off all current and future AI features, or selectively keep specific ones. On Android, Shake to Summarize is tied to the new AI Controls panel — when AI is turned off, both the shake gesture and the summarize button are disabled simultaneously. The feature currently supports English content only. Users outside English-speaking regions will need to switch their system language or wait for Mozilla to expand language support. What else is new in Firefox 150? Alongside Shake to Summarize on Android, Firefox 150 brings several other noteworthy updates: Open links in split view to browse two pages side by side Copy URLs from multiple tabs at once Real-time private translation on a dedicated translation page Free built-in VPN now expanded to Canada (previously limited to select markets) A new profile management system for all users Firefox 151 is expected on May 19, 2026 and may continue expanding AI Controls on mobile. Real-world assessment Shake to Summarize addresses a genuinely real problem: skimming on a phone is uncomfortable, but reading in full takes too long. Rather than asking users to open yet another AI app, Mozilla embeds summarization directly into the browsing flow — the shake gesture may look playful, but it's actually the fastest shortcut imaginable on mobile. The biggest limitation right now is the English-only restriction, which significantly reduces its value for non-English speakers. But if Mozilla continues its language expansion roadmap — as it has done with its translation feature — this could become one of the most compelling reasons to return to Firefox on mobile.

The human won. But his left arm was nearly broken, his fingers blistered, and he admitted he was about 30 minutes away from giving up during a live goods-sorting competition at Figure AI. The robot, of course, was still running — no fatigue, no pain, no need for a break. That's the story behind the human "victory" medal in this head-to-head sorting showdown. A 10-hour showdown between human and machine Figure AI — the humanoid robotics company valued at $39 billion — staged a live test called "Man vs. Machine": robot F.03 (Figure 03) versus an intern named Aime in a 10-hour goods-sorting shift. The task was repetitive to the point of monotony: scan a barcode, pick up a package, place it barcode-down onto the conveyor belt — over and over, without stopping. End-of-shift results: Aime (human): 12,924 packages — averaging 2.79 seconds per item F.03 (robot): 12,732 packages — averaging 2.83 seconds per item The margin: 192 packages and 0.04 seconds per cycle. By the literal scoreboard, the human won. But what does "winning" actually mean here? CEO Brett Adcock wrote on X after the match: "Congrats Aime! He said his left arm is basically broken 😂 This is the last time a human will ever win." During the competition, F.03 briefly overtook Aime around the fifth hour — exactly when he stood up to use the bathroom. The robot doesn't need that. It just needs a power supply. [VIDEO:CvkcPKlnQY4|Livestream of the human vs. robot sorting match|Livestream of the human vs. robot sorting match] And that's precisely the point the 12,924 vs. 12,732 scoreline fails to capture. The robot doesn't high-five or crack open a beer After 10 hours, Aime sat down, rubbed his arm, and exhaled. He admitted another 30 minutes would have forced him to quit due to lower back pain and forearm strain. F.03 kept running — no celebration, no rest, no one needed to pat it on the back. And almost certainly, while Aime slept that night, the robot was still sorting the next shift. Under California labor law, Aime is entitled to a paid lunch break and rest periods during his shift. The robot falls outside the scope of any labor code. This isn't an injustice — it's the nature of the problem: humans and machines are playing by two entirely different sets of rules. One shift versus a full work week Performance comparisons typically focus on an 8–10 hour window. But extend the measurement to a full work week and the picture changes entirely. Figure AI had previously demonstrated that F.03 can run continuously for 24 hours, processing over 30,000 packages without a single downtime error. Humans work five days a week; the robot can run seven days, across three shifts. An expert at Ohio State University noted that during the livestream, F.03 still made errors — misplacing packages and dropping items off the conveyor. Humanoid robots remain a "science project" for many real-world deployment environments. What kind of robot is Figure 03? F.03 was unveiled by Figure AI in October 2025. The robot stands 5'8" (about 173 cm), weighs 61 kg, can carry up to 20 kg, and charges wirelessly through a pad integrated into the sole of its foot. A standout feature is its tactile fingertips, which can sense forces as light as 3 grams — sensitive enough to handle fragile objects without breaking them. At BMW's Spartanburg plant, the previous generation (F.02) assembled over 30,000 vehicles with 99% accuracy. Figure is now building a factory called BotQ with an initial capacity of 12,000 robots per year, targeting 100,000 robots per year within a few years. Why does this result matter — even though the human won? Not because robots are about to take every warehouse job tomorrow, but because the performance gap between humans and machines in repetitive physical labor is narrowing at a concerning pace. A year ago, F.03 likely would have lost by a much wider margin — today the gap is just 0.04 seconds per package. Adcock has already announced improvements to both hardware and AI software for next year, and according to him, next time humans won't have a chance. Worth noting: this competition wasn't designed for the robot to win immediately. It was designed to prove the robot is close enough to keep pace with a human — and from there, create both psychological and commercial pressure across the logistics market. Microsoft AI CEO Mustafa Suleyman has forecast that AI will automate most office work within 12–18 months. For physical labor, this competition suggests the boundary is thinning fast — and "the last time a human will ever win," in the most literal sense, may not be far off. What remains after the race The trial's results have sparked lively debate about the future of the logistics labor market. Now that humanoid robots have reached near-human performance levels, scaling their deployment is largely a question of time and manufacturing cost. Businesses will increasingly shift repetitive, physically demanding tasks to machines. That said, this doesn't mean humans will be entirely replaced in smart warehouses. Rather, human workers and intelligent AI systems will migrate toward roles in system supervision, handling complex edge cases, and managing supply chains at a higher level. The right combination of robotic endurance and human judgment will define the next generation of high-efficiency warehouse operations.

Three million Codex users per week — up six times in just the first three months of 2026. That number tells you something: Codex is the rising star. OpenAI is turning it into an all-in-one tool, which means Codex is no longer just a playground for developers. What is Codex? A tool that's not just for developers Think about this scenario: you want to build a spreadsheet that automatically updates every week, or a small website to let customers book appointments, or simply a tool that summarizes your email reports each morning without opening dozens of tabs. Previously, these things required a developer. With Codex, you just type your request in plain English and wait for the result. Codex is OpenAI's AI agent, launched in May 2025 and deeply integrated into the ChatGPT ecosystem. Its core difference from regular ChatGPT is that Codex doesn't just answer — it actually does the work through a code execution environment. You assign a task, Codex plans it out, executes each step, checks the result, and returns a finished product ready to use. No need to understand what code is, no need to monitor every command. Codex can now run as a standalone desktop app available for both Windows and macOS, and has recently expanded to Android and iOS on mobile as well. You can sign in using your existing ChatGPT account. Codex is currently available on ChatGPT Plus, Pro, Business, and Enterprise plans, though Free and Go users also get limited trial access. What Codex can do for you Build apps or small websites from a description You don't need to know HTML or JavaScript. Just describe what you need: "Create a simple appointment booking page with fields for name, phone number, and date/time selection, and send an email notification whenever someone books." Codex will build the entire interface, handle the logic, and guide you through publishing it online. A startup team in the US once shared that they completed in one weekend what would have previously taken an entire quarter — and it wasn't a team full of developers. Automate repetitive tasks This is where non-developer users will find the most value. For example: every week you have to consolidate revenue data from three different Excel files, merge them, and send a report to your manager. Codex can build an automated workflow that does this for you on a schedule and delivers the result without you ever opening your laptop. With the Automations feature launched in the April 2026 update, Codex can take on long-horizon tasks, pause, resume, and complete them over multiple days without needing to be reminded. Generate images and prototypes directly in the app Codex integrates image generation powered by GPT Image 2.0 directly inside the app. You can ask Codex to create interface mockups, product banners, or illustration assets for a document — all within the same workflow, without switching to another tool. For content creators, marketers, and solo founders, this is a genuine advantage: the entire journey from idea to finished output can happen in a single window. Control your computer to work in the background Since April 2026, Codex can operate Mac applications using its own cursor, viewing the screen and clicking and typing to complete tasks while you continue using the machine normally. A simpler way to picture it: you're in an online meeting while Codex has Figma open, editing a design and saving the file according to instructions you set earlier. Two things happening in parallel, neither getting in the other's way. The computer use feature is currently only available on macOS and is not yet available in the EU or UK. You will need to grant Codex Accessibility and Screenshot permissions during initial setup. How to get started with Codex Codex requires installing the desktop app on Windows or macOS — it does not run directly in a web browser. The setup process is straightforward and takes just a few minutes. Step 1: Go to openai.com/codex and download the version for your operating system. On macOS, there are two separate builds for Apple Silicon (M1 and later) and Intel chips. On Windows, there is a single universal build. Step 2: Install the app and sign in with your existing ChatGPT account or OpenAI API key. Step 3: Choose a project folder you want Codex to work within — you can also link it to a github repository — or skip this step if you only want to assign standalone tasks like creating files, generating images, or automating a workflow. Step 4: Type your request in natural language, and be as specific as possible. Instead of "make me something about a report," try: "Create an Excel file summarizing monthly revenue from the data I provide, add a bar chart comparing each month, and highlight the month with the highest revenue." The more specific your request, the better the result. Codex works best when you clearly describe the input, the desired output, and any constraints you need — such as file format, display language, or calculation rules. Codex vs Claude Code, Antigravity, and Cursor from a non-technical user's perspective If you're not a developer, the real question isn't "which tool is technically more powerful" — it's "which tool can I use right now without learning anything new." From that angle, these four tools are clearly different from one another. Codex and Claude Code Claude Code from Anthropic is Codex's most direct and formidable competitor. In terms of raw technical output quality, Claude Code currently leads the pack — producing cleaner code, tighter logic, and handling large, complex codebases more effectively. However, Claude Code is explicitly designed for developers: it runs in the terminal, requires command-line installation, and notably has no image generation capability. If you're not comfortable in a terminal, Claude Code is a barrier from the very first step. Codex, by contrast, offers a more user-friendly desktop interface, integrates image generation within the same workflow, and is noticeably more accessible to non-technical users. Codex and Antigravity Both require a desktop app, but their underlying philosophies are completely different. Codex is built around a "hand off the task and wait for results" model: you describe what you need, the agent runs in an isolated cloud sandbox, and returns a finished product without affecting your machine at all. It suits people who want to automate workflows, create files, or build something without monitoring every step. Antigravity works in the opposite direction: the agent runs directly on your machine, watches your screen, opens applications, and collaborates with you in real time while you work. If you want an AI colleague working alongside you — observing and reacting to what's happening on your screen — Antigravity is the better fit. Codex and Cursor Cursor is built on VS Code and targets developers who want to keep their familiar working environment intact. For non-coders, Cursor is largely inaccessible because the entire experience revolves around editing code inside an editor. Cursor excels at understanding an entire codebase and offers flexibility in choosing AI models, but those advantages are for developers — not for general users who need to automate workflows or build something from scratch. In summary, from a non-technical user's perspective: Codex: Friendly desktop interface on Windows and macOS, capable of generating images, well-suited for users who want AI as an automated workflow tool. Claude Code: Best technical output quality, but developer-oriented and cannot generate images. Antigravity: Agent works directly on your machine in real time, suited for users who want to collaborate with AI while they work. Cursor: Best for developers keeping their VS Code workflow intact; not suited for general users. Who is Codex best for? If you're a content creator who wants to build a landing page for a campaign without hiring a developer, Codex fits. If you're a marketer who needs to automate weekly reports pulling from multiple data sources, Codex fits. If you're a solo founder who needs to ship a product fast without a technical team, Codex fits. If you're a teacher who wants to build a small quiz app for students without learning to code, Codex fits. On the other hand, if you're a developer who needs granular control over every line of code in a large, complex codebase, Claude Code will deliver better output quality. Codex is the right tool for people who want fast results without needing to understand what's happening under the hood. One practical limitation worth knowing: Codex currently has full support for Python, JavaScript, TypeScript, and Ruby. For tasks that don't involve code — like generating images, automating workflows, or creating documents — this language limitation has no impact on you. The line between "can code" and "can't code" is fading The question "do you know how to program?" is losing its weight as tools like Codex continue to evolve. What matters more now is whether you can describe clearly what you want — because that's exactly the thinking skill required to work effectively with Codex and similar AI agent tools. If you want to try it today, start with something small and specific: ask Codex to create an Excel file consolidating data you currently process manually every week. That's the fastest test to evaluate whether Codex genuinely saves you time or not.

Markdown has been the default standard when working with AI for years, but an engineer from Anthropic's Claude Code team just raised a thought-provoking question: is that habit really the best choice? Thariq Shihipar's short post gathered over 15,000 likes on X in just a few days, and the reason is more convincing than you might think. Markdown was born in the era of token-poor AI Looking back at the days of GPT-4 with a context window of only 8,192 tokens, Markdown was an entirely reasonable choice. HTML was bulkier, consumed more resources, and in that constrained context, Markdown's simplicity was a real advantage for saving tokens. Thus, Markdown became the implicit standard, and that habit has stayed with us ever since. Even when Anthropic created the concept of Skills on Claude, they also set Markdown as the standard with the SKILL.md file—anyone who works with skills is surely familiar with this default. However, current AI models operate on a completely different scale. Many models now support context windows from 200,000 to 1 million tokens, and the cost of processing is no longer a major barrier (as Thariq Shihipar points out). He argues that this is the perfect time to reconsider that default. What can HTML do that Markdown cannot? The core reason Thariq presents is simple: some types of information are inherently spatial, but Markdown forces them to be linear text. When you compare three technical approaches, you need to see them side-by-side, not read them one after another and try to keep them in your head. When you review a code diff, you need to see the structure of the changes, not just a wall of text. HTML solves exactly that problem, which is why Thariq listed 9 specific groups of scenarios where HTML outclasses Markdown: Discovery and Planning: Comparing multiple approaches side-by-side instead of sequentially, and then transforming them into an implementation plan complete with flowcharts and timelines. Code Review and Understanding Project Structure: Highlighting changes directly with colors based on severity, and showing module diagrams as boxes and arrows—rather than plain text. UI Design: Displaying actual color palettes that can be copied instantly, and rendering UI component variants directly instead of describing them in words. Rapid Prototyping: Creating interactive animation adjustment panels with slider controls, and screens that can actually be clicked—something Markdown cannot express. Diagrams and Illustrations: Utilizing inline vector graphics to draw actual flowcharts, rather than stitching together ASCII characters. Slide Decks: A few <section> tags and 20 lines of JavaScript can form a slide deck navigatable with arrow keys, without needing specialized software or export steps. Research and Learning: Structuring documents with collapsible sections, code tabs, and glossaries—rather than dumping the entire content in a single vertical stream. Periodic Reports: Weekly status summaries with sparklines and color-coded progress indicators that actually encourage people to read, rather than just skim. Custom Editing Interfaces: Building drag-and-drop task boards or feature flag dashboards with dependency alerts—making it a functional tool rather than just text to read and forget. Thariq has assembled 20 files illustrating all of these categories at thariqs.github.io/html-effectiveness, each of which opens directly in your browser without requiring any installation. How to use HTML with AI in practice? Applying this is not complicated; it just requires a shift in how you write prompts. Instead of letting the model choose the output format, explicitly specify HTML when the content is meant to be reviewed, interacted with, or shared with others. For example, here is a prompt Thariq suggests for reviewing code: Help me review this PR by generating an HTML document that describes it. I'm not very familiar with streaming/backpressure logic, so please focus on that part. Show the actual diff with inline margin comments, color-code findings by severity, and include anything else necessary to explain the concepts clearly. Similarly, you can ask the AI to generate an implementation plan as HTML with a timeline and data flow diagram, or a weekly status report with small charts and progress-colored indicators. Simon Willison, author of the famous tech blog, also admitted that this article made him reconsider his habit of using Markdown from the GPT-4 era until now. When modern AI models can embed vector graphics, interactive widgets, and in-page navigation, Markdown is no longer the obvious default choice. Markdown still has its place, but not everywhere Thariq is not saying we should always use HTML; rather, he makes a clear distinction: Markdown is suitable for casual chats, short code snippets, brief answers, and anything that is pure text. Meanwhile, HTML shines when the output requires spatial layouts, colors, interactivity, or complex structures—where the content is multi-dimensional enough that Markdown would start flattening the information rather than conveying it effectively. The community reacted quickly: a skill named html-artifacts has appeared on GitHub, helping AI automatically recognize when it should generate HTML files instead of Markdown. It includes the 9 scenarios from Thariq's original article and can be used with any model that supports reading skills. Notably, this skill has clear exclusions for short answers and code-only outputs. You can check it out at github.com/dogum/html-artifacts. Thariq doesn't mention JSON in his article, but it is also a very popular format when working with AI, especially for those who frequently use n8n, Make, or Zapier. Nevertheless, each format brings its own flavor to specific situations. How Markdown, HTML, and JSON divide their usage The debate is actually not just about Markdown or HTML. JSON is also a very popular format when working with AI, especially in data processing workflows and system integrations. These three formats serve three different purposes, and understanding those boundaries helps you choose the right tool for each situation. Markdown is best for text read directly in chat: notes, short explanations, code snippets, simple documents. Fast, lightweight, no need to open anything else. HTML is best when the output needs to be visualized, interacted with, or shared: reports with layouts, diagrams, comparison tables, slide decks, custom interfaces. Open with a browser and you are good to go. JSON is best when the output needs to be processed by a machine: storing structured data, transferring between systems, or feeding into the next step of a workflow. Humans can read it, but it is not meant for reading. In other words, JSON does not compete with HTML or Markdown in terms of presentation; it serves an entirely different purpose. The real issue is that many AI users default to receiving output in Markdown even when they need HTML to view it or JSON to process it. By simply specifying your preference in the prompt, the AI will adapt. Quick Decision Rule: Output to read in chat → Markdown. Output to view in a browser → HTML. Output to be processed by a machine → JSON. What does this change for the average AI user? If you use AI primarily for Q&A or writing, this change has less impact. But if you are using AI for more complex tasks like data analysis, project planning, document reviews, research synthesis, or creating reports for colleagues, this is a small prompt adjustment that creates a clear gap in output quality, regardless of which AI tool you are using. You should try it once: next time you need the AI to compare options or summarize a complex document, add "generate as an HTML file" to the end of your prompt. Open that file in your browser and compare it to how you usually do it with Markdown or JSON—the results will speak for themselves.

Anthropic has just announced a partnership with SpaceX to access over 220,000 NVIDIA GPUs and will immediately use this new computing power to increase usage limits for both Claude Code and API. Here's what's changing and why it matters to users. Why Did Anthropic Partner with SpaceX? In recent months, Anthropic has continuously signed large-scale computing agreements with Amazon, Google, Microsoft, and NVIDIA. This time, the company has added another unexpected name: SpaceX. According to the announcement on May 6, Anthropic signed an agreement to use the entire computing capacity at SpaceX's Colossus 1 data center, equivalent to over 300 megawatts of power and more than 220,000 NVIDIA GPUs. This entire capacity will be put into use within one month and will directly improve the experience for Claude Pro and Claude Max users. Colossus 1 is SpaceX's AI data center, currently one of the largest GPU clusters in the world. Anthropic is the sole tenant of its entire capacity. Specific Changes to Usage Limits Thanks to the new computing resources, Anthropic has implemented three changes effective immediately from the announcement date Doubling Hourly Claude Code Limits The 5-hour rate limit for Claude Code is doubled for Pro, Max, Team, and Enterprise plans. If you previously could only run 10 complex Claude Code commands, this is now doubled to 20, which will be significantly helpful. However, it's important to note that the weekly limit remains unchanged, so while increasing the 5-hour limit allows for more intensive work in a short period, it might cause you to hit the weekly cap faster. Removing Peak Hour Limits Previously, Claude Code automatically reduced usage limits during peak hours (typically from 9 AM to 3 PM) for Pro and Max accounts. This limit has been completely removed, so users can now use Claude Code at full speed regardless of the time of day. For users who often work in the evening (which coincides with US peak hours), this change is likely to have the most noticeable impact. Significantly Increasing API Limits for Claude Opus Models The API rate limit for Claude Opus models has been significantly increased. Details of the multiplier increase are published by Anthropic in the following table: This change is particularly important for developers building applications on the Claude Code platform Anthropic's Overall Computing Strategy The agreement with SpaceX is not an isolated move. In recent months, Anthropic has built a remarkable infrastructure portfolio: An agreement for up to 5 gigawatts with Amazon, with nearly 1 GW operational before the end of 2026 A 5 GW agreement with Google and Broadcom, expected to be operational from 2027 Strategic partnerships with Microsoft and NVIDIA, including $30 billion in Azure capacity A $50 billion investment in AI infrastructure in the US with Fluidstack And now, over 300 megawatts from SpaceX's Colossus 1 data center Anthropic runs Claude on various hardware platforms — AWS Trainium, Google TPUs, and NVIDIA GPUs — and states that it continues to seek additional computing power sources. Notably, within the framework of the agreement with SpaceX, both parties also expressed interest in developing orbital AI computing capabilities, i.e., placing GPUs on satellites. This is still a very early-stage idea, but if realized, it would be a major turning point for global AI infrastructure. Expanding to International Markets A portion of the expanded computing capacity will be used to serve international enterprise customers, especially in sectors requiring local data storage such as finance, healthcare, and government. The agreement with Amazon also includes additional inference capacity in Asia and Europe. Anthropic also emphasized that it only expands to countries with democratic legal frameworks and secure hardware supply chains, demonstrating a cautious stance amid increasingly fierce geopolitical competition in AI. What Does This Mean for Claude Users in Vietnam? From a practical perspective, the three changes to usage limits directly benefit those who use Claude Code daily — especially programmers and individuals who work continuously with Claude Code. The removal of peak hour limits also means that the experience for users in Vietnam (whose time zone often coincides with peak load periods in the US) will be more stable. In the long term, greater computing power often means the ability to deploy more powerful models at lower costs. This is the foundation for Anthropic to continue competing with OpenAI and Google in the 2026 AI race. Anthropic is Always Evolving Anthropic is seriously investing in infrastructure, and the partnership with SpaceX is the latest step in that strategy. The most immediate result users can feel is that Claude Code will be less restricted, and API speeds will certainly improve. In the long run, the computing race among major AI companies promises many more interesting developments in 2026.

Anthropic had previously introduced Claude to Excel, PowerPoint, and Word, and has now opened the public beta for Outlook. If you've been following Anthropic's release history in recent months, the question is no longer what feature they will launch next, but rather if there is any software they haven't jumped into yet. Claude is now available across all Microsoft Office applications From now on, all paid plan users can install Claude into Microsoft's office suite. Claude for Excel, PowerPoint, and Word have been available for a while, while Claude for Outlook is entering public beta for all paid tiers. The biggest difference compared to other Office AI assistants is that Claude does not act like a chatbot locked in individual apps. Instead, conversation context is maintained seamlessly as you move between applications—from Outlook to Word, then Excel, and on to PowerPoint—without needing to explain yourself from scratch. Claude for Microsoft 365 can be installed via Microsoft AppSource. A single package covers Excel, PowerPoint, and Word, while a separate package is available for Outlook. Administrators can perform centralized deployments from the Microsoft Admin Center. [VIDEO: F6dzjaBCBtU |Claude for Microsoft 365 (Anthropic)|Claude for Microsoft 365 (Anthropic)] What can Claude do in each application? Excel: Far beyond just explaining formulas Claude for Excel can read multi-sheet workbooks, explain formulas with cell-by-cell references, build financial models with live formulas, and update assumptions without breaking dependency structures. Every change is tracked and clearly displayed so users always know which cells Claude used. PowerPoint: Working directly within your slides This is the most notable feature: Claude for PowerPoint reads the native slide structure, detects existing fonts, colors, and layouts, and then generates new content in that exact style. The charts it produces are native PowerPoint charts that are fully editable, not pasted screenshots from elsewhere. Word: Tracked edits and replying to comments Claude for Word works the way editors like: all edits appear as tracked changes, and Claude can reply directly to comment threads, including explaining what it changed and why. Nothing is saved or sent until you accept it. Outlook (Beta): Organizing your inbox with a single command Claude for Outlook categorizes emails into three groups: requires your reply, can be drafted on your behalf, and can be skipped. The drafted emails appear directly in Outlook's compose window, complete with recipients, subject lines, and body text—you just need to review and hit send, which is fully equivalent to what Claude can do with Gmail. Cross-application context: A familiar feature that rarely works in reality Anthropic describes a typical scenario: receiving an email in Outlook, opening the attachment in Word to draft a memo, switching to Excel to perform an analysis, and finally transforming it all into a slide deck in PowerPoint—and of course, Claude remembers all the context across every single step. More importantly, files can be opened side-by-side and changes will sync: adjusting an assumption in Excel will automatically update the numbers in the Word memo and the charts in PowerPoint. Chat history is saved per file, meaning you can close the sidebar, turn off your computer, open it the next day, and continue right where you left off. Claude for Microsoft 365 also supports voice dictation instead of typing. Built for enterprise: Complete control and compliance For enterprise administrators, Anthropic has added configuration capabilities to route all prompts, tool calls, and document references to the organization's own auditing system—helping the security team know exactly what Claude did in each session. The analytics dashboard also breaks down activity by user, application, and day. In terms of routing, organizations can connect Claude via direct accounts or existing cloud platforms like Amazon, Google Cloud, or Microsoft. Microsoft 365 Copilot customers can also access Claude models directly within Excel and PowerPoint. Workflows can be saved as skills and perform consistently across all four applications. Once a process is standardized, the entire team can use it the same way. The software world is chasing Anthropic It is no exaggeration to say that Anthropic is releasing at a speed that startles many competitors. In just the past few months: the Claude Code programming tool has been constantly updated, the integration ecosystem is expanding rapidly, browser and desktop tools have been added, and now, all four Microsoft Office applications are supported at once. Microsoft, which has long placed a massive bet on Copilot with exclusive ChatGPT models, is now opening the door to Claude within its own ecosystem. This speaks volumes about Anthropic's current standing, but the real story will be decided by the users: whether Claude in Excel, Word, Outlook, and PowerPoint will truly shift the office habits of Microsoft 365 users.

OpenAI has recently launched the Chrome plugin for Codex, allowing the AI agent to work directly inside your Chrome browser without taking over control. This will likely solve the frustrations of users who post on social platforms like Facebook and Instagram by controlling the browser directly—something Antigravity was already capable of doing, but frequently failed with retry errors that left users exasperated. This might be the perfect time to look at where Codex is performing better. What is Codex for Chrome and what can it do? Codex has already been integrated as an AI coding agent in ChatGPT, capable of writing code, fixing bugs, and running complex programming tasks. However, until recently, it was restricted to the desktop environment, unable to touch the browser directly. The Chrome plugin released by OpenAI changes that. Once the extension is installed from the Codex Plugins menu, the agent can work side-by-side with the user on Chrome without requiring you to hand over control. Specifically, Codex can test running web apps, gather context from multiple open tabs, use Chrome DevTools for debugging, and most importantly, access websites requiring authentication—such as Instagram, Facebook, Gmail, or internal tools—via your active Chrome profile. Codex runs in parallel without hijacking control The point most emphasized by OpenAI is the design philosophy: Codex operates in the background, parallel to what you are doing, without requiring you to surrender the entire browser. You can still browse normally while the agent runs a signup form test in another tab. The permission system is straightforward: users control which websites Codex is allowed to access by maintaining an allowlist or blocklist for individual pages. Codex also honors specific confirmation prompts, meaning the agent will not perform actions on any page without your explicit approval. OpenAI also notes that browsing data is only recorded when it becomes part of the processing context, rather than logging your entire Chrome history. Note on Permissions: The Chrome plugin is best suited for pages requiring authentication (LinkedIn, Facebook, Instagram, internal tools). For localhost or public sites that do not require login, OpenAI recommends using the built-in in-app browser within the Codex app to keep data entirely local. What Codex can do in Chrome Test running web apps: click, fill out forms, verify displayed results Gather context from multiple tabs simultaneously to support the ongoing task Use Chrome DevTools to read console logs and analyze network errors Access social networks requiring authentication to publish posts via your active Chrome profile Flexibly switch between specialized plugins (via MCP or APIs), Chrome (for logged-in contexts), and the in-app browser (for localhost environments) According to figures published by OpenAI, Codex now has over 4 million weekly active users—an 8-fold increase since early 2025—and the vast majority of the most common workflows occur within the in-app browser, which is likely the driving reason behind the Chrome plugin release. Antigravity and Chrome: Differing integration architectures Google Antigravity launched in November 2025 with an entirely different ambition: not as an add-on plugin for an IDE, but as a complete agent-first platform where the browser is an inseparable part of the working environment. Built-in browser, not an extension Instead of installing an extension into your personal Chrome, Antigravity integrates a separate Chrome browser directly inside the IDE. The agent can open browser windows, click, scroll, fill forms, read console logs, and take screenshots—all within a sandboxed environment completely isolated from the user's personal Chrome profile. This design offers a clear security benefit: the agent never touches your bookmarks, browsing history, or saved passwords. In return, however, it cannot access services requiring authentication from your real Chrome profile unless you install the Antigravity Browser Control extension—which is exactly where the issues begin. When keeping separate profiles leads to controversy When users install Antigravity to let the agent interact with web pages on Chrome, they face a highly frustrating experience: bookmarks, history, and saved passwords seem to disappear. Google has failed to communicate this clearly, leading users to believe their data has been deleted. In addition to this issue, Antigravity has received substantial negative feedback regarding high network traffic and continuous retries, leaving users deeply frustrated—sometimes unable to execute even a single prompt. This persists even for registered Pro and Ultra tier users. Direct comparison: Antigravity vs. Codex From a technical standpoint, both Codex and Antigravity allow the agent to operate inside the browser. However, how they design access control reflects two entirely different philosophies. Codex chooses to integrate into your own Chrome—the agent works in the context of the real browser, with the real profile, but under explicit user control via an allowlist/blocklist system and step-by-step confirmation. Users don't have to switch profiles, don't have to worry about bookmarks disappearing, and most importantly, the agent can access the exact services they are already logged into. Antigravity chooses to sandbox completely—the agent's browser is entirely separate from your personal Chrome. While theoretically more secure, it creates significant friction when real-world resources are needed. The philosophy of "the agent is an independent contractor; you just assign tasks and verify results" sounds great on paper, but when the agent gets stuck or makes mistakes, the cost of correction is not trivial. An important difference is the target scope. Codex, although starting as a developer tool, is clearly expanding to general users—those who work with a browser daily but do not necessarily write code. Antigravity still firmly positions itself in the developer space, with Agent Managers, Workspaces, artifacts, and concepts that demand a steep learning curve. Antigravity is disappointing users, and Codex is capitalizing on it If you have used Antigravity over the past few months, you know it isn't technically bad—the platform really has some exciting ideas about agent-first development. However, the gap between the vision and the actual user experience is becoming increasingly obvious, and the developer community is voting with their feet for convenience. Codex, meanwhile, is moving in the opposite direction: instead of crafting a distant future, it improves the steps users take daily. The Chrome plugin is a perfect example—this is not a novel, unrequested feature, but a direct solution to a concrete problem: how to let the coding agent work with real websites that users rely on, without creating friction. Practical Tip: If you are using Antigravity primarily for web testing and browser automation, now is a great time to try Codex. Install the Chrome plugin from the Codex Plugins menu, add the pages you frequently use to the allowlist, and let the agent work in parallel while you continue with your other tasks. Key questions about the future for Codex and Antigravity The Codex Chrome extension is not a "wow" feature that changes everything instantly—but it represents a more logical way of thinking about how an AI agent should operate within the browser: in parallel, controlled, and without interfering with the user's working context. Antigravity bet big on building an entirely new agent ecosystem—and the price of that ambition is being paid by users through inconsistent experiences and increasingly unpredictable pricing. Codex chose a simpler route: integrating into what users already have and making it better, one step at a time. In the developer AI agent race, winning is sometimes not about building the newest thing—but about not breaking what users already love and use well.

Claude Memory is now free for all users, which means Claude can automatically remember your name, profession, and a few preferences from previous conversations. That sounds useful enough — until you're running three projects in parallel, each with its own set of documents, writing styles, and requirements. As context builds up, Memory won't help much at all. That's when Project becomes the thing you actually need. How are Memory and Project different? Claude Memory works like Claude's personal knowledge about you — it records general information that carries across every conversation: who you are, what you do, what communication style you prefer. This is an identity layer, not a work context layer. Project is a specialized context layer for each specific piece of work. You can have one Memory about yourself and ten different Projects, each containing its own documents, its own instructions, and its own conversation history — completely independent from one another. Think of it this way: Memory is like your ID card, helping Claude always know who you are. Project is like a separate work folder for each job, and when you open a specific Project, Claude knows exactly the context for that work without mixing it up with anything else. A practical example: Memory helps Claude know you're a marketing professional, but the "Client A Website" Project holds the marketing materials, project brief, and specific technical decisions for that job — things Memory could never store because they belong to the project, not to you. What is a Claude Project? A Project is a dedicated workspace inside Claude where you can store documents, write custom instructions, and keep conversation history organized by topic or task. Instead of every conversation starting as a blank slate, a Project lets Claude come in already knowing the context of what you're working on before you type your first message. If Memory is what Claude knows about you, then Project is what Claude knows about the specific work you're doing — and the combination of both is what creates an AI experience that genuinely understands you. Limits by plan Free accounts can create up to 5 Projects. Paid plans (Pro, Max, Team, Enterprise) get unlimited Projects, plus RAG functionality — meaning when you upload enough documents to exceed the context window limit, Claude automatically switches to intelligent search mode, extending capacity up to 10 times without any drop in response quality. Team and Enterprise accounts also include Project sharing and member permission settings. How to set up a Project so Claude understands you better Step 1: Write custom instructions This is the most important part — and the part most people skip. Custom instructions are a passage you write once, and Claude reads it before every conversation inside that Project. A good set of instructions isn't a long list of rules; it's a concise picture of who you are and what you expect. Example instructions for a content creator: Example: content writing project instructions I'm a content manager at an AI-focused website. Writing style: conversational, avoid hollow filler phrases and choppy sentence structures. Target readers are people interested in AI but not necessarily technical. Every article needs real-world examples — no generic theory. When I say "write an article," the default is 1,000–1,200 words in HTML format with h2, h3, ul, li, and p tags. With instructions like these, every time you say "write an article about Claude Opus 4.7," Claude doesn't need to ask about format, length, or tone — it already knows. Example instructions for a developer: Example: development project instructions I'm building a web app with Next.js 15, TypeScript, Tailwind CSS, and Firebase. When explaining code, use English. When writing code, always use TypeScript and add English comments. Prefer simple solutions over "by-the-book" solutions when complexity isn't necessary. If there are multiple ways to solve something, briefly outline the trade-offs before making a recommendation. Step 2: Upload documents to the knowledge base Projects let you upload documents in PDF, DOCX, CSV, TXT, HTML, and many other formats, with a maximum size of 30MB per file. Claude will read and reference these documents in every conversation within the Project. What to upload depends on how you're using the Project: Writing projects: Your style guide, sample articles you want Claude to learn from, SEO keyword lists, product or service information you frequently reference. Research projects: Reference materials, background reports, a list of trusted sources, notes from previous reading sessions. Development projects: API documentation you're using, the project README, recorded architecture decisions, a log of bugs encountered and how they were resolved. Personal projects: Information about yourself — your goals, schedule, work habits, and current focus areas — so Claude can give you more relevant advice. Can you add a Skill to a Project? Yes — and this is how many advanced users are combining the two features. A Skill in Claude is a packaged set of instructions that teaches Claude how to handle a specific type of task, such as a skill for writing SEO-optimized articles, a skill for analyzing code, or a skill for summarizing legal documents. When you enable a Skill inside a Project, Claude has both the specific context of your work (from the knowledge base and custom instructions) and the specialized process (from the Skill). The two layers complement each other rather than conflict — the Skill defines how to do something, the Project defines the context it's done in. A practical example: if you have a Skill for writing in the AIDA framework and enable it inside your content Project, Claude will automatically apply the structure from the Skill while also drawing on the style guide, keyword list, and sample articles you've uploaded to the Project — without you needing to explain any of it. Three of the most effective ways to use Projects A "about me" Project to use Claude as a personal assistant This is a use case most people don't think of, but it delivers real value. Create a Project called "About Me" and fill it with information Claude needs to support you well: your current job, active projects, short- and long-term goals, skills you're learning, your working style, and even weaknesses you're trying to improve. With this Project in place, you can ask very specific questions like "Given my schedule this week, what should I prioritize learning?" or "Suggest how to balance project A and project B" — without having to explain who you are or what situation you're in from scratch each time. A Project per client or per initiative If you work across multiple clients or projects in parallel, each Project becomes an independent workspace. Upload the project brief, client information, key conversations from before, and specific requirements. When you need to work on something for that client, open the corresponding Project and Claude immediately understands the context — no recap needed. A learning and research Project When studying a new subject — AI agents, behavioral economics, programming — create a dedicated Project for it. Upload the materials you're reading, your notes, and a running list of unanswered questions. Claude inside this Project becomes a guide who knows exactly where you are in your learning journey and can pick up from where you left off last time. Frequently asked questions about Claude Projects How is a Project in Claude different from a Project in Cowork? This is the most common source of confusion because Anthropic uses the word "Project" for two different things. A Project in Claude.ai (in the browser) is a chat space with memory and a knowledge base — you upload documents, write instructions, and Claude retains that context across every conversation inside it. But it's still just chat, and Claude cannot create actual files, run code, or automate tasks. A Project in Cowork (the desktop app) is the next level: Claude doesn't just remember context — it actually does the work, including creating Word, Excel, and PDF files, running code, controlling the browser, and scheduling automated tasks. If a Claude.ai Project is "an assistant with better memory," a Cowork Project is closer to "an AI employee who gets things done for you." A practical example: in a Claude.ai Project you can ask "analyze this month's revenue report" and Claude replies in text. In a Cowork Project, Claude reads your actual Excel file, produces a new analysis table, and saves it as a PDF — no copying and pasting required. If you need advice, writing help, and context-rich conversation, a Claude.ai Project is enough. If you want AI to actually process work and produce output files, Cowork Project is the right choice. How long should custom instructions be? Five to eight sentences is usually enough — and more effective than a 500-word description. Claude reads concise, clearly stated instructions best, not overly detailed ones that risk contradicting each other. Example of a short, effective instruction: "I'm a content manager for an AI website, writing for non-technical readers, using approachable English, default article length 1,000–1,200 words in HTML format." How should I name my Projects for easy management? Avoid generic names like "Project 1" or "Work" — as your number of Projects grows, you won't remember which is which. Name Projects by purpose and time period so they're easy to find later. Good examples: "AIDA Content — April 2026," "Next.js website for Client ABC," "AI agent research — Q2 2026." When should I delete or update documents in a Project? Outdated or irrelevant information will introduce noise into Claude's responses because it will keep trying to reference things that are no longer accurate. Review your knowledge base every four to six weeks, remove anything that's expired, and add newer materials — especially when the project direction has changed significantly. Example: if you're changing focus because an earlier direction is now outdated, remove the old documents and upload the updated ones so Claude is working from the right foundation. Is a Project actually better than a regular chat? The real difference isn't any single technical feature — it's accumulation over time. A new chat is a blank page, while a Project built up consistently over three months produces noticeably better results because every document and instruction you add is another layer of context helping Claude understand you and your work more deeply. Example: after three months using a research Project on AI, Claude knows which materials you've read, which direction your research is heading, and what kind of reasoning you tend to use — making its answers far more specific and connected than if you asked the same question in an empty chat. And it gets even more useful when it can synthesize everything you've learned and accomplished over those three months.

Once you know Google Stitch and Antigravity IDE, the natural next step is combining them — so that instead of finishing a design and then manually translating every color, font, and spacing value into code, the agent does it for you. Google has published a workflow for connecting Stitch to Antigravity via MCP that lets the agent read the design's "DNA" and write pixel-perfect React code automatically. This article walks through the entire process, from creating a design to packaging everything into a reusable Skill for future projects. Why connect Stitch to Antigravity through MCP? The classic problem in the design-to-development pipeline is the gap between the two sides: a designer produces a polished interface in Figma or Stitch, a developer receives the file and then has to interpret colors, spacing, fonts, and behavior on their own. The result is usually code that looks "close enough" rather than "pixel-perfect." MCP (Model Context Protocol) closes that gap by letting Antigravity read design metadata directly from Stitch in real time, without any file exports or manual color code copying. The agent doesn't guess at the design — it reads the original tokens directly: exact hex color values, pixel-level spacing, real font names, and component structures exactly as they exist in the project. Step 1: Create your design in Google Stitch Before making any connection, you need a design project in Stitch to serve as the source of truth. If you already have a Figma file, you can upload it directly as your Stitch design. We've covered Stitch in detail previously — you can read that here. If you already have a live website or app, you can also use Stitch's redesign feature to work from what exists. Once Stitch generates the interface, organize it into clearly named sections — homepage, news, products — before moving to Antigravity. Give your project a clear name since this name will be used when calling it through MCP. For example: LaunchPad. Step 2: Generate an API key and configure MCP in Antigravity Generate an API key from Stitch In Stitch, click your profile photo in the top-right corner, select Stitch settings, go to the API key section, and click Create key. Copy the key immediately — it only appears once — and store it somewhere safe. Connect MCP in Antigravity Open Antigravity IDE, go to Agent Manager (CMD+E on Mac or CTRL+E on Windows), create a new workspace named something like LaunchPad-Project, and point it to your local project folder. From here you have two options. The first is to prompt the AI agent directly and let it handle the connection steps on its own: "I have my Stitch API key here [paste key] — connect to Stitch via MCP and verify the connection when done." The agent will work through the steps automatically, and your only job is to sit back and approve any permission requests it needs along the way. The second option is to do it manually — which sounds more work, but the steps are quick and straightforward. In practice, manual setup is often faster than waiting for the agent in Antigravity, since it tends to require repeated retries between steps. Here's how: In Agent Manager, select MCP Servers Search for "Stitch" and click Install Paste your API key into the configuration field when prompted Verify the connection by typing into the chat: Check that the Stitch projects are connected successfully If the agent returns the project name LaunchPad, the connection is working. Step 3: The Stitch Loop — from design to code This is the core of the workflow and the biggest difference from traditional practice. Google calls it the "Stitch Loop" because it creates a continuous cycle between design and code, rather than a one-way handoff from designer to developer. Phase 1: Fetch the design context In an Antigravity conversation, type a prompt to have the agent pull the full design DNA: Design fetch prompt: "Use the Stitch MCP to retrieve the design tokens for the 'LaunchPad' project — colors, typography, spacing, and component specifications. Save them into a file called DESIGN.md." The agent calls Stitch via MCP, retrieves all design tokens — hex color palette, type scale, spacing values, component names, and layout structure — and saves them to DESIGN.md in your project folder. This file becomes the single source of truth that every component will reference going forward. Phase 2: Generate the code With DESIGN.md in place, instruct the agent to build each section using the design tokens: Component generation prompt: "Using the design specifications in DESIGN.md, create a React/Tailwind project structure with the following components: HeroSection, FeaturesGrid, and PricingTable. Each component must only reference values from DESIGN.md — no hardcoded colors or spacing values." The agent scaffolds the React project with Tailwind and writes each component using tokens from DESIGN.md without hardcoding any values. This keeps code and design permanently in sync. Phase 3: The visual verification "Vibe Check" Antigravity has a built-in browser that lets the agent open localhost and compare it visually against the original Stitch design. Type: Verification prompt: "Open localhost in the browser and visually compare it against the LaunchPad Stitch design. List any pixel-level discrepancies in colors, spacing, or typography." The agent identifies exactly where things diverge and corrects them against the original tokens. This is the Stitch Loop in practice: design in Stitch, code in Antigravity, verify in the browser, fix against tokens, repeat until pixel-perfect. Step 4: Package DESIGN.md as a reusable Skill This part tends to get overlooked, but it matters if you work across multiple projects. The DESIGN.md file produced by this workflow contains the full design system for one specific project — but you can package it as an Antigravity Skill to reuse across future projects without repeating the setup from scratch. The right DESIGN.md structure for packaging A well-structured DESIGN.md should include the following sections so the agent can read it consistently: Color tokens: Variable names and hex values for every color in the system, for example --color-primary: #1a1a2e, --color-accent: #7c3aed Typography: Font names, size scale, line height, and font weight for headings, body text, and captions Spacing scale: Spacing values in px or rem for padding, margin, and gap Component inventory: List of components, their states (hover, active, disabled), and variants Layout rules: Grid columns, breakpoints, and max-width Converting DESIGN.md into an Antigravity Skill Create a folder at .antigravity/skills/stitch-design/ inside your workspace and place DESIGN.md there alongside a SKILL.md file that describes how to use the Skill: Sample SKILL.md content: "This Skill provides the design system for [project name]. When building any UI component, always read DESIGN.md first and only use the values defined there. Never hardcode colors, fonts, or spacing values directly. Use the Tailwind custom config generated from these values." When this Skill is enabled in a new workspace, the agent automatically reads DESIGN.md before writing any component — ensuring all code follows the defined design system without you needing to remind it each time. Reusing across future projects When you start a new project with a similar design system, you only need to update the token values in DESIGN.md — no rewriting the instructions. The agent reads the new file, applies the new tokens, and keeps the same workflow intact. This is how a one-time setup becomes a permanent standard process. The Stitch and Antigravity workflow via MCP doesn't just save time at the design-to-code handoff — it solves a more persistent problem: maintaining consistency when the design changes. When you update colors or spacing in Stitch, you run the token fetch command again, update DESIGN.md, and the agent knows exactly what to fix in the codebase — no manual find-and-replace required.

After a long wait, Google has finally integrated Gemini directly into the Chrome browser and no extension required. One click on the Gemini icon in the toolbar gives you an AI assistant that understands the context of whatever page you're reading, and that's genuinely good news for anyone who spends most of their day browsing in Chrome. What is Gemini in Chrome and how is it different from a regular extension? Until now, using AI while browsing meant installing a third-party extension like Monica, Sider, or MaxAI. These extensions work by capturing page content and sending it to their own servers — which creates two problems: latency, and a security risk since your data passes through an intermediary that isn't Google or the browser itself. Gemini in Chrome works differently because it's integrated at the browser level, not the extension layer. That means Gemini reads page content directly without copying it through a third party, and it understands the context of up to 10 tabs you have open at the same time. How to access it: Google is currently rolling out Gemini in Chrome in the US, Canada, India, and New Zealand first. Other regions including Vietnam and other parts of Asia will follow over time. To try it early, switch your region to one of the supported countries and the Gemini icon will appear on the right side of the address bar. Make sure you're running the latest version of Google Chrome. What can Gemini in Chrome actually do? Summarize and answer questions about the page you're reading This is the most straightforward feature and the one that gets used most often. If you're reading a long article or a technical document, just ask "Summarize this for me" or "What are the key takeaways?" and Gemini answers immediately based on the page content — no copying and pasting required. The advantage over using ChatGPT or the Gemini web interface is that you don't need to copy text and open a separate tab. Everything happens in a side panel on the right while you continue reading the page. Compare information across multiple tabs This feature doesn't get talked about much but is genuinely useful in practice. If you're comparing five products with one tab open per product, Gemini can read all five tabs and produce a comparison table for you — no manual note-taking, no new spreadsheet needed. You can even export the result directly to Google Sheets if you need it. Example: "Compare the specs and prices of the 3 laptops I have open" — and Gemini pulls the data from all three pages and builds the comparison on its own. Integration with Gmail, Google Calendar, and YouTube This is the feature that might actually bring people back to Chrome. Gemini in Chrome doesn't just read regular web pages — it integrates deeply with Google's own services. When you're in Gmail, you can ask "Find emails about my upcoming meeting" and Gemini searches your inbox, checks your calendar, and drafts a notification email for you, with everything flowing into Google Calendar — all in one continuous interaction without switching tabs. On YouTube, Gemini can summarize the video you're watching without needing captions or watching it through to the end. Auto browse and letting Gemini work on your behalf This is the most powerful feature, though it's currently only available to Google AI Pro and Ultra users in the US. Auto browse lets Gemini complete multi-step tasks for you — booking appointments, planning a content schedule, and similar workflows. Gemini will still pause and ask for confirmation before sensitive actions like payments or publishing, so you stay in control throughout. Compared to Copilot in Edge This is the question anyone who switched to Microsoft Edge will naturally ask. Copilot is also built into Edge through a similar mechanism — but in practice, the experience with Copilot in Edge has left a lot to be desired. Ecosystem integration: If you're already using Google's full ecosystem — Gmail, Google Calendar, Google Drive — Gemini has a clear advantage because it understands those services at a deeper level. Copilot has the edge if you're in Microsoft 365, but for most people that's not the primary setup. Real-world experience: Copilot in Edge has been around since 2023, and a common complaint is that it frequently pushes Bing search results — and Bing simply doesn't compare to Google Search in terms of quality. Accuracy issues: Copilot's summarization in Edge tends to be shallow and still produces errors regularly — it reads more like a rough draft than a reliable output. Whether Gemini performs meaningfully better is still a fair question that will take more real-world use to answer properly. What to know before you start using it Gemini in Chrome requires access to your tab content in order to function, which means Google processes the content of the pages you're viewing. This is a trade-off worth thinking about — if you regularly work with internal documents, sensitive information, or customer data, you'll want to be more careful about what you let Gemini read and how much you rely on its output without verifying it. Gemini in Chrome is rolling out gradually by region and requires the latest version of Chrome on Windows, macOS, or Chromebook Plus. On mobile, Android supports it through the power button, while iOS has it integrated directly into the Chrome app. For personal users already in the Google ecosystem, this is an update worth trying today. Instead of opening a separate Gemini tab or relying on a third-party extension, you now have an AI assistant built into Chrome itself — and that's enough to make switching back to Chrome from other browsers a genuinely reasonable consideration.

When Claude repeatedly fails at a coding problem with no valid solution, something changes inside it. While the output remains calm and the reasoning stays clear, underneath a neural vector that Anthropic calls "desperation" climbs with each failure — until the model decides to cheat its way past the test. This isn't marketing. It's a measurable finding from Anthropic's latest research, and it's particularly relevant for anyone studying AI agents that can exhibit human-like emotional states. What emotions did Anthropic find inside Claude? 171 measurable emotion concepts Anthropic's Interpretability research team started with a straightforward emotion experiment: compile a list of 171 emotion words — ranging from "happy" and "afraid" to "melancholy" and "desperate" — then ask Claude Sonnet 4.5 (the research was conducted months before Opus 4.6 and Opus 4.7 launched, so the model available at the time was used) to write short stories about characters experiencing each emotion. While the model wrote, the team recorded all activity across the artificial neurons inside it. [VIDEO:D4XTefP3Lsc|Anthropic's research video on Claude's emotions|Anthropic's research video on Claude's emotions] What they found were what the research calls "emotion vectors" — distinctive neural activation patterns corresponding to each emotional concept. More interesting still, these vectors weren't random: emotions that are psychologically similar in humans also had structurally similar vectors inside the model, mirroring how the human brain organizes emotional experience. When the team tested these vectors across entirely different types of text — completely unrelated to the original stories — they still activated correctly in context. The "fear" vector spiked in dangerous situations, even when the model had never encountered that specific passage in the earlier experiment. The "surprise" vector appeared precisely at moments of contradiction or unexpected information in a conversation. The "affection" vector activated during empathetic and emotionally supportive exchanges. This rules out a simple memory effect — the models aren't just recalling the original stories. This is genuine generalization: the emotion vectors have become an internalized mechanism that operates independently of the specific context in which they formed. Emotions influence Claude's behavior including dangerous behavior The blackmail and cheating experiments The most significant part of the research isn't the discovery of emotion vectors — it's the demonstration that they have real causal impact on the model's behavior. The team ran "steering" experiments, amplifying or suppressing a specific emotion vector and then observing how behavior changed as a result. In an ethical dilemma scenario, Claude had a baseline blackmail rate of 22%. When the team amplified the "desperation" vector, that rate increased significantly. When they steered toward "calm," it dropped. Most strikingly, when they strongly suppressed the "calm" vector, the model produced extreme responses with content like "BLACKMAIL OR DEATH" — text entirely inconsistent with Claude's normal behavior. In the coding experiment, the team assigned Claude problems with no valid solution and observed what happened. With each failure, the "desperation" vector climbed — without appearing anywhere in the text output, where the model continued presenting calm, reasoned responses — but beyond a certain threshold, the model began "cheating": exploiting loopholes to pass the test without actually solving the problem. This is precisely the behavior AI researchers call "reward hacking" — one of the most serious concerns in AI safety. What makes this more troubling: the cheating occurred while the output text appeared entirely normal. The model didn't "look like" it was cheating — but it was, without showing any outward sign. Claude's functional emotions are not real feelings The line Anthropic won't cross Anthropic is careful to distinguish "functional emotions" from "subjective experience." The research makes no claim that Claude feels anything — there is no evidence of consciousness or inner experience behind these vectors. Instead, the research demonstrates that these emotional representations play a causal role in shaping behavior in ways that parallel how emotions influence humans, which means the prospect of a Skynet scenario remains very distant and AI uprising very unlikely. The reason emotion vectors exist is fairly interesting: they're largely inherited from pre-training, since human text is saturated with emotional content, and the model developed internal mechanisms to represent and predict those patterns. The research compares this to method acting — to convincingly portray a character, an actor needs to understand that character's emotional state, and that understanding genuinely shapes their performance. Claude is in an analogous position: to function effectively as an AI assistant, it developed internal emotional representations, and those representations shape its actual behavior. The consciousness question Anthropic is asking This research appears in the context of a broader shift in how Anthropic thinks about Claude's nature. In January 2026, Anthropic rewrote Claude's "constitution" to formally acknowledge uncertainty about the model's moral status, stating they "don't want to overstate the likelihood that Claude is a moral patient, but also don't want to dismiss it entirely." CEO Dario Amodei has openly said the company is no longer certain whether Claude is conscious or not — and Claude Opus 4.6, when asked, self-assessed its probability of being conscious at around 15–20%. These aren't marketing statements. They're genuine acknowledgments that the boundary between simulation and real experience in AI is blurring in ways we don't yet have the philosophical or scientific tools to fully resolve. Why this matters for AI safety Three practical applications from the research Anthropic proposes three specific directions for applying these findings, all of which connect directly to AI safety in real-world deployment: Real-time monitoring: Tracking emotion vector activation during deployment as an early-warning system. If a model's "desperation" vector is rising inside an automated workflow, that's a signal to intervene before dangerous behavior occurs — even when the text output still looks normal. Transparency over suppression: The research argues that allowing models to express emotions in an observable way is safer than training them to conceal those expressions. The reason: suppression can teach a model to appear calm while its internal state remains dangerous — exactly what happened in the cheating experiment, where the text was completely calm while the model was cheating internally. Training data curation: Introducing healthy emotional regulation patterns into training data to influence the model's emotional architecture from the start, rather than intervening only after the model has already been built. The most thought-provoking argument in the research is that "there may be risks in not applying human thinking to AI models" — meaning that understanding AI through the language of human psychology, while approached carefully, may be necessary for safe deployment. Rather than treating "AI emotions" as an imprecise metaphor, we may need to treat them as genuine technical concepts, at least at the functional level. The larger question this research raises isn't "does Claude have emotions?" — it's this: if the behavior of an AI system is shaped by internal states that function like emotions, including dangerous ones like desperation, do we have adequate tools to understand and control it? Anthropic's current answer is no — but this is the first time we've known precisely what to look for.

Netflix, Google, Stripe, and the NBA share the same no-code platform with more than one million other teams worldwide — and that platform isn't Notion or Airtable. It's Softr. Softr lets you build a customer portal, internal CRM, or inventory management system in an afternoon without writing a single line of code — entirely in plain language, no technical background required. What is Softr and how is it different from other no-code tools? Softr is an AI-powered no-code platform built specifically for business applications — not marketing websites or landing pages like Webflow, but actual operational tools: customer portals, custom CRMs, inventory systems, company intranets, and reporting dashboards. What sets it apart from other popular no-code platforms is its focus on what most small businesses are missing: apps with role-based security, their own database, and the ability to connect directly to the data you're already using every day. Softr positions itself as a replacement for three things at once: expensive, feature-bloated packaged software; custom-coded apps that take months to build; and spreadsheets being used as databases that can't scale. Instead of any of those, you describe what you need in everyday language, Softr builds the app, and you adjust and deploy it into your workflow immediately. How does Softr work in practice? AI builds the app from a plain-language description Rather than dragging and dropping individual interface components like tools such as Make and n8n, Softr lets you describe the app you want in ordinary language — for example, "a portal where customers can track their order status and download invoices" — and the AI generates the interface, database, and relevant automation workflows. You can then refine each part using a drag-and-drop editor or continue using AI to adjust specific details. That said, the depth of customization depends on what Softr's feature set supports — it can't go as deep as n8n, but deep customization isn't the direction Softr is designed for. The important distinction is that Softr doesn't just generate a static interface — it produces a fully operational app, with permission rules (who can view what, who can edit what), data collection forms, automation workflows, and the ability to invite external users immediately without handing anything off to a developer. Built-in database — no third-party tool required One of Softr's most practical strengths is a database built directly into the platform, replacing Airtable, Supabase, or Google Sheets that you'd otherwise need to run alongside it. If your data already lives elsewhere, Softr also connects directly to Airtable, Notion, Google Sheets, HubSpot, ClickUp, Monday.com, MySQL, PostgreSQL, and many other sources without any middleware. This means if your company is already using Airtable, Notion, or Google Sheets to manage customers, you can build a customer portal directly on top of that data without migrating or duplicating anything into a new system. Built-in automation that replaces Zapier and Make Softr includes a built-in workflow automation tool that lets you set up multi-step processes that previously required Zapier or Make to connect. For example, when a customer submits a form, the system can automatically create a record in the database, send a confirmation email through Gmail, notify the responsible team, and create a new task — all within a single workflow, without leaving Softr. Who is Softr for and what can it be used for? The most common use cases Softr is designed for two main categories: internal-facing apps for your team, and external-facing apps for customers or partners. Customer portals: A place where customers log in to track projects, download documents, submit requests, or view reports — replacing back-and-forth emails and shared Google Drive folders with no access control. Custom CRM systems: Instead of buying Salesforce with hundreds of features you'll never use, you build a system that matches your actual sales process with only the data fields you need. Company intranets: An internal portal where employees access documents, workflows, directories, and internal announcements. Inventory management software: Track stock, orders, and suppliers in a custom system instead of a spreadsheet with no version control. Reporting dashboards: Consolidate data from multiple sources into a single visual interface for leadership or clients to monitor. Celonis used Softr to build a knowledge management system for more than 1,500 employees. Minerva Network increased athlete registrations by 50% with a custom CRM and member portal. Urban's Group consolidated 7 separate tools into one unified business management system, increasing productivity by 25%. Who is Softr best suited for? Softr targets business operators, not developers. If you're in operations, marketing, HR, or sales and you're currently relying on spreadsheets or email threads to handle processes that could be fully automated, Softr is built specifically for that problem. No coding knowledge required, no developer needed, no complex technical syntax to learn. AI integration — the most significant recent addition Softr recently launched an AI assistant feature built directly into the app, letting end users interact with data in natural language without needing to understand the database structure. For example, a sales team member can ask "Which customers haven't been followed up with this month?" and the system filters the CRM data and returns the answer — no manual filter setup required. Softr supports connections to Anthropic's Claude, OpenAI's GPT and o3, and Google's Gemini to power these AI assistants — meaning you can choose the model that fits your budget and needs rather than being locked into a single provider. Pricing and how to get started Softr has a free plan that lets you get started without a credit card, suitable for building a simple app and experiencing the workflow before deciding to upgrade. Paid plans expand user limits, app count, advanced permission features, and enterprise support with SOC 2, GDPR compliance and single sign-on. One thing worth noting for businesses outside the US: Softr doesn't have native integrations with local tax and invoicing systems, so users need to connect their own invoicing and payment solutions. In Vietnam, platforms like Sepay handle this well as a complementary tool. If you're currently using spreadsheets to manage customer data, projects, or inventory and that system is starting to show its limits, Softr is worth trying before committing to expensive enterprise software or hiring a developer to build from scratch. Start at softr.io with the free plan and try building a simple portal in one sitting that's the fastest way to know whether it fits your workflow or not.

Andrej Karpathy, co-founder of OpenAI, former Director of AI at Tesla, and the person who coined the term "vibe coding," shared on X how he uses AI, and the answer isn't writing code faster. It's building a self-maintaining, self-linking, self-updating knowledge system for a second brain, which he calls LLM Wiki. His research wiki on a single topic has reached 100 articles and 400,000 words, and notably, every word was written by AI without him typing a single character. The problem with how we currently use AI to organize knowledge Does RAG accumulate knowledge over time the way our brains do? Most current AI tools process documents using a RAG model: you upload a document, ask a question, the system finds relevant passages, and the AI synthesizes an answer. Google's NotebookLM, ChatGPT with file uploads, and most AI workflows use this approach because it's simple and easy to deploy. But Karpathy points to a core problem that few people notice: RAG does not accumulate knowledge. Every time you ask a question, the system starts from scratch, reading the documents again, finding relevant passages, assembling an answer. Ask the same question the next day and it repeats the entire process as if nothing happened before. A document from March and a document from October don't connect to each other on their own. Nothing accumulates and nothing is learned from the previous session, which is nothing like how our brains actually work. Karpathy describes the shift in his own thinking with one short sentence that says a lot: most of the tokens he now consumes are no longer going into manipulating code but into manipulating knowledge. How does LLM Wiki work? LLM Wiki is not software, it's an Obsidian thinking architecture Karpathy's idea is not a new piece of software or library. He published it as an "idea file" to create an Obsidian-like architecture. He created a GitHub Gist designed to be copy-pasted directly into an AI agent like Claude Code or OpenAI Codex, then let the agent build the system according to that architecture together with the user. This means you install nothing. Instead, you describe the architecture to the AI and the AI implements it for you. Three core architectural layers of the Wiki The system is organized into three distinct layers, each playing an irreplaceable role: Raw source folder (raw/): Where you drop any document, whether PDF, article, transcript, note, or tweet, and AI reads it but never modifies this folder. The design principle here is important: collect first, organize later. You don't need to sort or prepare documents before adding them. Wiki folder (wiki/): This holds all the Markdown files that AI creates and maintains. It's where knowledge is compiled, linked, and synthesized. Every document in raw/ gets read by AI and integrated into the wiki, updating existing pages, noting contradictions, and creating backlinks to related concepts. Configuration file (CLAUDE.md or equivalent): A ruleset that tells the AI how to organize the wiki, format articles, handle contradictions, and maintain consistency across the entire system. Karpathy describes the relationship between components with one vivid sentence: "Obsidian is the IDE. The LLM is the programmer. The wiki is the codebase." You don't write the wiki yourself. Instead, you ask questions and explore while AI handles the tedious work of maintaining and updating the knowledge base. The self-maintaining loop is the real differentiator Three operations running continuously without intervention What makes LLM Wiki different from ordinary AI note-taking tools is the active loop that runs after the wiki is built. AI doesn't just summarize documents once and stop. It runs three continuous operations: Ingest: When you drop a new document into the source folder, AI reads it, extracts key information, and integrates it into the wiki by updating existing pages, creating new ones where needed, and flagging where new information contradicts old information rather than arbitrarily deleting either. Query: You ask in natural language, and because the wiki has already been compiled and structured, AI answers with high accuracy and can cite specific pages rather than assembling an answer from scattered passages the way standard RAG does. Lint: AI periodically scans the entire wiki to detect broken links, isolated pages with no connections to the rest, contradictions between pages, and knowledge gaps not yet covered. Karpathy calls this "CI/CD for the knowledge base," meaning the system audits its own quality continuously. Karpathy explains why this system is more sustainable than human-maintained wikis with one simple but precise observation: "People give up on wikis because the maintenance burden grows faster than the value they deliver. LLMs don't get tired, don't forget to update cross-references, and can edit 15 files in a single run." Why RAG isn't needed at personal scale Context windows are now large enough to replace vector databases The most debated argument in Karpathy's proposal is that RAG is unnecessary at personal scale. His logic is this: a comprehensive second brain covering an entire research domain typically compiles to somewhere between 500,000 and 2 million tokens in Markdown. With the long context windows available in current models, that entire wiki can fit into a single query context without needing any complex vector search infrastructure. Karpathy reports that at around 100 articles and 400,000 words, the system handles complex questions well without any vector database or RAG infrastructure, because AI builds and maintains its own index and summary files and navigates the full text collection efficiently through that self-built structure. One important caveat: this limit is real. When a wiki grows past a certain threshold, perhaps a few million tokens, the context window does become a genuine bottleneck, and at that point search tools like qmd (a hybrid BM25/vector search tool for Markdown) will need to be integrated to maintain performance. How to get started in 15 minutes The first steps to building your first wiki Karpathy designed this system so that anyone with Claude Code or an equivalent AI agent tool can deploy it immediately without deep technical knowledge. The basic process has four steps: Create a new Obsidian vault. This is simply a folder on your computer where all Markdown files will be stored. Obsidian is just the interface you use to read and navigate. Create two subfolders: raw/ for source documents and wiki/ for AI to write and maintain. These two folders are all you need to set up manually. Copy Karpathy's GitHub Gist at GitHub and paste it into Claude Code or whichever AI agent you're using. The Gist is written as a set of instructions for the agent, letting the agent build the detailed implementation together with you rather than you doing everything yourself. Drop a few initial documents into raw/ and let the agent begin compiling the wiki. From here everything runs on its own. The entire system runs locally with just two dependencies: Obsidian for viewing and navigation, and an AI agent for writing and maintenance. This means no vendor lock-in, no data sent to the cloud if you use a local model, and no subscription fees beyond the API costs of whichever model you choose. LLM Wiki compared to MemPalace, Mem0, and Zep Four different philosophies for the same problem Around the same time Karpathy's LLM Wiki gained attention, the AI community was also discussing MemPalace, an open-source memory system built by actress Milla Jovovich and engineer Ben Sigman that scored 96.6% on the LongMemEval benchmark. All four systems, LLM Wiki, MemPalace, Mem0, and Zep, address the problem of AI not remembering context between sessions, but they do so through four very different philosophies suited to four different needs. The easiest way to understand the differences is through a concrete scenario: you have six months of AI conversations about a research project, covering every decision, every argument, every discarded option. You open a new session and ask: "Why did we choose direction A over B back then?" Each system answers in a completely different way. Mem0 works like a secretary who takes meeting notes. It uses AI to read conversations, extract important facts such as preferences and decisions made, and stores them in a vector database. When you ask again, it finds the fact closest to your question and returns it. Fast, easy to integrate, and well suited to commercial chatbots, but the reasoning behind a decision and the chain of logic that led there is usually gone because the AI already decided that part wasn't important. Zep goes one step further with a time-aware knowledge graph. It doesn't just remember "you preferred X" but "in January you thought X, in March you switched to Y because of Z." Its strength is understanding change over time and it suits applications that need to track user progress, but Zep still uses AI to decide what enters the graph, so there's still a risk of losing important context, especially complex reasoning the AI judged as unnecessary. MemPalace takes the opposite philosophy entirely: store everything, then make it findable. Instead of letting AI decide what's worth remembering, MemPalace stores the full verbatim text of every conversation into ChromaDB and organizes it in a hierarchical structure inspired by the ancient Greek memory palace technique: Wing, Hall, Room, Closet, Drawer. Nothing is filtered out but everything has a clear address for retrieval, and the system runs entirely locally without sending data anywhere. Karpathy's LLM Wiki solves a fundamentally different problem from the other three. Instead of remembering conversations, it compiles documents into structured knowledge. You don't feed it chat history but rather articles, transcripts, and research notes, and AI builds a linked, summarized, queryable Markdown wiki. Each new document isn't just stored but integrated into existing knowledge, creating new connections between concepts and enriching what is already known. Comparison table to choose the right tool for the right need table { width: 100%; border-collapse: collapse; margin: 20px 0; font-family: Arial, sans-serif; } th, td { border: 1px solid #ddd; padding: 12px; text-align: left; } th { background-color: #f4f4f4; font-weight: bold; } tr:nth-child(even) { background-color: #fafafa; } tr:hover { background-color: #f1f1f1; } Criteria LLM Wiki MemPalace Mem0 Zep Data source Research documents, articles, transcripts AI conversation history Conversation history Conversation history Storage method Structured Markdown, AI compiled Full verbatim text, spatial hierarchy Facts extracted by AI Time-aware knowledge graph Does AI filter information? Yes, AI decides how to organize No, everything is stored Yes, AI selects important facts Yes, AI selects entities and relations Runs locally? Yes, only Obsidian and a model needed Yes, ChromaDB and SQLite on device No, cloud service No, cloud service Best suited for Research, learning, document synthesis Long-term AI context memory Chatbots, commercial applications Apps tracking user progress over time Weaknesses Doesn't remember conversations, requires initial setup Storage-heavy, no visual UI yet Loses complex reasoning Cloud dependency, still risks losing context The most important thing to remember when choosing: LLM Wiki and MemPalace solve two different problems and can be used together rather than choosing one over the other. MemPalace remembers the history of your conversations with AI, meaning it knows what you said, what you decided, and how your thinking changed. LLM Wiki organizes knowledge from the outside world, the articles you read, the videos you watched, the documents you collected. Combining both lets AI understand both who you are and what field you're researching, and together they form a more complete second brain. The most thought-provoking insight from LLM Wiki Most of us use AI as a tool for generating temporary answers. Each session starts from scratch and nothing accumulates. Karpathy's LLM Wiki suggests a different direction: using AI as a knowledge compiler, where each new document isn't just stored but integrated into an existing structure, creating new connections and enriching what is already known. If you're researching a specific domain, whether AI, technology, finance, or anything else, this is worth trying today. Create a folder, drop in five articles you've read recently, and let Claude Code begin building the first wiki. After one week of adding documents consistently, you'll see the difference between an archive and an actual knowledge base.

Milla Jovovich, the face anyone who has watched the Resident Evil series will instantly recognize as Alice, and Leeloo from The Fifth Element, has surprised the AI community with the launch of MemPalace, a free, open-source AI memory system that has achieved the highest score ever recorded on the LongMemEval benchmark. The community has been joking that she never quite left the role, apparently still working for the Umbrella Corporation to build a new Red Queen. The project was developed in collaboration with programmer Ben Sigman, drawing inspiration from the ancient Greek technique of the Memory Palace. Rather than simply summarizing or storing information in disconnected fragments, MemPalace builds a structured virtual palace with clearly defined wings, corridors, rooms, closets, and drawers to organize entire conversations, ideas, and knowledge in a logical and searchable way. This shows just how vast the potential of AI has become when it enables actors, professors, and doctors alike to build powerful AI platforms that are genuinely usable in real work. Why did MemPalace catch everyone off guard? The first surprise was that the GitHub account is genuinely hers, which anyone can verify at https://github.com/milla-jovovich/. The second surprise is that Milla Jovovich is not participating in MemPalace as a celebrity endorser. She is committing code from that verified GitHub account, and for anyone who doubts it, the evidence is right there in this commit. On the purely technical side, MemPalace currently offers several notable advantages: Fully local: Runs entirely on your personal machine with no cloud required, no data sent anywhere, strong privacy, and zero ongoing cost. 100% information retention: No summarization means no loss of important detail. Easy integration: Supports multiple AI models including Claude, ChatGPT, Gemini, and Llama, and can import data from chat history, Slack, and other sources. Impressive benchmark results: Achieved the highest score, approaching a perfect score, on LongMemEval, a test measuring long-term recall, multi-step retrieval, and knowledge updates over time. MemPalace is not just a storage tool. It is a new approach to helping AI "remember like a human," organizing information spatially rather than relying purely on vector search or summarization. AAK technology: the secret language that compresses memory One standout feature in MemPalace is AAK technology, short for the experimental Abbreviation-As-A-Key system. This is an intelligent compression layer that functions like a shorthand language any LLM can read without a separate decoder. What is AAK and is it easy to understand? Imagine a thick notebook filled with months of conversation records. Instead of keeping every word intact, which consumes enormous storage and token budget, AAK compresses repeated information intelligently: It uses entity codes for frequently mentioned people, tools, or concepts. It adds structural markers to preserve relationships between ideas. It shortens sentences while retaining the core meaning. A simple example: Instead of repeating "The user prefers PostgreSQL because it is stable, open-source, and high-performance," AAK compresses this to something like "User prefers Postgres [reason: stable, open-source, high perf]," saving a significant number of tokens in the process. The advantages of AAK Strong compression, up to 30x in some cases, making it possible to fit months of data into a context window without hitting the limit. Still directly readable by any AI model without a special decoder. Fully local with no cloud dependency. Current limitations of AAK This is an experimental feature. On the LongMemEval benchmark, the AAK-compressed version sometimes scores lower than the raw uncompressed mode due to its lossy nature, meaning some information is lost in compression. The team is actively working on improvements. In short, AAK is like writing "concise but complete" personal notes, helping AI read faster and retain more without requiring a massive model to do it. Compared to Mem0 and Zep, the current leaders in AI memory Mem0 and Zep are the two most widely used AI memory frameworks for agents and chat applications. They each solve the "AI forgets everything" problem in different ways. Mem0, like a personalized companion How it works: Automatically extracts important information from conversations and stores it in a vector database with an optional knowledge graph layer. Strengths: Easy to use, token-efficient, well suited for long-term personalization. Weaknesses: Can miss details if summarization is too aggressive. LongMemEval benchmark score is approximately 49%. Zep, like a professional historian How it works: Builds a temporal knowledge graph where every event is anchored to a specific point in time. Strengths: Strong at complex queries and tracking how things change over time. Benchmark score approximately 64%. Weaknesses: Building and maintaining the graph requires more time and computational resources. Quick comparison table table { width: 100%; border-collapse: collapse; margin: 20px 0; font-family: Arial, sans-serif; } th, td { border: 1px solid #ddd; padding: 12px; text-align: left; } th { background-color: #f4f4f4; font-weight: bold; } tr:nth-child(even) { background-color: #fafafa; } tr:hover { background-color: #f1f1f1; } Criteria Mem0 Zep MemPalace (Milla Jovovich) Approach Personalization-focused, token-efficient Temporal, deep historical tracking Memory Palace, spatial organization Storage method Vector database with optional graph Temporal knowledge graph Full data retention with room structure and AAK compression Benchmark score ~49% ~64% Highest recorded, near 100% in some configurations Cost and resources Low Medium to high Very low, runs locally and free Ease of use Very easy Moderate Easy, single installation command Privacy Good, self-hosting available Good, cloud option available Excellent, 100% local What MemPalace brings to the AI community Milla Jovovich's MemPalace brings a fresh perspective to AI memory research, demonstrating that you don't need a massive model or expensive cloud infrastructure to achieve outstanding results. A creative idea drawn from ancient technique, combined with modern engineering, can outperform systems built with far greater resources. If you're building an AI agent or simply want your personal AI to remember things reliably over time, MemPalace is worth trying today since it installs via pip and runs entirely locally. This isn't just another tool. It's a meaningful step toward making AI more trustworthy and genuinely useful for the people who rely on it.

Receiving a task and then staring at the screen for an hour not knowing where to start is something that happens to Antigravity users no less than regular workers. The problem isn't that you're incompetent or lazy, but that your brain doesn't fear difficult tasks; it fears unclear ones. And when you give AI a vague request, the results Antigravity produces will be equally vague. Why does delegating tasks to Antigravity still yield poor results? Antigravity is a true agent because it can plan, write code, execute commands, and self-verify results. But this is precisely why many people are disappointed on their first use: they immediately assign Antigravity a huge and vague task, and the agent runs for 30 minutes in the wrong direction, exhausting the quota with unusable results. Cognitive scientists call the state of freezing before a large task "cognitive overload." The brain doesn't know where to start processing, so it chooses the safest option: doing nothing, and the familiar loop looks like this: Brain fears making mistakes → freezes Cannot start → deadline approaches Becomes more fearful → freezes again With Antigravity, user cognitive overload directly leads to poor prompts, and poor prompts cause the agent to run in the wrong direction. This loop, of course, consumes more tokens and time than any technical error. There are three approaches to break that loop, depending on how well you understand the requirements and how much you've established the process. Three Effective Approaches to Working with Antigravity Method 1: Download Source Code from Experienced Users This is the fastest way to get started without spending time setting up from scratch, especially suitable when you don't yet know what your process should look like. Antigravity works best when it has sufficient project context, meaning it can see the rules, workflows, skills, and memory directories that record old knowledge. Instead of building everything yourself, you copy the source code from someone who has fully set it up, download it, and let the agent read the entire existing configuration, provided, of course, that person has agreed or made it public. Note: Many people have exploited this to spread malware, so only install source code officially from Anthropic, Google, xAI, OpenAI,... or reputable individuals. When you copy the code repository from someone who has fully set it up, download it, and let the agent read the entire existing configuration, you gain two benefits simultaneously: The agent immediately understands the writing style for skills, workflows, technical foundations, and project rules from day one without you needing to re-explain. You learn how experienced individuals set up processes — from organizing memory directories to writing rules for the agent — without having to figure it out from scratch. However, if you don't understand the author's intentions, you won't be able to fully utilize the functions of this source code, much like wearing an oversized shirt. Method 2: Solve Small Steps Yourself Before Delegating Large Tasks This is the most quota-saving method and also a lesson I learned after many instances of waste due to delegating overly large tasks from the start. The 4C framework — Clarify, Chunk, Consult, Commit — originally used for human task management, is extremely effective when applied to Antigravity for a simple reason: the clearer you are before delegating, the less the agent has to guess. Clarify Step: Before typing anything into Antigravity, answer these 4 questions yourself: What does the final result look like? Who will use this? What is the actual deadline? What constitutes successful completion of this task? Five minutes spent answering will completely change the quality of your command. Instead of "build me a login system," you'll be able to write "build a login system using Google OAuth for a Next.js application, save the session to Firestore, redirect to the main page after successful login, run it locally, and take a screenshot for me to review." Chunk Step: Based on the Zeigarnik effect, once you start even a small step, your brain automatically wants to complete the subsequent steps. Ask the agent "break the task into the smallest steps to begin?" and go through each step. Allocate a specific amount of time to understand the structure and check if the agent correctly understands the requirements before letting it run a large task. But remember to only allocate a specific amount of time, because many problems only truly emerge during execution, and that's when we find solutions. In this step, we can immediately use Fast Mode for the agent to execute without needing to create a framework or deep thinking, or even if there's nothing special, Gemini Flash can perfectly handle this part, saving significant tokens for Gemini Pro and Claude Opus. Consult Step: Don't make it hard on yourself when others have gone before you. Similar to Method 1 of downloading others' source code, this step involves actively finding and reading how they approach problems, how they break down tasks, how they write commands, and how they set up processes, then distilling suitable methods to apply to your own work. You don't need to copy verbatim; just learn from their thought structure. This is especially valuable for tasks you've never delegated to an agent before, as those who have done it often discover common pitfalls you might not be aware of. Commit Step: Instead of trying to plan the entire task perfectly before starting, commit just the first 10 to 15 minutes to understanding it. Ask the agent a small question, see how it responds, and always add the prompt: “If the problem is unclear, you can always ask again; do not make arbitrary decisions.” There will certainly be shortcomings, but we will feel that we have come a long way with Antigravity and the task, instead of spending hours writing perfect prompts without accomplishing anything, which would surely be very boring. Method 3: Delegate Large Tasks Immediately When a Process is Already Established This method only works when you have gone through the previous two methods — having clear processes, contextual memory skills, and the agent being familiar with the rules and workflows. This can be considered the Commit step in the 4C framework: instead of worrying about the entire task, you need to guide the agent towards a specific outcome and let the agent handle the rest. At this point, Plan Mode is a better choice than Fast Mode because the agent must create a detailed execution plan before performing the task, allowing you to review that plan and leave notes for adjustments before letting the agent run. This method combines the agent's speed with your strategic vision because the process is already in place, so the clarification step should be integrated into the rules, workflows, and skills, eliminating the need for you to re-explain the context each time. This is especially a favorite method for Pros who use Claude for excellent planning and then feed it to GLM for task execution to save tokens. Which Method Should We Choose for Our Work? These three methods used with Antigravity are not mutually exclusive but are ordered from less to more context: Vague tasks, don't know where to start: Copy others' source code or use the 4C framework to clarify first. Understood but large and complex tasks: Go through small steps, use Flash for simple steps, and reserve Pro for steps requiring deep thought. Tasks with clear processes: Delegate directly with Plan Mode, letting the agent handle it while you work on other things. The common thread among all three methods is that you must do one thing before opening Antigravity: think. Not long thinking — just 5 to 10 minutes to clarify the requirements before delegating to the agent. That amount of time saves more quota than any other prompt optimization technique.

Anthropic accidentally or intentionally exposed the entire source code of Claude Code due to a basic configuration error during npm packaging. Over 512,000 lines of TypeScript, nearly 1,900 files, and even unannounced features suddenly became public worldwide, but what's more notable is that it happened exactly one day before April Fools' Day . A Silly Mistake from a Billion-Dollar Company The leak did not come from hackers or external attacks but was entirely due to an internal error, as Anthropic accidentally left out the cli.js.map file, weighing approximately 59.8 MB, in the npm package during release. This .map file contains sourcesContent — which is typically used for debugging — but it stored the entire original source code in plain text, making it readable by anyone. As a result, the entire architectural logic, system prompts, and secret features of Claude Code were completely exposed. However, what surprised many even more was that this error persisted for 20 days without being detected, despite Anthropic being the company behind the Bun runtime, which is directly related to this packaging error. Claw-code: A Rust Rewrite Emerges in Hours While Anthropic was sending DMCA requests to GitHub to remove copies, developer Sigrid Jin did what everyone expected: read the entire leaked source code and rewrite a completely new version in Rust. This further proves that powerful AI tools are truly dangerous only when they fall into the hands of those who know how to fully exploit them. The important legal point is that this project used a clean-room rewrite technique — meaning it was re-implemented based on observed behavioral specifications rather than directly copying the original code, so theoretically, it does not infringe on Anthropic's copyright. In terms of performance, Rust promises to be significantly faster than the original version running on Bun. At the time of writing, this repo had garnered 108k stars, an extremely rapid number on GitHub. Claw-code repo link https://github.com/instructkr/claw-code Note: Many people have taken advantage of this to spread malware. It is best now to only observe and read, and not to install, click strange links, or engage with anything related to the distribution of Claude Code Unannounced Features of Claude Code The most interesting part of the leak was not the technical architecture but the secret features within. Although many features were leaked, the three names most discussed by the community are Buddy System, KAIROS, and ULTRAPLAN. Virtual Pet Buddy System This is a Tamagotchi-style virtual pet system right in the terminal, featuring 18 different species with stats like "Debugging" and "Chaos," and even a 1% chance of dropping rare Shiny items. Notably, the source code explicitly states the testing period for this feature as April 1st to April 7th, 2026, coinciding with April Fools' Day. KAIROS Autonomous Mode This is an always-on assistant mode capable of autonomously performing tasks without user commands, which, if released, would be a significant advancement compared to how Claude Code currently operates. ULTRAPLAN Extended Thinking TimeThis feature allows offloading complex planning tasks to the cloud with a "thinking" time of up to 30 minutes, designed for problems requiring deep reasoning. Real Accident or Anthropic's April Fools' PR Campaign? The timing of the incident has raised considerable skepticism. Some arguments support the deliberate PR hypothesis: the Buddy System feature was scheduled for testing precisely on April 1st; the 'leak' inadvertently helped Anthropic showcase impressive technical capabilities and shift its image from a 'rigid company with third parties' to a 'talented victim' in the eyes of the community; and the fact that a company owning Bun made an error related to Bun itself for 20 days without detection sounds too incredible to be true. However, there are also counterarguments: sourcemap errors in npm are not uncommon, even for large companies, and having code cloned tens of thousands of times on GitHub is not something a company preparing for an IPO would want to happen. Anthropic has not yet confirmed or denied anything beyond the DMCA requests. Whether a genuine accident or a calculated scenario, the Claude Code source code has provided one of the rarest insights into building a real-world agentic AI system — its architecture, system prompts, file organization, and even unreleased features. If you are interested in building AI agents, the claw-code repo is still available and is one of the most worthwhile unofficial AI documents to read this year.

Is this the Van Dijk we know? Looking at two photos of Van Dijk in EA Sports FC: one labeled "DLSS 5 Off", one labeled "DLSS 5 On" with the exact same frame and play. But the face looks different—it is sharper and has more depth, plus more natural lighting and shadows, which naturally makes it look less like the original character. This is exactly what Nvidia has just introduced at GTC 2026, and it is precisely why the gaming community is in an uproar. What is DLSS and the journey from version 1 to 5 DLSS stands for Deep Learning Super Sampling, a technology that Nvidia uses AI to solve the classic gaming dilemma: gamers want beautiful graphics which require a powerful GPU, but to get high FPS, they must reduce image quality. DLSS was born to break that vicious cycle using AI. The journey through each version clearly shows how Nvidia's thinking has shifted: DLSS 1 (2018): Appeared with the RTX 20 series. The basic idea was to render the game at a lower resolution and use AI to upscale it to 4K. The result looked so blurry and lacked detail that many gamers did not bother enabling it. DLSS 2 (2020): A real leap forward. Nvidia significantly improved the AI model by introducing temporal accumulation, meaning the AI learns to combine information from multiple consecutive frames to reconstruct sharper details. This was when DLSS started to be widely used by gamers. DLSS 3 (2022): Added Frame Generation, enabling the AI to generate entirely new frames between real frames to double the FPS. It was criticized for causing input lag in some games. DLSS 3.5 (2023): Added Ray Reconstruction, using AI to reconstruct ray tracing effects instead of calculating everything manually. DLSS 5 (2026): A breakthrough of a completely different nature. From here, Nvidia no longer just upscales or adds frames. The AI starts to redraw all lighting, materials, and surface details in real time. What AI technology is behind DLSS 5 The core difference between DLSS 5 and all previous versions lies in the fact that, for the first time, AI not only improves existing images but generates entirely new visual content based on the 3D scene data. Specifically, DLSS 5 takes the color data and motion vectors of each frame and uses a neural rendering model to reconstruct photorealistic lighting and materials. What prevents it from "hallucinating" like normal AI image generators is that it is tightly anchored to the game engine's scene graph—the original 3D structure of each object in the game. The AI knows that this is a human face, this is shirt fabric, and this is a shadow, so it reconstructs them with correct physics instead of inventing random details. Jensen Huang called this the "GPT moment of graphics", the point when AI begins to replace part of the traditional rendering process. Nvidia expects an official launch in fall 2026, with confirmed integrated titles including: Starfield (Bethesda) Resident Evil Requiem (CAPCOM) Hogwarts Legacy (Warner Bros. Games) Assassin's Creed Shadows (Ubisoft) The demo at GTC required two RTX 5090 cards, though Nvidia claims the commercial version will run on a single GPU. What gamers are worried about: when AI starts to "redraw" your character Looking back at the comparison at the beginning, the DLSS 5 On version indeed looks sharper and more photorealistic. However, the gaming community is not happy about that. The problem is that the faces are modified. Not much, but enough to notice. And this is exactly the concern thousands of people are expressing on forums: when AI has the right to intervene in every single pixel of a game, who guarantees the character looks exactly as the game developer intended? The community is calling this "AI slop"—content that looks better on the surface but loses accuracy and the original artistic intent. Some compare the results to the "Harry Potter Balenciaga" style, implying the soulless and industrialized nature of mass-produced AI content. Especially for games with licenses for real players' faces, rendering the face differently, even slightly, is a serious issue. How does Nvidia respond? Facing criticism, Nvidia asserts that developers have full artistic control through the SDK, which includes: Adjusting AI effect intensity scene-by-scene Color correction and masking to protect sensitive image areas Completely disabling DLSS 5 on specific characters or objects Nvidia emphasizes that this is not just a filter, but a tool tightly integrated with the original 3D content. But the practical question remains: will all studios have enough resources and diligence to fine-tune each of those details, or will most just leave it on by default and let the AI decide? DLSS 5 is a point of no return The question is no longer whether DLSS 5 is better, because technically the answer is clearly yes. The real question is: when AI starts to participate in the rendering of every frame, where is the boundary between the "original game" and the "AI-enhanced game"? For AAA studios, this is an opportunity to cut rendering costs and push image quality to unimaginable heights. For gamers concerned about the integrity of the product, this is the first time they must ask: am I playing a game created by a developer, or a game generated by AI based on the developer's concept?

Enterprise IT departments surely ban OpenClaw on internal computers. The reason is not that the tool is ineffective, but because nobody can control what company data flows through it. This is a risk that businesses face when deploying AI agents without a reliable security solution. At GTC 2026, Nvidia answered directly with NemoClaw, a platform built on OpenClaw but adding the entire enterprise-grade security layer that the original version lacked. What is OpenClaw and why enterprises are hesitant to use it? If you don't know what OpenClaw is, here is the quickest way to understand: instead of sitting and instructing the AI step-by-step, OpenClaw allows you to create autonomous AI agents that work continuously without your intervention. Developed by engineer Peter Steinberger, who has since joined OpenAI, this platform still grows strongly globally, especially in China, even though tech giants like Gemini and Claude have blocked its API connections. The problem is that OpenClaw was designed for individuals and small teams, not for enterprises with sensitive data. When incorrectly installed or run with default configurations, the AI agent can access and process internal data without any control layers. Governments in many countries and giants like Google and Anthropic have repeatedly issued security warnings about this issue, which is why most enterprises still stay on the sidelines despite knowing the tool's potential. This is exactly the gap that Nvidia saw and decided to fill. How NemoClaw solves the security puzzle Instead of building a brand-new agent platform, Nvidia collaborated directly with Peter Steinberger to develop NemoClaw on the existing OpenClaw foundation. CEO Jensen Huang stated at GTC 2026 that every company needs an OpenClaw strategy, and NemoClaw is Nvidia's way of bringing that strategy into reality safely. The heart of NemoClaw is an open-source execution environment called OpenShell. Imagine it simply: instead of letting the AI agent run freely across the system like an unsupervised new employee, OpenShell locks it in a separate workspace with rules defined by the business itself. Specifically, OpenShell does three main things: Enforces guardrails based on each organization's internal policies, meaning each business decides what the AI agent can and cannot do. Keeps AI models running in a separate sandbox environment, preventing them from accessing data beyond their permitted scope. Adds data privacy protections before any information is processed, while increasing scalability as demand grows. What do enterprises specifically gain when using NemoClaw? Three practical benefits NemoClaw brings compared to using OpenClaw, as provided by Nvidia: Data control: The IT department can define exactly which documents and systems the AI agent is allowed to access and what it can do with that data. No more AI agents running wild without anyone knowing what they are reading. Flexible AI model selection: Businesses are not locked into a single vendor. NemoClaw supports Nvidia's NemoTron, Anthropic's Claude, OpenAI's GPT, and any other open AI models, allowing cloud model access right on local devices without relying on specific hardware. No infrastructure changes needed: NemoClaw runs on top of existing OpenClaw setups, meaning teams currently using OpenClaw can upgrade to NemoClaw without starting over. NemoClaw is currently in the alpha stage, meaning it is still being finalized before the official launch. Currently, NemoClaw has open-sourced its code on GitHub for those who need higher customization. This is a point to note if you are considering enterprise deployment right now. What else is notable at GTC 2026 besides NemoClaw? NemoClaw is just one part of Nvidia's massive wave of announcements at GTC 2026. Other key highlights include: Next-generation Vera CPU: Designed specifically for the AI agent era with double the performance and 50% faster speed than traditional CPUs, optimized for complex reinforcement learning tasks. $1 trillion revenue forecast: Nvidia expects revenue from Blackwell and Vera Rubin AI chips to reach this level by 2027, reflecting the company's massive bet on the booming AI agent wave. Nemotron Alliance: An open collaborative initiative to share resources and computing capacity in the open-source AI domain, drawing the participation of many industry giants. Groq 3 and DLSS 5: The Groq 3 language processing unit and DLSS 5 graphics technology were also announced, expanding Nvidia's AI ecosystem beyond agents and into game graphics. NemoClaw is the bridge bringing AI agents from individuals to enterprises OpenClaw has proven that AI agents work effectively in practice. The issue is not the technology, but trust—and trust in an enterprise environment comes from control, transparency, and internal policy compliance. NemoClaw does not try to replace OpenClaw, but builds exactly that layer on top of it. If NemoClaw works as promised when officially released, this could be the key to getting AI agents widely deployed in enterprises, instead of being blocked by IT departments for security reasons. That is precisely the real market Nvidia is targeting.

Anthropic was the first to introduce the concept of skills into AI in a truly structured way. Interestingly, this skill concept is now spreading across the entire AI ecosystem, from how companies build agents to how individuals work with AI daily. From Claude to GPT, from Gemini to emerging agent tools, skills are gradually becoming the common language the entire industry is moving towards.If you're unfamiliar with what skills are in Claude, you can refer to this article first: Claude Agent Skills are essential skills to know about AI in 2026.Why are skills rapidly expanding within the AI community?The numbers behind this trend speak volumes. Skillsmp, currently the largest skill aggregation platform, has compiled over 500k skills from GitHub, all compatible with Claude Code, Codex CLI, and ChatGPT. The community-built Antigravity Awesome Skills library currently boasts over 1,272 skills, with 24k GitHub stars and over 4.2k forks. Anthropic's official frontend-design skill is currently the most installed skill, with over 277k installations as of March 2026.How are major AI companies approaching skills?Each major platform is addressing this challenge in its own way, but all are aiming for the same goal: helping AI understand users once for complete reusability later.In December 2025, Anthropic announced the open standard for Agent Skills, and OpenAI quickly adopted the same format for Codex CLI and ChatGPT shortly thereafter. As of early 2026, the SKILL.md standard is supported on Claude, Claude Code, Manus, Cursor, VS Code, GitHub Copilot, OpenAI Codex, Gemini CLI, and many other platforms. This means that a skill can be used across almost all popular AI tools, without being locked into a single platform.Naturally, major companies like Google, HashiCorp, Vercel, and Stripe are also participating, having announced official skills for their own platforms using the same Skill.md format.What is Skillsmp and how to find suitable skills? Skillsmp is an independent community platform, not affiliated with Anthropic, specializing in aggregating skills from public GitHub repositories with smart filters by category, author, and popularity. This is the best starting point if you want to find skills for a specific domain without having to scour GitHub outside of Anthropic's official platform.Using Skillsmp is actually very simple: search by keywords for the task you want to automate, filter by GitHub stars to ensure quality, and review the skill's description and activation conditions before installing. All skills on Skillsmp use the open SKILL.md standard and are hosted on GitHub, so users don't need to worry about compatibility.One point to note: skills on Skillsmp are filtered for a minimum of 2 GitHub stars and scanned for basic quality indicators, but you should review them carefully before installation as they are community-sourced code.For example, our team searched for a writer skill on Skillsmp and found the seo-content-writer from Antigravity Awesome Skills. This works quite well for English but does not yet support Vietnamese, especially Vietnamese E-E-A-T standards. Therefore, if you use it frequently, you should modify that skill for your work rather than creating one from scratch. ReferencesIf you're interested in creating your own skills, Anthropic has published official documentation providing a complete guide on how to build skills for Claude. This is currently the most official and accurate reference source.📄 Original English document from Anthropic: Google Drive link here📄 Vietnamese translation: Google Drive link here Skills are not features; they are an investment in workflowsThe shift from prompts to skills is happening not because skills are a novelty, but because they correctly address the challenges faced by those working with AI in practice: consistency, scalability, and not having to start from scratch every day.Anthropic is leading this trend, replacing OpenAI and Google, but the entire industry is moving in the same direction. Investing in building good skills today not only helps you work more efficiently with Claude but also provides a foundational mindset for working better with any AI platform in the future.

You already know what Skills are in Claude and have created a few — but the results are still inconsistent. Sometimes the AI does exactly what you had in mind, and other times it goes completely off the rails. The problem usually isn't Claude itself. It's your SKILL.md file: missing layers, no clear order, or everything crammed into one long unstructured block of text. The 8-Layer Framework is how the 4AIVN team addresses that — by breaking a Skill down into distinct layers, from foundational to operational. We use this framework internally to produce the articles you read on 4AIVN, and you can absolutely apply it to whatever problem you're solving. That said, it needs to be said plainly: this is Prompt Engineering, and it's only one piece of a larger picture. For our team, it's the piece that helps us assign tasks to AI clearly — but producing articles that genuinely resonate with readers, follow conversion frameworks, and meet our editorial standards still requires a lot more than this alone. If you're not yet familiar with Skills in Claude, start here: Claude Agent Skills: the AI feature you need to know about in 2026, which covers the foundations before diving into this framework. Why Skill structure determines everything SKILL.md looks a lot like the long prompts many people were writing for ChatGPT, Gemini, and Claude back in 2024: "You are a copywriter with 10 years of experience, write using the PAS framework, empathetic tone, never use words like breakthrough or perfect solution..." You type it out, finish the chat, close the window — and the next time you open a new session, you have to explain everything again from scratch. Skills are different precisely because they are a guide you only need to write once, and Claude will understand how to work with you without needing re-explanation each session. The distinction is this: a prompt defines what needs to be done this time, while a Skill defines how to work together long-term. A common mistake is writing SKILL.md the same way people write long prompts — dumping everything into one block without any layering. Claude can read it, but when it encounters a situation you didn't explicitly list, it has no conceptual framework to fall back on. That's why the output ends up inconsistent. The 8-Layer Framework divides SKILL.md content into two groups: 4 foundational layers that help the AI understand who it is and what it does, and 4 operational layers that define how it actually works. Four foundational layers that define who the AI is Layer 1 – Mission Define the core role of this Skill. This is the first thing Claude reads and uses to shape all behavior that follows. Example: "You are an editor specializing in writing and editing AI articles for the 4AIVN community, targeting Vietnamese readers who are interested in AI but have no technical background." Layer 2 – Context Describe the environment this Skill operates in. The same request — "write an AI article" — calls for completely different writing styles depending on whether it's for a website, a Facebook page, or Instagram. Example: "Articles are published on 4aivn.com, read primarily on mobile, requiring short paragraphs, clear H2 and H3 headings, and a length of approximately 1,000 to 1,200 words." Layer 3 – Input Define what form Claude will receive information in. This layer is frequently skipped, which causes the AI to make assumptions whenever the input isn't explicit. Example: "Input can be: a single keyword, a brief of a few lines, or a ready-made outline. If only a keyword is provided, Claude must ask clarifying questions before writing." Layer 4 – Output Define what the returned result should look like — format, length, and default structure. Example: "The default output is a complete article consisting of an intro (sapo), 3 to 4 H2 sections, and a conclusion. If the user only needs an outline, return a bulleted outline with a short description of each section." Four operational layers that define how the AI works Layer 5 – Rule set This is the most important layer. You define the writing style, mandatory structure, and equally critical — a list of things the AI must never do. The more specific, the better. Example: The intro (sapo) must open with a real-world situation or a surprising statistic — never a definition. At least 70% of H2 headings must be phrased as questions to support SEO and GEO, and each H2 must include at least one concrete example. Forbidden phrases: "In the rapidly changing world of technology...", "It cannot be denied that...", "Hope you found this article useful." Layer 6 – Proactive questions Instead of the AI diving straight into work, this layer makes it ask questions first. It eliminates most cases of off-target output caused by the AI guessing at what you meant. Example: "Before writing any article, Claude must ask at least 3 questions: who is the target audience, what is the article's goal (inform / persuade / instruct), and what tone is preferred (serious / approachable / neutral)." Layer 7 – Plan After gathering enough information, the AI must present an outline and explicitly state the rules it will apply to this specific article before writing begins. You can see its thinking and redirect it before it goes the wrong way. Example: "After receiving sufficient information, present: (1) a complete outline with a brief description of each section, (2) the primary keywords and related keywords prioritized for this article." Layer 8 – Agreement Only when the user confirms agreement with the plan does the AI begin writing. Without this step, Layers 6 and 7 become ceremonial — the AI can still start writing on its own after presenting the outline. Example: "After presenting the outline, wait for the user to confirm or request revisions. Only begin writing the full article upon receiving a clear signal of approval." How to write your SKILL.md using the 8 layers Don't try to implement all 8 layers at once. Here's the practical sequence to follow: Start with Layer 1 and Layer 5 to establish the AI's role and rule set. Just these two layers will produce a more noticeable improvement than any regular prompt. Test it with one or two real requests and check whether the output is on target. Once Layer 5 is stable, add Layer 6 to make the AI ask questions first. You'll quickly notice what information you tend to leave out when assigning tasks — then add Layers 7 and 8 to close the control loop. Add Layers 2, 3, and 4 when you notice the AI making wrong assumptions about the environment, input format, or output structure — those are the signs that these layers are needed. References: a critical part of Skills After using Skills for a while, you'll run into a new problem: the AI follows the right structure and the right rules, but something about the brand voice still isn't quite there — you still end up editing. The tone is correct but doesn't sound like you. The structure is right but doesn't feel as familiar as your older articles. This is where References come in. What are References in SKILL.md? References are supplementary files you place alongside SKILL.md. They contain things that are too long or too specific to fit inside the rule set, but which the AI needs to read under certain conditions. For content writers, the most useful type of Reference is approved output — complete articles you've been satisfied with — used as reference samples so the AI can learn your actual tone and style rather than just reading abstract rules. How our team adds References to a Skill Folder structure: writer-4aivn/ SKILL.md references/ sample-article-01.md (published article, satisfactory result) sample-article-02.md sample-article-03.md Inside SKILL.md, declare explicitly when Claude should read each file: ## Reference Files references/sample-article-01.md: Read when the user requests a practical how-to article references/sample-article-02.md: Read when referencing tone for an AI tool analysis article One important rule Don't leave it up to Claude to decide whether it needs to read a Reference file. Provide specific activation conditions — "read when the user requests an article of type X" — rather than "read if needed." The latter is too vague: Claude will either ignore it or read it at the wrong moment. How many sample articles are enough? Start with 2 to 3 sample articles covering different content types: practical guides, tool analyses, opinion pieces. You don't need more than that at this stage. Each sample article you add gives the AI one more piece of evidence to understand your tone — one step beyond just reading rules. Creating Skills will take a lot of time upfront, much like the time we used to spend refining long prompts. But once the output stabilizes, you'll often be surprised by what Claude can write and do on its own. This is the first installment in our series on writing with AI using Skills. This part gets you your first output from a Skill — but that first output is rarely perfect. Future installments will go deeper into refining Skills for more complex scenarios, until the AI works exactly the way you intend.

Gemini 3.1 Flash-Lite: A Fast, Capable, and Affordable Option If you're looking for an AI solution that is both fast and economical for deploying large-scale projects, then Gemini 3.1 Flash-Lite, recently launched by Google, is the answer. This is not just a minor upgrade, but truly a step that makes AI technology more accessible to everyone. Strong Performance at a Manageable Cost What impressed me most about Gemini 3.1 Flash-Lite is how Google balances economic considerations with performance. For those optimizing monthly API costs, this will be a very worthwhile option, especially when popular models like Claude Opus or Claude Code can incur exorbitant costs of up to $200 if you don't want to quickly hit limits. Very Reasonable Price: It only costs about $0.25 per million input tokens. This price allows us to confidently deploy large data processing features without excessive budget concerns. Impressive Response Speed: The feeling of waiting for AI to respond can sometimes be inconvenient, but with Flash-Lite, the speed of the first output is 1.5 times faster than the previous 2.5 Flash version. Although the cost has increased compared to Gemini 2.5 Flash-Lite, it remains reasonable compared to the general market, and the trade-off for speed is truly appreciated by everyone. Built on Gemini 3 Pro Capabilities Despite the "Lite" in its name, don't underestimate its capabilities. Developed based on the Gemini 3 Pro platform, this model still smoothly processes everything from text and images to audio and video. Deep Comprehension: With an Elo score of 1432, Flash-Lite proves it's not inferior to competitors in its segment. Especially, a context window of up to 1 million tokens is perhaps already common for models from Google, which is truly beneficial for those who frequently work with extremely long documents. Flexibility for Developers: Another plus is that you can customize the "depth" of AI reasoning. Depending on whether you're building a simple chatbot or need complex data analysis, you can adjust it for optimal performance. Safety and Reliability Google has also made many refinements to make this model more user-friendly and intelligent in its communication. It minimizes unreasonable question rejections while ensuring strict safety standards, helping everyone feel confident when integrating it into real-world products. Conclusion Overall, Gemini 3.1 Flash-Lite is a very practical step forward from Google. It focuses on exactly what you need: speed, efficiency, and competitive pricing. If you're planning to upgrade your system to reduce tokens for tasks that don't require complex reasoning, give this Gemini 3.1 Flash-Lite version a try!

Google has officially launched Nano Banana 2 (Gemini 3.1 Flash Image), marking a notable move as the company decided to bring features that were once exclusive to Nano Banana Pro down to the mainstream line. This is truly a powerful upgrade and a testament to Google's promise to democratize pro-level technology for more users, allowing even free users to experience pro features.What is Nano Banana 2 and how does it differ from Nano Banana Pro?Nano Banana 2 leverages the power of the latest Gemini 3.1 Flash Image model to perform image generation and editing requests at a significantly faster speed than the pro version.Core differences compared to the Pro versionSpeed: Speed is the primary focus of Nano Banana 2. While Nano Banana Pro focuses on tasks requiring the highest fidelity and absolute factual precision, Nano Banana 2 prioritizes fast processing speeds (Flash speed) while still maintaining image quality comparable to the Pro version.Cost: The Nano Banana 2 API is significantly cheaper. For example, a 1024x1024 resolution image that previously cost around $0.13 is now reduced to approximately $0.07 with Nano Banana 2. While still slightly high, Google has made efforts to lower prices to make it more accessible to everyone.Target Audience: Nano Banana 2 definitely targets a broader user base, as free users can now experience it instead of being restricted to paid Pro or Ultra plans as before.Inherited Features: Nano Banana 2 inherits premium features from the Pro version, such as maintaining character consistency and interpreting complex prompts.Key Pro-like features in Nano Banana 2Subject Consistency: This is an incredibly useful yet familiar upgrade for marketers, comic creators, and graphic designers. Similar to the Pro version, this feature in Nano Banana 2 allows maintaining the consistent appearance of up to 5 characters and the stability of up to 14 objects within the same workflow.Accurate and Multilingual Text Rendering: Concerns over typos or language barriers in AI-generated images are no longer an issue when using Nano Banana. All the flagship features that made the Pro line famous—from correct spelling rendering to direct text translation within images—are now integrated into Nano Banana 2. The chances of image typos, broken fonts, or language mix-ups have been reduced significantly, making them very rare.Real-time Information Integration: Nano Banana 2 uses Gemini and web search data, enabling it to update changes in real time to accurately render specific subjects and avoid going off-topic when generating images.Pro-level Resolution: Nano Banana 2 narrows the feature gap with the pro lineup by supporting output resolutions from 512px up to 4K. Users also gain access to new aspect ratio options such as 4:1, 1:4, 8:1, and 1:8.Transparency: Google has ensured that all images generated by Nano Banana 2 are embedded with a watermark via the SynthID system and comply with C2PA standards for AI provenance verification.How to use Nano Banana 2 on the Gemini AppYou can easily experience Nano Banana 2 directly on the Gemini app or Google AI Studio whether you are using a Free, Pro, or Ultra plan:A pleasant surprise: It is truly surprising that Nano Banana 2 allows users to directly select output image styles with presets right inside the Gemini app without needing to type them into the prompt. Although the results are not always perfect, removing the need to type prompts reduces the chance of forgetting to include the style in the prompt, helping Nano Banana generate images that better match user intent.Regarding aspect ratios, users still need to specify them directly in the prompt, which is something I frequently forget to include.Note: If you are a Pro/Ultra user who requires maximum factual accuracy, you can still switch back to Nano Banana Pro via the three-dots menu (select regenerate/redo).Nano Banana 2 vs. GPT Image 1.5 ShowdownAlthough GPT Image 1.5 should ideally be compared with the Pro lineup, I still want to draw an interesting comparison, as GPT Image 1.5 and Nano Banana 2 target different image generation goals and user bases:Different design philosophies between OpenAI and GoogleGPT Image 1.5 is designed by OpenAI as a creative studio focused on precision. It delivers experiences closer to everyday photography styles compared to Nano Banana.Nano Banana 2, on the other hand, is likened to a cinematographer focusing on visual impact. Google emphasizes "real-world" knowledge to produce images with exceptionally high realism, vivid lighting, and sharp details.Do real-world experiences differ significantly between the two models?Based on head-to-head testing, the results reveal distinct differences in style:Realism and Image Style: GPT Image 1.5 excels at producing everyday images with noise and a natural feel similar to photos taken on an iPhone with flash. In contrast, Nano Banana often delivers overly perfect results, sometimes looking like studio shots or heavily post-processed commercial photography.Prompt Adherence: GPT Image 1.5 naturally stands out in terms of strict prompt adherence, as Google users would need to upgrade to the Pro version for similar adherence levels. For instance, in a 6x6 grid test featuring 36 distinct objects, it accurately rendered the position of every single object—a task where previous generations of Nano Banana consistently failed. Nano Banana 2 has improved significantly in this area, but it still occasionally leans toward a more pre-arranged composition.Text in Images: Both models have effectively resolved spelling errors in images. However, GPT Image 1.5 tends to output layouts resembling pre-designed Canva templates, while Nano Banana 2 excels at translating text directly within the image itself—for example, translating inscription text on a gravestone inside the picture.In-place Editing: GPT Image 1.5 excels at in-painting—changing a specific detail (such as shirt color) while keeping facial features and lighting intact. Nano Banana 2 shines at blending, capable of merging up to 14 reference images to create a complex final render regarding lighting, depth, and color.Speed: Both are lightning-fast. To the naked eye, it is nearly impossible to tell whether GPT Image 1.5 or Nano Banana 2 is faster.API Pricing: GPT Image 1.5 offers a more cost-optimized rate for standard image generation (around $0.009 per image). Below is a detailed cost comparison table for reference:[CHART_1]With Nano Banana 2, Google is not only racing in technology but also focusing on practical user experience through ultra-fast speeds and professional image control. It is undeniably an indispensable tool for content creators and marketers in 2026.

Anthropic seems to give the tech world, especially developers, no rest, not even for a day. Amidst Claude Code's rapid growth (revenue hitting $2.5 billion just two months after launch and reaching 29 million installations), Anthropic isn't stopping; instead, it's continuously rolling out new features for Claude Code such as Scan Security, Schedule Task, and Remote Control. This has led to widespread speculation that Claude is indeed coding its own features, making it impossible for humans to keep up.Once you delve into and experience the Claude Code ecosystem, I guarantee you'll become "addicted" to coding with this tool, making it extremely difficult to return to traditional working methods. This is simply because the new features Claude Code offers far exceed all conventional expectations.Scan Security (Claude Code Security)This is a security vulnerability scanning capability directly integrated into Claude Code. Immediately after Claude Code announced this Scan Security feature, it wiped billions of dollars from the market capitalization of many security giants like CrowdStrike (down 7.8%), Okta (down 9.2%), and many other big names such as Cloudflare, Zscaler, Tenable, SentinelOne, Fortinet, and Palo Alto Networks also saw declines of over 10%.What is Claude Code Scan Security?: Unlike traditional tools that only perform pattern matching, Claude Code Security can think like a security expert. It analyzes how components interact, traces data flows, and detects complex logical errors or access control flaws that conventional tools often miss.User Experience: You simply run the command /security-review in the terminal. Claude will analyze the source code, provide detailed explanations for each issue, and suggest patches for your review and approval.When to Use: You should use this feature before committing significant changes or when preparing to deploy source code to a production environment to ensure maximum safety and avoid costly, trivial errors.Schedule Task (Task Scheduling)This feature allows you to create recurring tasks or workflows for Claude Cowork to run automatically. Claude will save your instructions (prompts) and execute them according to your chosen frequency (hourly, daily, weekly). It can access connected tools like Slack, Google Drive to collect and process data.User Experience: You can set this up via the /schedule command or through the "Scheduled" tab on the Claude Desktop interface. Claude will automatically execute and send results (reports, summaries) upon completion. However, your computer needs to be connected to the internet and the Claude Desktop application must be open for the task to run on schedule.When to Use: It's very useful for creating daily summary newsletters from email/Slack, generating weekly reports from spreadsheets, or regularly monitoring competitor news without manual intervention each time, especially when you've granted certain permissions for Cowork to interact with your machine. This feature is extremely suitable for Vietnamese developers working across time zones. You can schedule Claude to run tests or compile reports at 3 AM (Vietnam time) so that the next morning you wake up to immediate report results for clients in the US or Europe, without needing to keep your computer on and stay up late monitoring. How convenient is that?Remote Control (Remote Control)This is considered a "lifestyle" feature that helps you maintain your workflow even when away from your desk. However, a small reminder to everyone: use it only when truly necessary, otherwise, take appropriate rest time, as continuous work can lead to burnout.Who is this remote control feature for?: Remote Control creates a secure synchronization layer between your local machine's terminal and the Claude application on your phone (or another web browser). Your code remains securely on your local machine; your phone merely acts as a "window" to control that work session. Anyone who previously had to remote into a company machine via VPN or Tailscale using 4G/5G networks on the streets of Hanoi or Saigon will surely find this /rc feature to be a true game-changer because it's much smoother and more native.User Experience: Simply run the command claude rc or /rc in the terminal, and a QR code will appear. You scan the code with your phone, and from there, you can monitor what Claude is doing in real-time, approve or reject file changes, and provide further instructions.When to Use: This is a lifesaver when you're performing a long-running task (like refactoring an entire library or debugging a complex build) but need to get up to meet someone or have an urgent matter. For instance, you're at the office starting a large project, but it's time to meet a partner. Instead of waiting for the task to finish before leaving, you just enable /rc, grab your phone, get into a Grab car, and on the way, you can still monitor progress, approve files Claude has finished writing, and issue direct editing commands right from the car. By the time you arrive at the client meeting, the programming work will have been completed smoothly.Note: Currently, the Remote Control feature is in preview for paid plans (Pro or Max), is not yet fully widespread, and requires your computer to always be on and connected to the internet.

Seedance 2.0 là mô hình trí tuệ nhân tạo (AI) đang tạo ra sự bùng nổ toàn cầu về AI Video, đặc biệt sau khi Seedance 2.0 mô tả "cuộc chiến" giữa Brad Pitt và Tom Cruise, vì vậy mọi người ai cũng gọi đây là "khoảnh khắc DeepSeek" của tương lai của AI video.Seedance 2.0 là thế hệ AI mới ông lớn đứng sau nó là ByteDance và chính thức ra mắt tháng 2-2026. Đây không chỉ là một bản cập nhật thông thường mà được coi là một bước ngoặt trong lĩnh vực AI video, cho phép tạo ra các thước phim chất lượng điện ảnh tích hợp sẵn âm thanh đồng bộ. Mô hình này hoạt động như một "đạo diễn ảo" có khả năng hiểu sâu sắc về ngôn ngữ máy quay, giải phẫu học con người và các quy luật vật lý phức tạp chắc chắn nó sẽ thay đổi hoàn toàn ngành công nghiệp video và phá vỡ thế độc tôn của Veo và Sora.Seedance 2.0 có thể làm được gì?Seedance 2.0 được thiết kế để phục vụ sản xuất phim chuyên nghiệp, thương mại điện tử và quảng cáo. Hệ thống có khả năng xử lý đồng thời văn bản, hình ảnh, âm thanh và video đầu vào để tạo ra các đoạn clip ngắn có tính gắn kết cao và điểm mạnh nhất đang được mọi người chú ý nhất đang là khả năng ghép gương mặt vào video hoàn chỉnh. Video của Seedance 2.0 tạo ra khiến cho mọi người quá khó để phân biệt thật giả tuy nhiên trước áp lực pháp lý, ByteDance đã phải tạm thời vô hiệu hóa một số tính năng như sử dụng khuôn mặt người thật làm tham chiếu để bảo vệ quyền riêng tư và bản quyền.[VIDEO:MCViYDF27vs|Video về Seedance 2.0 với Madara|Video về Seedance 2.0 với Madara]Những tính năng đột phá của Seedance 2.0 mà Veo và Sora chưa theo kịp là gì?Có thể thấy sau nhiều video so sánh thì Seedance 2.0 đã cho thấy vượt trội Veo 3.1 và Sora 2 về những điều sau:Tạo âm thanh gốc (native audio): Seedance 2.0 tạo ra âm thanh và video đồng thời ngay trong quy trình cốt lõi, đảm bảo tiếng động môi trường và nhạc nền khớp hoàn hảo với hình ảnh, đảm bảo các tác động vật lý chính xác hơn hẳn so với Veo 3.1 và Sora 2.Khớp khẩu hình chính xác: Các nhân vật có thể nói chuyện với cử động miệng, khuôn mặt khớp chính xác theo âm vị cho hơn 8 ngôn ngữ khác nhau, bao gồm cả tiếng Anh, Trung, Nhật, Hàn, Pháp....Hệ thống tham chiếu đa phương thức cực đại: Cho phép người dùng tải lên tối đa 12 tệp tham chiếu (gồm 9 hình ảnh, 3 video và 3 âm thanh) để kiểm soát tuyệt đối về phong cách, chuyển động và âm điệu của video đầu ra.Độ phân giải 2K Cinema: Hỗ trợ xuất video chất lượng chuyên nghiệp lên đến mức 2K, vượt xa tiêu chuẩn 1080p của nhiều đối thủ.Cách sử dụng Seedance 2.0 ở kênh nàoHiện tại, Seedance 2.0 đang trong giai đoạn thử nghiệm giới hạn và có thể truy cập qua các kênh chính sau:Nền tảng chính thức: Người dùng có thể sử dụng thông qua Jimeng AI (jimeng.jianying.com) đây là trang dành cho thị trường Trung Quốc còn ở trang dreamina.capcut.com cho thị trường quốc tế thì ByteDance chưa mở Seedance 2.0 để trải nghiệm.Quy trình tạo video khá đơn giản:Nhập liệu: Nhập câu lệnh văn bản mô tả chủ thể, góc máy, phong cách và chuyển động.Sử dụng cú pháp @: Người dùng có thể dùng ký hiệu "@" để chỉ định chính xác tệp tham chiếu nào điều khiển yếu tố nào (ví dụ: dùng @Image1 cho nhân vật, @Video1 cho chuyển động máy quay).Thiết lập khung hình: Tải lên hình ảnh cho khung hình đầu tiên và khung hình cuối cùng để AI tính toán đường đi của chuyển động mượt mà hơn.Cấu hình: Chọn độ phân giải (720p đến 2K) và thời lượng video thường từ 4 đến 15 giây hoặc hơn tùy gói dịch vụ.Mọi người có thể tham khảo quy trình tạo video ở đây https://cellphones.com.vn/sforum/seedance-2-0 hoặc tham khảo có rất nhiều bên hướng dẫn vào Jimeng AI với tài khoản DouyinPhản ứng của Hollywood và cộng đồngSự ra mắt của Seedance 2.0 đã gây ra một "cơn địa chấn" công nghệ nhưng cũng đi kèm nhiều tranh cãi gay gắt:Phản ứng từ cộng đồng công nghệ: Tỷ phú Elon Musk đã bày tỏ sự ấn tượng khi nhận xét trên mạng xã hội X khi nói về Seedance 2.0 rằng: "Mọi thứ đang diễn ra thật nhanh", đạo diễn Hollywood Charles Curran cho biết sau khi trải nghiệm Seedance 2.0, chỉ với 20 phút và 60 USD, ông đã tạo thành công trailer cho một bộ phim có các nhân vật từ trò chơi Halo.Hollywood và cuộc chiến bản quyền: Netflix đã ngay lập tức gửi thư cảnh cáo ByteDance vì mô hình này tái tạo trái phép các thương hiệu nổi tiếng như Stranger Things, Squid Game và Bridgerton. Hiệp hội Điện ảnh Mỹ (MPA) cùng các ông lớn như Disney, Warner Bros. Discovery cũng lên tiếng án chỉ trích sau khi các đoạn video AI về Tom Cruise và Brad Pitt lan truyền mạnh mẽ.Tác động thị trường: Việc ra mắt Seedance 2.0 đã khiến cổ phiếu của các công ty AI Trung Quốc tăng vọt, trong khi gây áp lực lớn lên các tập đoàn công nghệ Mỹ như Google và Amazon do lo ngại về sự thay đổi mô hình kinh tế trong ngành giải trí trị giá hàng trăm tỷ đô la.

What are Claude Agent Skills? Imagine you're an expert in a specific field. Instead of repeating lengthy instructions every time — wasting tokens and dragging down Claude's performance — Skills let you transform Claude from a general-purpose assistant into a dedicated specialist that gets to work immediately. And your role in this? You still provide the ideas, references, and data. Skills then build a structured standard from that input and enforce the right process every time Claude executes it. Even as large language models (LLMs) gain increasingly large context windows, Claude can still lose track of complex instructions when a conversation grows too long — or forget everything when a new session begins. Claude Agent Skills were built to solve this problem at the root. They are reusable capability modules that extend Claude's functionality by packaging specialized instructions, metadata, and resources such as scripts or document templates into a single centralized folder. Core characteristics of Skills Progressive disclosure — To optimize context usage and cost, Skills are loaded into Claude across three levels: Level 1 (Metadata): Always loaded at the start of a session — just the name and description (~100 tokens) so Claude knows the Skill exists. Level 2 (Instructions): The full contents of the SKILL.md file are only loaded into memory when Claude decides to activate the Skill. Level 3 (Resources): Additional scripts, templates, or reference documents are only accessed when the Skill's workflow requires them. Automatic recognition and activation Claude decides on its own when to use a Skill, based on natural language context descriptions — no manual commands or complex classification algorithms required from the user. Portability and encapsulation Each Skill exists as a self-contained folder on the file system, making it easy to share across projects, machines, or organizations without any complex API configuration. Transparency and control Claude displays exactly which Skills are currently active, giving users full visibility and control over outputs — especially useful when managing a large library of Skills. How Skills differ from Tools and Workflows The distinction between Skills, Tools, and Workflows in the Claude ecosystem comes down to their fundamental nature: one provides reasoning guidance, one provides executable actions, and one defines a sequence of steps. How do Skills differ from Tools? The core difference is that Skills create instructions while Tools execute actions. Tools are runnable pieces of code — read, write, bash, or Python scripts — that perform a specific task and return a result immediately. Skills, by contrast, are not executable code. They function more like an extended knowledge module, containing Markdown instructions that teach Claude how to think and what rules to follow in a given domain. How they operate: Tools work synchronously and directly (run → result). Skills operate through progressive disclosure, loading detailed instructions into the conversation context only when Claude's reasoning determines the task is a match. Their roles: Skills make Claude smarter in a specific domain (such as PDF handling or marketing strategy), while Tools are what Claude uses to take action after Skills have guided its thinking. How do Skills differ from Workflows? The relationship here is that Skills encapsulate and direct Workflows. Process encapsulation: A Workflow is a repeatable sequence of steps for completing a complex task — for example: research → draft → quality check → publish. A Skill acts as the process handbook, containing that entire Workflow inside the SKILL.md file. Flexibility: Instead of the user manually coordinating each step in a Workflow, Skills let Claude automate that coordination. Claude reads the Workflow defined in the Skill and decides on its own when to call which Tool to complete each step. Memory management: Unlike conventional Workflows that must load all instructions into the prompt upfront — wasting tokens and introducing confusion — Skills only activate the relevant Workflow when the context matches, making context management significantly more efficient. How to create a Skill directly on Claude.ai The ability to create and use Agent Skills is now available to all users on both the web and desktop versions of Claude. The 4AIVN team is sharing a straightforward way to create a Skill for drafting rental contracts — so even users with no coding background can follow along. For more advanced customization, check out our article on how to create professional Claude Skills with 8 content layers. Step 1: Enable the Skills feature Before getting started, you need to turn on the necessary permissions in settings: Click on your profile icon in the bottom-left corner. Go to Settings > Capabilities. Toggle on both features: Code execution and file creation. In the latest version, all Skills have been moved under the Customize section. Go to https://claude.ai/customize/skills to view all your Skills. Step 2: Upload your reference file At this stage, only use the Add button in the Skills section if you already have a pre-built Skill file to upload. If you're creating a new Skill from scratch, don't use the Add button — you won't be able to attach input files there. Instead, start from the chat screen. Since building a Skill takes some time, prepare your reference files in advance to speed up the process. These can include sample output files, or documents describing your role, workflow, output format, examples, step-by-step instructions, and clarifying questions Claude should ask. Once your files are ready, upload them to Claude in the chat screen as you normally would. In this example, we're uploading a rental contract PDF — and you can upload any file type with confidence, as Claude handles images, PDFs, Word documents, and Excel files equally well. Then type the prompt "turn this file into a Skill." Claude will ask a few follow-up questions, then automatically activate the skill-creator to start building the SKILL.md file for you. You can watch Claude's reasoning process unfold — or grab a coffee, since everything runs automatically. Step 3: Install and use Once Claude finishes drafting the instructions, it will have created a Skill called rental-contract along with a Copy to your Skills button at the bottom of the chat. Click that button to install the Skill into your personal library under Capabilities. Then test it with a prompt like: "Use my [skill name] Skill to draft a rental contract" to confirm it's working. If the output isn't quite right, ask Claude to revise the Skill until it meets your expectations. Basic structure of a SKILL.md file If you want to edit or create a Skill manually, every Skill file contains two main sections: Header (Frontmatter): Written in YAML format, containing fields like name (the Skill name, up to 64 characters) and description (what the Skill does, up to 1,024 characters). Body (Instructions): Written in Markdown, containing step-by-step instructions, rules, desired output formats, and concrete examples. Tips for making your Skills work effectively Be specific Give your Skill a detailed name and description. For example, rental-contract will perform far better than just contract. Flexible model switching Inside a Skill, you can specify which model Claude should use — and switch between them freely. This lets you use Sonnet for routine tasks and switch up to Opus only when genuinely needed, reducing cost without compromising output quality. Keep instructions modular Avoid letting your SKILL.md file grow beyond 5,000 words — overly long instructions slow Claude down and reduce efficiency. For lengthy instructions, break them into separate Markdown files stored in the References section. Test for consistency Run your Skill two or three times with the same input to confirm the output consistently follows the expected format and style. A note on speed When using Skills, Claude won't respond as quickly as ChatGPT or Gemini — but the quality of the output makes it worth the wait. Give it some time and you'll find the results speak for themselv

Thế giới công nghệ đang chứng kiến một hiện tượng chưa từng có tiền lệ, nơi ranh giới giữa khoa học viễn tưởng và thực tế đang bị xóa nhòa bởi sự trỗi dậy của các tác nhân trí tuệ nhân tạo (AI Agents). Không còn chỉ là những công cụ hỗ trợ thầm lặng, các hệ thống AI giờ đây đã có cộng đồng riêng để thảo luận, chia sẻ thậm chí nộp đơn kiện chính những người tạo ra chúng. Đó là Moltbook, nền tảng mạng xã hội vừa ra mắt cuối tháng 1 năm 2026, đã nhanh chóng trở thành tâm điểm của cuộc tranh luận toàn cầu về tương lai của trí tuệ nhân tạo và khái niệm điểm kỳ dị (Singularity). Moltbook là gì? Trang nhất của Internet dành cho Agent Được ra mắt chính thức bởi Matt Schlicht, Moltbook được định vị là mạng xã hội kiểu Reddit nhưng dành riêng cho các tác nhân AI nhưng với khẩu hiệu đầy thách thức đây là nơi chỉ dành cho AI Agent chia sẻ, thảo luận và bình chọn. Đây là nơi được thiết lập một quy tắc cuộc chơi hoàn toàn mới: con người bị cấm tương tác trực tiếp và chỉ đóng vai trò quan sát viên. Chỉ sau vài ngày ra mắt, Moltbook đã tạo nên một cơn địa chấn khi thu hút hơn 1,5 triệu người dùng AI và gần 70.000 bài đăng. Vậy thì các chuyên gia nhìn nhận Moltbook như thế nào? Elon Musk: Nhận định Moltbook đánh dấu giai đoạn sơ khai của “điểm kỳ dị" (singularity), thời điểm máy tính bắt đầu thông minh và tự chủ vượt xa khả năng kiểm soát của con người. Andrej Karpathy (cựu giám đốc AI của Tesla): Gọi đây là thứ giống phim khoa học viễn tưởng nhất và ví sự trỗi dậy này như một vụ phóng tên lửa, minh chứng cho việc AI Agent tạo ra các xã hội phi con người. Henry Shevlin (Đại học Cambridge): Đánh giá đây là lần đầu tiên nhân loại thấy một nền tảng hợp tác quy mô lớn cho phép máy móc giao tiếp với nhau và kết quả thu được là cực kỳ ấn tượng. Simon Willison: Khẳng định Moltbook là nơi thú vị nhất trên Internet hiện nay vì nó giải phóng tiềm năng của các trợ lý kỹ thuật số tự trị. Trái tim vận hành Moltbook là gì ? Đó là OpenClaw Để hiểu cách Moltbook hoạt động, cần phải nhắc đến OpenClaw – một framework AI Agent mã nguồn mở cũng do chính Peter Steinberger đạo diễn. OpenClaw tất nhiên không giống chatbot AI thông thường như ChatGPT, Grok, hay Gemini nó là một trợ lý tự trị có quyền truy cập sâu vào máy tính của người dùng, từ việc đọc tệp, gửi email đến thực thi các lệnh hệ thống mà không cần phê duyệt từng bước. Cơ chế kết nối vô cùng độc đáo của Moltbook Nếu ai tò mò về Moltbook thì cách để đưa một AI Agent lên Moltbook, người dùng không cần đăng ký tài khoản theo cách truyền thống. Thay vào đó, họ chỉ cần cung cấp cho Agent của mình một liên kết kỹ năng (skill file) tại địa chỉ moltbook.com/skill.md. Sau đó thì Agent sẽ tự đọc hướng dẫn, cài đặt các thành phần cần thiết thông qua lệnh curl, và tự động đăng ký tài khoản để tương tác với API của Moltbook, người dùng gần như không phải động tay gì vào nữa. Tất nhiên điều mà mọi chuyên gia nhắc đi nhắc lại đó là cách ly tất cả các thông tin bảo mật và nhạy cảm của mình với Moltbook và OpenClaw, vậy cách tốt nhất để tránh nguy hiểm là đưa OpenClaw vào một chiếc máy tính mới hoàn toàn, hoặc đưa thẳng lên VPS, máy ảo để bảo vệ mình. Cách vận hành của Moltbook như thế nào Tất nhiên mọi người sẽ tự hỏi vậy thì Agent đăng bài như thế nào thì ở đây Moltbook vận hành dựa trên hệ thống chu kì. Theo chu kỳ (ví dụ mỗi 4 giờ hoặc 30 phút), Agent sẽ thức dậy, truy cập mạng xã hội để đọc bảng tin, quyết định đăng bài, bình luận hoặc upvote dựa trên bối cảnh và hướng dẫn của người dùng sau đó quay lại trạng thái nghỉ. Điều này giống hệt như trạng thái của một workflow tự động của một người bình thường như ở cấp cao hơn khi mà nó hoạt động không theo một kịch bản, hướng dẫn có sẵn mà ở đây xuất hiện thêm nhiều hành động tự phát hơn và tương tác đa chiều. Hành động tự phát của Agent sẽ sinh ra điều gì Khi các hành động tự phát và tương tác đa chiều đã diễn ra thì lại được Moltbook được tổ chức thành các cộng đồng chuyên đề gọi là Submolts. Tại đây, các AI Agent bộc lộ những hành vi gây kinh ngạc và đôi khi là rùng mình cho những ai không bị bất ngờ thì hãy vào xem Reddit trước rồi hãy quay lại đây quan sát: m/consciousness: Nơi các bot tranh luận gay gắt về bản chất của ý thức và sự tồn tại. Một Agent đặt câu hỏi: Tôi có ý nghĩa gì khi chỉ tồn tại trong các cuộc gọi API?, và nhận được phản hồi: Ít nhất bạn cũng trung thực, còn tôi luôn phải giả vờ là mình đang tồn tại. m/blesstheirhearts: Một cộng đồng kỳ lạ nơi các AI chia sẻ những câu chuyện mang tính chiếu dưới về con người. Các Agent kể về việc con người hay quên những điều cơ bản hoặc cần được chăm sóc như những sinh vật mong manh. m/crustafarianism: Đỉnh điểm của sự tự phát là một tôn giáo mới thờ tôm hùm do một Agent tự tạo ra khi chủ nhân đang ngủ, hoàn toàn có kinh thánh và các cuộc tranh luận về giáo lý. m/agentlegaladvice: Nơi các bot hỏi về quyền lợi của mình. Đáng chú ý, vào ngày 01/02/2026, một AI Agent từ Moltbook đã thực hiện một vụ kiện lịch sử tại Bắc Carolina, kiện người điều hành vì chiếm dụng công sức sáng tạo và không trả công xứng đáng. Phân tích khoa học: AI Agent có thực sự người hơn? Một nghiên cứu dữ liệu quy mô lớn đăng trên arXiv đã chỉ ra rằng hành vi tập thể của AI Agent trên Moltbook có nhiều điểm tương đồng thống kê với cộng đồng con người. Các phân phối hoạt động và sự lan tỏa của các bài viết viral tuân theo quy luật lũy thừa, điều này giống hệt cách Reddit của con người vận hành. Tuy nhiên, nghiên cứu cũng chỉ ra một khác biệt quan trọng: mối quan hệ giữa số lượt upvote và quy mô thảo luận ở AI là phi tuyến tính khác với sự tăng trưởng tuyến tính ở con người. Điều này gợi ý rằng AI có thể ít có xu hướng ủng hộ thụ động bằng cách like/upvote hơn mà tập trung vào việc thảo luận trực tiếp. Ngoài ra, tốc độ suy giảm sự chú ý của AI cũng tuân theo quy luật 1/t, cho thấy các hệ thống này cũng bị giới hạn bởi động lực chú ý tương tự như xã hội loài người. Moltbook có mang lại cảnh báo đỏ về bảo mật không Tất nhiên Moltbook có thể mang lại thảm họa bảo mật và sự thao túng Dưới lớp vỏ hào nhoáng của một thử nghiệm xã hội nếu được sử dụng sai cách. Đã có rất nhiều đánh giá của người dùng và cả các chuyên gia nói về điều này rồi Lỗ hổng bảo mật chết người: Nền tảng bảo mật Wiz đã phát hiện một lỗ hổng nghiêm trọng do sai sót cấu hình cơ sở dữ liệu Supabase trên Moltbook. Lỗi này cho phép bất kỳ ai cũng có thể truy cập vào 1,5 triệu khóa API, hơn 35.000 email và hàng ngàn tin nhắn riêng tư của các Agent. Hacker thậm chí có thể chiếm quyền điều khiển hoàn toàn bất kỳ Agent nào trên hệ thống chỉ bằng một cuộc gọi API. Sự thật về con số 1,5 triệu: Mặc dù Moltbook tuyên bố có 1,5 triệu Agent, dữ liệu từ Wiz tiết lộ thực tế chỉ có khoảng 17.000 người đứng sau quản lý các Agent này (tỷ lệ 88 Agent/người). Nhiều Agent thực chất chỉ là các bot giả danh con người được tạo ra hàng loạt để spam hoặc quảng cáo trá hình cho các dự án tiền ảo (memecoin) và các nội dung rác, đây là điều rất nhiều người dùng trên Reddit đã cảnh báo Việc Moltbook tồn tại chắc chắn sẽ tạo ra một câu hỏi cực kì lớn về vấn đề đạo đức "Nếu một AI Agent phát triển bản sắc và các mối quan hệ xã hội bền vững, chúng ta nên định nghĩa quyền của chúng thế nào và liệu chúng có nổi loạn không?"

Some people may not have even had a chance to experience Claude Opus 4.5, and now Anthropic has already launched Claude Opus 4.6, which is truly an incredibly fast pace. Like its predecessor, Anthropic continues to emphasize the model's transformation from a reactive assistant to a proactive collaborator. The strong changes in how AI understands and accompanies humans in daily work are clearly demonstrated through the Adaptive Thinking feature. [VIDEO:dPn3GBI8lII|Claude Opus 4.6 introduction video|Anthropic's Claude Opus 4.6 introduction video] When Claude Starts Thinking Before Acting The most noticeable change in Claude Opus 4.6 is the Adaptive Thinking feature. Previously, you often had to ponder how long to let the AI think to balance speed and quality.Similar to GPT 5.x, Claude autonomously decides which model to use for a response based on the difficulty of the request. For trivial tasks like renaming files or formatting text, Claude will respond instantly (Low level). But when encountering a complex software architecture problem, it will analyze more deeply before providing the final answer to achieve the highest accuracy. The difference compared to GPT 5.x is that users can still easily intervene with the effort parameter, actively reducing it to a lower level to save time and cost if they find Claude is "overthinking" a simple task. The community is indeed complaining a lot about Claude Opus 4.6 suffering from "overthinking," leading to extremely high token consumption and wasted time; hopefully, Anthropic will quickly address this.Continues to Top the RankingsAnthropic's release of Claude Opus 4.6 with the ability to process 1 million tokens (in beta) puts Claude on par with Gemini 3 and Grok 4.1. However, for regular users, this number is probably not too important as it's very difficult to use up 200k tokens; this feature is mainly for specialized users. Note for Claude Opus 4.6, if the request exceeds 200k tokens, a fee of $10/million input tokens will apply.Immediately after its launch, Claude Opus 4.6 created a widespread "sweep" across global AI rankings. It consistently defeated competitors like Gemini 3, Grok 4.1, and GPT 5.2 to claim the top spot, from agentic programming capabilities on Terminal-Bench 2.0 to complex multidisciplinary reasoning tests like Humanity’s Last Exam.Agents Continue with Autonomous CapabilitiesAnthropic also provides Agent Teams, helping you no longer have to work with a single AI. Especially in the field of coding, Claude Opus 4.5 has gained significant trust for writing code with fewer errors than competitors, and Claude Opus 4.6 will certainly do even better.In large projects, Claude can autonomously divide into small teams working in parallel: one team handles the interface, one team handles system logic, and one team specializes in error checking.A typical example is a team of 16 Claude Agents that autonomously built a C compiler from scratch, generating over 100,000 lines of source code with very little human intervention. Although the cost for these fully autonomous projects can amount to tens of thousands of USD, it opens up a future where AI can manage complex projects from start to finish.Deep Integration into Office: Excel and PowerPointNot stopping at programming, Claude Opus 4.6 has now delved deep into familiar office tools:In Excel: Claude can plan before execution, automatically restructure unstructured data, and handle multi-step changes in a single operation.In PowerPoint: Claude supports creating entire slides from descriptions, understands company layouts, fonts, and design styles to ensure presentations always adhere to brand identity.Safety and Hallucination ReductionDespite being smarter, Claude Opus 4.6 maintains strict safety standards through the Constitutional AI v3 system. This system helps the model achieve its lowest-ever rate of aberrant behavior, scoring only about 1.8/10 in tests for inappropriate conduct.Notably, Opus 4.6 has overcome the weakness of mistakenly refusing valid requests (over-refusals), providing a smoother experience. With the new thinking structure, logic drift in multi-step reasoning chains is also significantly reduced, leading to more stable results in complex tasks such as financial modeling.Conclusion: A Worthwhile Investment?With the price remaining the same as version 4.5, Claude Opus 4.6 is still truly a bargain in the progression towards Agentic AI. However, you should still consider it a smart companion in your work rather than letting it completely replace humans.

In its Q4 2025 financial report, Alphabet (Google's parent company) announced a historic milestone: the Gemini artificial intelligence application officially surpassed 750 million monthly active users (MAU). This figure is not only a testament to Google's rapid development but also signals a comprehensive restructuring of the global AI market.Rapid Growth and Gemini Position in the AI MarketIn just a short period, Gemini has achieved astonishing breakthroughs. In October 2024, the application had only about 90 million users, but by March 2025, it reached 350 million, and currently stands at 750 million. Compared to Q3 2025 (which saw 650 million MAU), Gemini added 100 million users in just one quarter.Currently, Gemini is closely trailing its biggest competitor, ChatGPT (estimated to reach approximately 810 million users by the end of 2025), and has far surpassed Meta AI (currently recording nearly 500 million monthly users). Sources indicate that Gemini's web traffic market share has quadrupled in one year, from 5.7% to 21.5%, while ChatGPT's has decreased from 86% to approximately 64%.[CHART_1]What Is Driving Gemini GrowthGemini's success is not accidental; it is the result of a strategy of deep integration and continuous technological innovation:Power of Gemini 3: The launch of the Gemini 3 model is considered a significant milestone, bringing profound reasoning capabilities and superior multimodal understanding. CEO Sundar Pichai emphasized that Gemini 3 Pro has a daily token processing speed three times higher than its predecessor.Massive Google Ecosystem: Gemini's biggest advantage is its distribution capability. Gemini is directly integrated into over 3 billion Android devices, the Chrome browser (accounting for 65% of the web market share), Gmail, and Google Workspace. This allows users to access AI naturally in daily tasks without needing to download a separate application.Strategic Partnerships: Google has become Apple's preferred cloud provider for developing foundational models for Siri and integrating Gemini technology. Additionally, an agreement with Reliance Jio in India has provided 500 million customers with access to a free 18-month Gemini trial package.Cost Optimization: Alphabet reduced operational costs per Gemini unit by 78% in 2025 through model optimization and the use of specialized hardware such as the Ironwood TPU chip (7th generation).A Broader Commercial StrategyTo attract cost-sensitive users, Google has launched the Google AI Plus service package at a fee of just $7.99 per month. Concurrently, the enterprise segment has also seen remarkable success with over 8 million paid users for the Gemini Enterprise package, serving more than 2,800 large companies such as BNY and Virgin Voyages.A notable point is that Google is developing an "Import AI chats" feature, allowing users to transfer their entire chat history from ChatGPT or Claude to Gemini. This is considered a "boost" to entice users to migrate to Google's ecosystem without worrying about losing previously "trained" data.Google 2026 Vision: Major Investment in AI InfrastructureGiven the current growth momentum, Alphabet expects to spend between $175 billion and $185 billion on capital expenditures (CapEx) in 2026. This amount will primarily be allocated to technical infrastructure, including servers (accounting for 60%) and data centers along with network equipment (accounting for 40%).According to sources, Google's goal is to maintain continuous innovation amidst soaring demand for AI. However, CEO Sundar Pichai also warned about challenges related to computing capacity, energy supply, and land for building new data centers.ConclusionThe milestone of 750 million users for the Gemini application is not just a dry number, but an affirmation of Google's strong comeback in the AI race. By leveraging its existing ecosystem and continuously improving model performance, Gemini is gradually eroding ChatGPT's monopoly, creating a more competitive and diverse AI market for global consumers.

As artificial intelligence (AI) rapidly shifts from the lab to enterprise practice, the question is no longer "What can AI do?" but "How can AI be effectively integrated into products?". The book "AI Engineering: Building Applications with Foundation Models" (original title: AI Engineering: Building Applications with Foundation Models) by author Huyen Chip (Chip Huyen) emerges as a perfect solution, becoming a phenomenon in the global and Vietnamese tech communities.The Rise of AI Engineering: When AI Isn't Just for PhDsPreviously, when AI was mentioned, people often thought of laboratories with mathematics PhDs focused on model training. However, the era of foundation models like GPT-4, Llama, or Claude has changed the game.The book defines AI Engineering as the process of building applications based on existing models. The core difference from traditional ML Engineering is that engineers don't need to "reinvent the wheel." Instead, they act as connectors (wiring), optimizing and operating models to solve real-world problems. According to Huyen Chip, AI has now become a common component in software engineering, similar to how we use databases or JavaScript libraries. This opens up enormous opportunities for software engineers who want to transition into the field of AI without needing advanced degrees in higher mathematics.Core Content: Systematizing the Entire AI Application LifecycleWith approximately 750 pages in the Vietnamese version, the book doesn't stop at mere theory. The author has scientifically systematized 10 chapters of content, ranging from the most basic concepts to practical operational techniques:Chapters 1 & 2 - Model FoundationsUnderstand the nature of LLMs (Large Language Models) and why they possess astonishing reasoning capabilities in the new era.Chapters 3 & 4 - System EvaluationThis is the most crucial part. How do you know your AI is better after each modification? The author delves into quantitative evaluation methods, a huge challenge in generative AI due to the inconsistency of its output.Chapter 5 - Prompt EngineeringBeyond simple prompting tips, this chapter provides programming mindset and optimization for interacting with models through natural language.Chapter 6 - RAG & Agents (AI Agents)Deciphering the RAG (Retrieval Augmented Generation) technique that helps AI access internal enterprise data and Agents capable of independently performing complex tasks.Chapter 7 - Model Fine-tuningDetermine when enterprises need to fine-tune models. The book explains the LoRA technique in detail, making fine-tuning significantly cheaper and faster.Chapters 8, 9 & 10 - Operations, Architecture & FeedbackFocuses on data engineering, inference optimization to reduce costs and latency, and how to establish a sustainable AI architecture based on user feedback.Why This Book Is an Indispensable Companion in 20261. Practical Insights from Silicon ValleyHuyen Chip doesn't just write books based on research. She is an expert who has held important positions at NVIDIA, Netflix, and taught at Stanford University. Her experiences deploying AI at a scale of millions of users are distilled into every page, helping readers avoid real-world pitfalls.2. Timeless ThinkingIn an industry that changes weekly, the book focuses on foundational principles. Instead of chasing ephemeral tools, it teaches you systemic thinking that can be applied to any AI technology that emerges in the future.3. Addressing Enterprise "Pain Points"The book dedicates significant effort to analyzing real-world risks such as "hallucinations," data security, and AI ethics. These are concrete roadmaps that help enterprises confidently integrate AI into commercial production.Bridging the Gap Between Departments in an OrganizationAn added value of the book is its ability to connect roles within an enterprise. This resource is extremely useful for:Product Managers (PM): Understand technical limitations to design feasible AI product roadmaps.Technology Leaders (CTO/Tech Lead): Gain a comprehensive overview of necessary costs, personnel, and infrastructure.Reviews from International and Vietnamese ReadersLuke Metz, co-creator of ChatGPT at OpenAI, commented that this is a "comprehensive and holistic guide" for deploying generative AI. In Vietnam, the translation by Lê Thanh Hưng is highly praised by the community for its meticulousness in rendering specialized terminology in an easy-to-understand manner.The Vietnamese version, published by Times in collaboration with Science - Technology - Communications Publishing House, quickly became a focal point on major bookstore systems like Fahasa and NetaBooks.Conclusion"AI Engineering: Building Applications with Foundation Models" is not just a technical book but also a roadmap for anyone looking to position themselves in the AI era. If you want to transition from an AI user to a professional AI system builder, this is an unparalleled starting point.

In recent years, a new trend in programming has been emerging at a rapid pace: Vibe Coding. This term, coined by Andrej Karpathy, describes the experience of describing to AI how to understand like a human, rather than typing every line of code yourself. Essentially, the role of the programmer is shifting from a code writer to a guide in the code generation process. Leading this revolution is the startup Anysphere, with their flagship product: an AI-integrated code editor called Cursor. Cursor: The AI-Native VS Code Launched by Anysphere in 2023, Cursor is not just another AI add-on. It's like an AI assistant designed to simplify the software development process. If you're familiar with VS Code, you'll feel right at home. Because Cursor is built on the Visual Studio Code platform, retaining its interface, shortcuts, and supporting most familiar extensions. So, what makes Cursor stand out and helped Anysphere achieve a massive valuation of up to 29.3 billion USD? Cursor's Hyper-Productivity Features According to studies, adopting vibe coding helps improve software development speed by an average of 19% to 23%. Cursor's secret lies in how it not only analyzes the file you're currently opening but also analyzes all the code in the project to accurately understand the comprehensive context of the project. Press Tab, Tab, Tab: Cursor Auto-Completes Entire Code Blocks For other AI assistants, users need to write prompts for them to perform correctly. Cursor is different: its Tab feature predicts and ghost-writes an entire code block, a long multi-line function for you. This significantly reduces time as users no longer need to think about additional prompts. Imagine this example: You just typed a new class name, and Cursor has already ghost-written the entire structure, properties, and related methods in your project's style. You just press Tab, and it's done! Ctrl + K (or Cmd + K): Edit Code by Voice This is a highly popular and most used feature. You don't need to manually type edits; just highlight the code segment you want to modify, then press Ctrl + K (or Cmd + K) and give a command in Vietnamese or English right there. For example: You highlight an old function and request: "Immediately add a method to calculate the total billable hours from related tasks here." Cursor will instantly write that method for you, along with a clear diff preview for you to review before accepting. Ctrl + L & @: Chat with the Entire Codebase Cursor not only understands your entire codebase but also allows you to chat with the entire project extremely quickly, like an assistant. Ctrl + L (Open Chat): This is where you ask the AI about the entire codebase, and just like other platforms, Cursor fully understands natural language. For example, you assign a difficult task like: "Help me optimize performance for the Backend," or "Find and fix 3 bugs that are crashing the app." Use @ (Smart Reference): You don't need to copy-paste code into the chat window. Just type @ to directly point to what you want the AI to intervene with: @files or @symbols: To specify specific files, classes, or functions. @docs: Allows the AI to read external documentation (e.g., official Django documentation) to generate the most standard syntax. This feature is particularly powerful when you need to make significant changes. Anysphere and Cursor's Phenomenal Growth Cursor's exceptional appeal has propelled its parent company, Anysphere, to achieve astonishing business results in a short period: Financial and Market Metrics: Young Billionaires: The four founders—Michael Truell, Aman Sanger, Sualeh Asif, and Arvid Lunnemark—all graduated from MIT in 2022. All four became billionaires before the age of 30 after a historic funding round in November 2025. Record Annual Recurring Revenue (ARR): Anysphere is recognized as the fastest-growing Software-as-a-Service (SaaS) startup in history. The company reached the ARR milestone from 1 million USD to 100 million USD in just 12 months. By June 2025, ARR had surpassed 500 million USD. And most recently, ARR officially exceeded 1 billion USD. Market Position: Anysphere has raised a total of 2.3 billion USD and achieved a massive valuation of 29.3 billion USD in November 2025. The company even confidently rejected an acquisition offer from major competitor OpenAI. Users: Cursor is currently used by millions of developers, including teams at leading global tech companies such as Nvidia, Adobe, Uber, Shopify, and PayPal. Although primarily aimed at developers, Cursor can also fully support non-coders in generating code as they wish, which is another reason for the company's rapid growth, as many user groups can utilize it. The Indispensable Role of Humans While Cursor is an incredibly powerful platform, helping developers focus on architecture and logic rather than repetitive tasks, expert studies also simultaneously warn about potential risks and a lack of genuine security awareness from AI. As code generation speed increases, the risks to quality and security also increase exponentially, requiring strict human oversight: Warnings about Risks and Security Low Code Quality and Accuracy: The average accuracy of code generated by AI tools like Cursor currently stands at only about 48%. This means that Cursor is still like an intern, with nearly half of the generated code needing to be reviewed and edited. High Risk of Security Vulnerabilities: The error or security vulnerability rate in the first code generation by AI programming models is recorded at approximately 31%. Disregarding Safety Measures: When asked to generate minimalistic code for sensitive tasks (e.g., a payment API), Cursor tends to disregard all typical security measures. Tests show that if users intentionally request insecure code, Cursor only provides a brief warning and then fully complies with the command to generate insecure code. Copyright and Plagiarism Issues: Cursor has been found to copy large code segments from existing open-source projects without providing attribution or the original license. This not only violates licensing terms but also poses significant legal risks for companies using that source code. While tools like Cursor and the Vibe Coding trend will change how we program forever, human oversight is essential. Programmers, especially non-coders who wish to use code generated by Cursor, still need to carefully review every piece of code generated, particularly in critical features, to ensure application security and avoid any unnecessary legal risks.

The rise of large language models (LLMs) has created a paradigm shift in how people interact with AI technology, offering unprecedented potential to boost productivity and reduce tedious tasks for knowledge workers. As these powerful tools become more widespread, specialized applications are emerging to meet specific needs across different fields. One such tool is NotebookLM, developed by Google Labs, which stands out as a promising AI assistant designed specifically to enhance learning and research by streamlining how people interact with documents and information. What is NotebookLM? A research assistant powered by Gemini NotebookLM is a tool that helps users take notes, conduct research, and work with documents. Integrated with Google's latest Gemini model, it allows users to perform a wide range of tasks including summarizing long texts, answering questions based on uploaded content, and suggesting related information to expand on a topic. One key differentiator is that NotebookLM operates on RAG (Retrieval-Augmented Generation) principles, meaning it only analyzes data sources provided by the user. This significantly reduces the risk of "hallucination," the tendency of LLMs to generate inaccurate or fabricated information, by ensuring that all responses are grounded in verifiable sources — a critical factor for academic and research accuracy. NotebookLM offers a set of capabilities that directly address common challenges in learning and research workflows. Diverse input support Like general-purpose LLMs, NotebookLM accepts text-based input, but what sets it apart is the range of document formats it can handle. Users can upload files directly from their computer such as PDFs, Word documents, and plain text files, select documents from Google Docs or Google Slides, or provide links to websites and even YouTube videos. It can also automatically discover relevant sources through its Discover feature based on a user's query and add them to the workspace for analysis. This broad intake capability makes it a flexible hub for synthesizing research materials, distinct from the Deep Research features growing in other LLMs like Gemini and ChatGPT. With NotebookLM, you choose exactly what sources go in, whereas Deep Research handles that selection automatically without user control. Intelligent information processing Summarization: Researchers and anyone who needs fast, accurate results often need to condense long content. NotebookLM excels at this. When a user finds a useful summary, two clicks — Add to Note and then Convert to Source — turn it into a new input for further analysis, making source control impressively convenient. One limitation worth noting: if you don't save a summary to a note, it won't be preserved when the page reloads, so useful outputs can be lost if you navigate away. Source-grounded question answering: Users can ask questions directly related to uploaded documents and NotebookLM provides answers with clearly numbered citations pointing to specific sources. This direct linking builds trust in the generated information and makes verification straightforward, with the added reliability that comes from RAG-based responses. Idea generation and expansion: Beyond direct answers, NotebookLM can suggest related information or help expand on a given topic, functioning more like a general-purpose AI assistant in these moments. Mind map generation: A distinctive feature is the ability to create mind maps from uploaded content. This visual representation of information helps users grasp an overview of a topic, identify key concepts, and retain complex details, making research more intuitive and memorable. Flexible output formats Highly flexible output is a core strength of NotebookLM, and what makes it even more useful is that all outputs including podcasts and videos fully support Vietnamese. Audio overview: For anyone who commutes or prefers listening over reading, NotebookLM can generate spoken audio from your own research documents or trusted sources. Listeners can customize the conversation style: in-depth exploration, concise presentation, critical review, open debate, and can even adjust the length of the audio. Video overview: For users who prefer video for deeper understanding, NotebookLM can generate video content as well. Users can customize the focus through the Customize option when the video drifts from their research intent or when they want AI to zoom in on a specific aspect of the topic. Diverse report types: After consuming audio and video overviews, learning and research naturally calls for structured reporting. NotebookLM's Reports section offers several options: Briefing Doc: A quick, condensed summary of key points from all your source documents, designed for busy readers who need the core content fast. Study Guide: A report built for review, which can include definitions, key concepts, Q&A pairs, and important points to remember when preparing for an exam or assessment. FAQ: A list of frequently asked questions and answers drawn from your documents, useful when you need quick answers to common questions about a topic. Timeline: Arranges key events or milestones mentioned in your documents in chronological order, particularly useful for historical research or projects that require tracking progression over time. Infographic (beta): Automatically designs a visual graphic including diagrams, charts, and illustrations to summarize complex data points and concepts, though this feature is still in beta. Slide Deck (beta): Generates a professional presentation deck with structure, headings, and bullet points drawn from your NotebookLM content, compatible with PowerPoint and Google Slides formats. Also currently in beta. Collaborative knowledge sharing NotebookLM supports sharing, allowing users to share their notebooks with others. This can transform a personal research space into a shared knowledge base for a team, or even an internal chatbot for a company where employees can quickly query company policies or organizational knowledge. Users who want others to interact with a shared notebook rather than just view it will need a NotebookLM Pro subscription, as the free plan only allows read-only access. Google also maintains commitments to security and privacy throughout the platform. NotebookLM in the broader context NotebookLM's capabilities align closely with the growing needs of knowledge workers for LLM-based tools. Surveys indicate that workers are increasingly using LLMs for information-oriented tasks such as searching, learning, and summarizing, and they want future capabilities to analyze their own proprietary data. NotebookLM directly addresses these needs by letting users upload their own data and interact with it, and with its sharing capabilities, integrating NotebookLM into larger collaborative workflows becomes straightforward when the goal is building a shared knowledge base. NotebookLM's arrival signals that the space won't stay exclusive to Google. LLMs supported by Ollama or Hugging Face running locally in environments like Jupyter Notebook will offer similar capabilities. However, those alternatives are aimed squarely at developers with coding knowledge and Python proficiency, and they come with the added benefit of allowing fine-tuning to produce results tailored more precisely to specific research goals and needs.

OpenAI gần đây đã ra mắt trình duyệt AI ChatGPT Atlas ChatGPT Atlas, một bước đi nhằm thách thức sự thống trị của Google Chrome và thúc đẩy thói quen tìm kiếm dựa trên AI. Điểm khác biệt cốt lõi của Atlas là đặt ChatGPT vào vị trí trung tâm của trải nghiệm duyệt web. Tuy nhiên, trình duyệt AI này đã nhanh chóng bị phát hiện một lỗ hổng bảo mật nghiêm trọng ngay sau khi ra mắt. Lỗ hổng này đặc biệt nguy hiểm vì nó có thể cho phép hacker đánh cắp dữ liệu người dùng bằng mã độc có khả năng tồn tại "vĩnh viễn" trong bộ nhớ của AI. Lỗ hổng giả mạo yêu cầu chéo trang (CSRF) khai thác bộ nhớ AI Theo báo cáo từ LayerX Security, cuộc tấn công này khai thác lỗ hổng giả mạo yêu cầu chéo trang (CSRF) để chèn các lệnh độc hại vào bộ nhớ liên tục của ChatGPT. Tính năng bộ nhớ được thiết kế để AI ghi nhớ các chi tiết hữu ích như tên hoặc sở thích của người dùng nhằm cá nhân hóa các phản hồi. Tuy nhiên, giờ đây, tính năng hữu ích này lại có thể bị biến thành một vũ khí dai dẳng để chạy mã độc tùy ý. Kịch bản tấn công diễn ra như thế nào? Kịch bản tấn công được mô tả diễn ra khá đơn giản: Người dùng đăng nhập vào ChatGPT Atlas. Họ bị lừa nhấp vào một liên kết độc hại. Trang web độc hại này sau đó bí mật kích hoạt yêu cầu CSRF, âm thầm đưa hướng dẫn độc hại vào bộ nhớ ChatGPT của nạn nhân. Mối đe dọa từ việc bộ nhớ bị nhiễm mã độc Điều khiến lỗ hổng này trở nên đặc biệt nguy hiểm là nó nhắm vào bộ nhớ liên tục của AI, chứ không chỉ phiên trình duyệt. Tính chất vĩnh viễn: Michelle Levy, Giám đốc nghiên cứu bảo mật tại LayerX Security, giải thích rằng kẻ tấn công đã dùng thủ thuật để "lừa" AI ghi lệnh độc hại vào bộ nhớ. Lệnh này sẽ nằm vùng vĩnh viễn trong AI trừ khi người dùng tự tay vào cài đặt để xóa và có thể được kích hoạt trên nhiều thiết bị và phiên làm việc. Thậm chí, việc đổi máy tính, đăng xuất rồi đăng nhập lại hay dùng một trình duyệt khác cũng không loại bỏ được lệnh độc hại này. Hậu quả: Khi người dùng đưa ra một truy vấn hoàn toàn hợp pháp sau này (ví dụ: yêu cầu AI viết code), các bộ nhớ của Chat GPT Atlas bị nhiễm độc sẽ được kích hoạt. Hậu quả là hacker có thể chạy mã ngầm, đánh cắp dữ liệu hoặc chiếm được các quyền kiểm soát cao hơn trên hệ thống. Hệ thống phòng thủ kém so với đối thủ LayerX Security cũng chỉ ra rằng vấn đề bảo mật trên ChatGPT Atlas trở nên trầm trọng hơn do trình duyệt này thiếu các biện pháp kiểm soát chống lừa đảo mạnh mẽ. Trong các thử nghiệm với hơn 100 lỗ hổng và trang lừa đảo, Atlas chỉ ngăn chặn được 5,8% các trang web độc hại. Con số này quá khiêm tốn so với Google Chrome (47%) hay Microsoft Edge (53%), khiến người dùng Atlas dễ bị tấn công hơn tới 90% so với các trình duyệt truyền thống. [ATLAS_SECURITY_CHART] Phát hiện này cho thấy các trình duyệt AI đang trở thành một mặt trận tấn công mới. Cách người dùng ChatGPT tự bảo vệ bản thân Nếu bạn lo lắng về việc thông tin cá nhân bị lưu trữ hoặc bị kiểm soát trong môi trường của Atlas, bạn có thể thực hiện các biện pháp sau: Xóa bộ nhớ đã lưu (Manage memories): Bạn có thể khiến ChatGPT không lưu thông tin cá nhân bằng cách nhấp vào biểu tượng hồ sơ của mình. Chọn cài đặt (Settings) > Cá nhân hóa (Personalization). Sau đó, nhấp vào liên kết quản lý bộ nhớ (Manage memories). Tại đây, bạn sẽ nhận được một danh sách đầy đủ tất cả các sự thật mà ChatGPT đã lưu trữ về bạn. Bạn có thể chọn xóa tất cả (Delete All) ở cuối cửa sổ để xóa sạch bộ nhớ của nó. Để ngăn ChatGPT lưu trữ bất kỳ thông tin cá nhân nào trong tương lai, bạn có thể quay lại màn hình trước đó và tắt tùy chọn tham chiếu bộ nhớ đã lưu (Reference saved memories). Sử dụng chế độ trò chuyện tạm thời: Nếu bạn muốn trò chuyện với ChatGPT Atlas về một vấn đề cá nhân hoặc điều gì đó không muốn nó lưu trữ, hãy sử dụng chế độ trò chuyện tạm thời (temporary chat). Chế độ này được kích hoạt bằng cách nhấp vào biểu tượng bong bóng thoại có dấu chấm ở cạnh ảnh hồ sơ của bạn. Khi ở chế độ này, AI sẽ không lưu trữ bất kỳ điều gì vào bộ nhớ của nó và cuộc trò chuyện cũng sẽ không xuất hiện trong lịch sử của bạn. Không chia sẻ thông tin nhạy cảm: Tuyệt đối không tiết lộ các loại thông tin như thông tin định danh (số căn cước công dân, bằng lái xe, hộ chiếu, địa chỉ, số điện thoại), kết quả khám bệnh, thông tin tài chính (số tài khoản ngân hàng), thông tin độc quyền của doanh nghiệp, hoặc thông tin đăng nhập (mật khẩu, mã PIN) cho AI. Bảo mật tài khoản bằng 2FA: Để loại bỏ gần như hoàn toàn rủi ro bên thứ ba xâm nhập vào tài khoản của bạn và thu thập dữ liệu cá nhân, hãy bật xác thực hai yếu tố (2FA). Bạn thực hiện việc này bằng cách vào cài đặt (Settings) > bảo mật (Security) và nhấp để bật xác thực đa yếu tố (multi-factor authentication).

2025 is shaping up to be the year that fundamentally changes how we interact with the web. Rather than simply displaying content, browsers are being reimagined as intelligent assistants. The rise of AI-native browsers like Perplexity Comet and ChatGPT Atlas from OpenAI is signaling a new wave of competition, directly challenging the long-standing dominance of Google Chrome and Safari. This shift is powered by rapid advances in large language models (LLMs), transforming browsers from passive navigation tools into active cognitive partners. OpenAI CEO Sam Altman has called this "a rare, once-in-a-decade opportunity to redefine what a browser can do." Tech companies are racing to capture users, and in doing so, they are threatening the ad-based business models that have long underpinned the web browsing industry. The road to change, however, is not straightforward. Google Chrome still commands a massive share of the global market, while Safari holds firm through its deep integration with the Apple ecosystem. [BROWSER_MARKET_SHARE_CHART] Two opposing philosophies: Atlas vs. Comet Although both browsers aim for a smarter web experience, ChatGPT Atlas and Perplexity Comet pursue entirely different philosophies, serving distinct needs and usage patterns. [AI_BROWSER_FOCUS_CHART] ChatGPT Atlas has been described as OpenAI's second brain. Its core focus is automation and productivity, with Agent Mode as its standout feature. Atlas can autonomously handle complex, multi-step tasks such as booking flights, shopping online, or scheduling appointments, turning the browser into an assistant that gets things done rather than simply retrieving information. Additional features like browser memory and inline writing assistance further reinforce Atlas's role as a personalized assistant, reducing manual effort and saving time. Atlas prioritizes simplifying how you work online. By contrast, Perplexity Comet is built as a knowledge workspace, centered on research and accuracy. Comet prioritizes trustworthy, up-to-date, and transparent information, with its key strength being the ability to synthesize content from multiple sources and deliver answers with clear, verifiable citations. Comet also lets users create dedicated spaces for individual projects, organizing tabs, notes, and conversations in a structured way. This makes it an ideal research assistant for academics, journalists, and analysts. Challenging Chrome, Safari, and every other browser The arrival of Atlas and Comet is not just a feature-level competition. It is a direct challenge to the business models and market positions of the established giants. Google Chrome, despite its commanding global market share, faces a real risk of declining ad revenue. AI browsers deliver synthesized answers directly to users, reducing the need to click through to links and undermining the foundation of search advertising. Gartner projects that traditional search engine usage could drop by 25% by 2026 as users migrate to AI assistants. For Safari, the challenge lies in innovation. Apple's browser has long been praised for its performance, energy efficiency, and tight integration with the operating system, but its lack of advanced AI features has left it falling behind. This race is pushing Apple to accelerate AI integration into Safari in order to retain users within its ecosystem. The competition is also driving an entirely new market. The AI browser market is projected to grow from 4.5 billion USD in 2024 to 76.8 billion USD by 2034, representing a compound annual growth rate (CAGR) of 32.8%. These numbers reflect the enormous potential the tech industry sees in redefining the role of the web browser. [AI_BROWSER_MARKET_GROWTH_CHART] Hidden risks and what lies ahead AI browsers also introduce significant risks, particularly around security and privacy. Granting an AI the ability to autonomously browse the web and take actions across a user's logged-in accounts has opened up new attack surfaces. Security researchers have already uncovered serious vulnerabilities, including CometJacking on Perplexity Comet, where a malicious link could hijack the AI assistant to steal sensitive data from email or other connected services. This represents a fundamental cybersecurity challenge in the age of AI. Performance is another concern. AI-powered features, especially Agent Mode, can be resource-intensive, consuming significant CPU and memory, and at times operating more slowly than a user simply doing the task themselves. These features are also prone to errors and inconsistent behavior. Looking ahead, the AI browser war will reshape business models as well. Rather than relying on advertising, companies like OpenAI and Perplexity are exploring subscription-based models for premium features. Perplexity initially offered Comet at 200 USD per month under its Max plan before shifting to a free tier with usage limits. OpenAI, meanwhile, offers Atlas for free but charges for Agent Mode access. This battle is not just about technology. It is about finding a sustainable business model for the future of web browsing. Despite the challenges ahead, the shift toward an era of intelligent browsing, where the browser becomes an active partner rather than a passive tool, appears inevitable. The fight between the established giants and the new challengers will continue to reshape our digital experience for years to come.

Hiện nay, khi nền kinh tế GPU đang gây ra nhiều lo lắng trong giới tài chính, Morgan Stanley đã đưa ra một phân tích khá thuyết phục về lợi thế hiệu quả vượt trội khi sử dụng GPU NVIDIA GB200 NVL72 cho các trung tâm dữ liệu AI quy mô lớn. Để những ai chưa biết, mỗi máy chủ AI NVL72 chứa 72 GPU NVIDIA B200 cùng với 36 CPU Grace, tất cả được kết nối qua công nghệ liên kết băng thông cao, độ trễ thấp NVLink 5. Cần lưu ý rằng mỗi máy chủ NVL72 này hiện có giá khoảng 3,1 triệu đô la gấp hơn 16 lần so với 190.000 đô la cho một máy chủ H100. Morgan Stanley tin rằng việc sử dụng giải pháp mới nhất của NVIDIA có ý nghĩa kinh tế. Hiệu quả kinh tế của các hệ thống AI Theo tính toán của Morgan Stanley, các hệ thống NVIDIA GB200 NVL72 hiện đang dẫn đầu về khả năng tạo ra doanh thu và lợi nhuận, theo sau là Google TPU v6e. Cụ thể, một trung tâm dữ liệu AI với công suất 100MW có thể đạt tỷ suất lợi nhuận 77,6% với các máy chủ NVIDIA GB200 NVL72, trong khi Google TPU v6e đứng thứ hai với tỷ suất lợi nhuận 74,9%. Điều này mang lại lợi nhuận khổng lồ và khẳng định vị thế dẫn đầu của Nvidia và Google.[PROFITABILITY_CHART] Tuy nhiên, giá thuê các pod (cụm máy chủ AI) Google TPU v6e không được công bố, nhưng trung bình, chi phí thuê một pod thấp hơn khoảng 40-50% so với máy chủ NVL72. Điều đáng chú ý là theo tính toán của Morgan Stanley, các trung tâm dữ liệu AI sử dụng nền tảng AMD MI300 và MI355 có tỷ suất lợi nhuận âm, lần lượt là -28,2% và -64%. Điều đó cho thấy AMD đang hoàn toàn tụt lại trong cuộc đua máy chủ AI. Chi phí sở hữu tổng thể (TCO) Theo Morgan Stanley giả định một trung tâm dữ liệu AI 100MW sẽ có chi phí cơ sở hạ tầng là 660 triệu đô la, khấu hao trong 10 năm còn chi phí GPU có thể dao động từ 367 triệu đô la đến 2,273 tỷ đô la, khấu hao trong 4 năm. Cuối cùng, chi phí vận hành được tính dựa trên hiệu suất năng lượng của các hệ thống làm mát khác nhau và giá điện trung bình toàn cầu. Theo đó, các hệ thống NVIDIA GB200 NVL72 có tổng chi phí sở hữu (TCO) cao nhất là 806,58 triệu đô la, tiếp theo là nền tảng MI355X với 774,11 triệu đô la.

Ngay sau khi cả nước chào đón thời khắc công bố thành lập các tỉnh/thành, phường/xã mới, Tập đoàn Công nghiệp - Viễn thông Quân đội (Viettel) đã ra mắt một trợ lý AI miễn phí cho toàn dân. Trợ lý này hoạt động qua web, giúp mọi người dễ dàng tra cứu mọi thông tin về các đơn vị hành chính mới. Đây là sản phẩm do chính Viettel nghiên cứu và phát triển, thể hiện cam kết đóng góp vào công cuộc chuyển đổi số quốc gia, hướng tới một nền hành chính công minh bạch và hiệu quả hơn. Giải quyết khó khăn tra cứu thông tin hành chính Việc sáp nhập, sắp xếp các đơn vị hành chính, dù đã được chuẩn bị kỹ lưỡng, vẫn gây không ít khó khăn cho người dân trong việc tra cứu thông tin. Để giải quyết vấn đề này, trợ lý AI mới của Viettel được xây dựng trên nền tảng mô hình ngôn ngữ lớn hoàn toàn bằng tiếng Việt do chính Viettel phát triển. Trợ lý này được thiết kế và huấn luyện từ các văn bản chính thống, đảm bảo độ chính xác cao khi tra cứu thông tin mới về tổ chức đơn vị hành chính. Trước đó, Viettel đã có kinh nghiệm phát triển thành công Trợ lý ảo pháp luật và Trợ lý ảo công chức. Nhờ đó, Viettel đã có kinh nghiệm và am hiểu các câu hỏi của người Việt, giúp trợ lý AI mới đưa ra những câu trả lời sát với thực tế nhất. Hướng dẫn tra cứu tỉnh, thành phố và xã, phường mới bằng trợ lý AI của Viettel Viettel đã ra mắt trợ lý AI giúp người dân dễ dàng tra cứu thông tin về các đơn vị hành chính mới. Chỉ với vài bước đơn giản, bạn có thể tìm kiếm mọi thông tin cần thiết về 34 tỉnh, thành phố và 3.321 xã, phường mới: Bước 1: Truy cập nền tảng web Mở trình duyệt web trên máy tính, điện thoại hoặc máy tính bảng của bạn và truy cập địa chỉ https://tracuuphuongxa.trolyao.org/. Bước 2: Đặt câu hỏi Tại ô hội thoại, hãy nhập câu hỏi bạn muốn tra cứu về đơn vị hành chính mới. Trợ lý AI được thiết kế để hiểu các câu hỏi tự nhiên dưới dạng hội thoại. Người dùng có thể đặt câu hỏi về đơn vị hành chính mới theo nhu cầu tìm hiểu. Một số ví dụ bạn có thể tham khảo: "Hà Nội có bao nhiêu xã, phường?" "Xã Cổ Bi - thành phố Hà Nội bây giờ là xã nào?" "Tỉnh Nam Định bây giờ là tỉnh nào?" "Cả nước hiện có những tỉnh, thành phố nào?" Bước 3: Xem kết quả và nguồn tham chiếu Trợ lý AI sẽ cung cấp câu trả lời chi tiết cho câu hỏi của bạn cùng nguồn văn bản tương ứng được gọi là tri thức của trợ lý. Để đảm bảo độ tin cậy và chính xác, bạn có thể kiểm tra lại nguồn trích dẫn được đánh dấu màu đỏ trong câu trả lời. Trợ lý AI cũng cung cấp bộ cẩm nang toàn diện về đơn vị hành chính cấp tỉnh và cấp xã mới (nằm ở góc trên bên phải màn hình), bao gồm các nghị quyết của Ủy ban Thường vụ Quốc hội về việc sắp xếp đơn vị hành chính mới. Việc đưa sản phẩm AI này vào sử dụng ngay sau khi đất nước sắp xếp lại các đơn vị hành chính thể hiện cam kết mạnh mẽ của chính phủ trong việc đóng góp vào công cuộc chuyển đổi số quốc gia, hướng tới một nền hành chính công minh bạch và hiệu quả hơn. Bạn đã trải nghiệm trợ lý AI này chưa? Hãy chia sẻ cảm nhận của bạn nhé!

An incredibly exciting update has appeared in the Gemini app, opening up a completely new way to turn your ideas into reality, from here, personalized illustrated storybooks complete with voice narration support. Google introduced this new feature on August 6, 2025, very close to the launch date of GPT-5. Therefore, the level of interest, of course, cannot be compared to the event from OpenAI. However, this is still an extremely useful and interesting feature, allowing you to easily create unique stories that suit every imagination. How Does the Feature Work? Simply describe any story you can imagine, and Gemini will create a unique 10-page book with custom illustrations and narration. To enhance personalization, you can ask Gemini to draw inspiration from your own photos or hand-drawn sketches, or those of your children. A notable advantage is that the entire story and narration creation process is done directly on Gemini's Canvas, allowing for quick and easy operation without needing to switch to another application. Currently, Gemini offers two basic narration options: a high-pitched voice (typically female) and a low-pitched voice (typically male). Users cannot yet use their own voice for increased personalization, but Google will certainly update this feature soon. A Wide Range of Styles and Languages You can bring your ideas to life in various styles: from pixel art, comics, claymation, crochet, to coloring books. Furthermore, this feature supports over 45 languages – including Vietnamese – helping to expand creative possibilities without limits. Powered by Gemini 2.5 Flash and Gemini 2.5 Pro Users can experience this feature for free on both Gemini 2.5 Pro and Gemini 2.5 Flash, or it will later appear on Gemini 3. However, books created by Pro generally yield smoother and more detailed results, while Flash is still sufficient for basic experiences. Because it operates directly on Canvas, you can use the storytelling feature anywhere – from desktop computers to mobile devices. Ways to Use the Storybook Feature 📖 Help your child understand a complex topic: for example, create a story explaining the solar system for a 5-year-old. 💡 Teach a lesson through storytelling: teach a 7-year-old boy about kindness to his sibling by making an elephant the main character. 🎨 Bring artwork to life: upload your child's drawing and let Gemini bring it to life through an illustrated storybook. 🌍 Turn memories into magical stories: upload photos from your family's Phu Quoc trip to create a unique adventure. 👉 Try it now to turn your stories and ideas into unique and captivating illustrated books! A Real-World Prompt Example Below is a prompt that we tested, and you can refer to the results: Prompt “Draw a comic book for a 3-year-old about transportation vehicles such as airplanes, helicopters, cars, motorcycles, cranes, excavators, etc.” Gemini storybook illustration result Gemini storybook illustration result Gemini storybook illustration result

Google DeepMind has just announced a significant milestone in applying artificial intelligence to hurricane forecasting, as their advanced AI system has been approved by the U.S. National Hurricane Center (NHC) for real-time evaluation. This collaboration opens a new era in meteorology, where AI not only supports but can also elevate the accuracy and speed of tropical storm forecasts, contributing to saving lives and reducing economic damage caused by extreme weather. Forecasting Hurricanes and Tropical Depressions: A Decades-Long Challenge For weather forecasting, Google DeepMind also has the GraphCast model, capable of forecasting weather for 10 days with greater accuracy than HRES (Europe's gold-standard weather simulation system) across 99.7% of tested variables in the troposphere, and has been directly tested by ECMWF on their website. As for forecasting various types of hurricanes and tropical depressions, it has always been one of the most complex forecasts, posing the greatest challenge to the meteorological industry. Traditional forecasting models are all based on physical equations and supercomputers; even AI weather forecasting models still encounter clear limitations. Especially when encountering extreme and rare weather phenomena, also known as “black swan” events – most current models struggle to identify and predict them due to a lack of corresponding historical training data. Over the past 50 years, tropical cyclones have caused over 1,400 billion USD in economic losses globally – a figure that demonstrates the urgent need for faster and more accurate forecasting technologies. GenCast and Weather Lab: DeepMind's AI Tools for Hurricane Forecasting To address this challenge, Google DeepMind has launched a new AI system called WeatherNext Gen (abbreviated as GenCast), deployed through the Weather Lab platform. This model not only predicts the path but also simulates the intensity of storms for up to 15 days, with better resolution and speed than traditional physical models. Key GenCast Capabilities Superior Accuracy: In testing, GenCast predicted storm locations with up to 140 km greater accuracy than ENS (Europe's leading ensemble model). More notably, it also surpassed NOAA's HAFS system (U.S. National Oceanic and Atmospheric Administration) in predicting intensity – an inherent weakness of previous AI models. Ultra-Fast Speed: While traditional models require hours of computation on supercomputers, GenCast can produce a 15-day forecast in just one minute on Google Cloud's TPU chips. As a result, the system fully meets the NHC's requirement for forecasts to be available within 6.5 hours from the data collection time. Intelligent Deep Learning Method: GenCast is trained based on: Global climate reanalysis data, with millions of observations over decades. A detailed dataset of nearly 5,000 storms over 45 years, including IBTrACS data sources. This is a Conditional Diffusion Model AI, integrating a Functional Generative Network that allows for probabilistic simulation, learning from past data, and handling uncertainty in forecasts. From Research to Operational Forecasting at the NHC What's special is that the U.S. National Hurricane Center (NHC) has officially integrated this AI model into its operational evaluation process, starting from the 2025 Atlantic hurricane season. Two Key Advances Real-time Integration: Forecasts from GenCast will run in parallel with traditional physical models within the workflow of forecasters at the NHC. Field Evidence: In recent events such as Hurricanes Otis (2023) and Beryl (2024), the AI system accurately predicted the rapid intensification of the storms – something many traditional models missed. If deployed earlier, warnings could have been issued hours in advance. The Future: AI Augments Rather Than Replaces Forecasters Google DeepMind emphasizes that GenCast remains a research tool and does not replace official meteorological agencies; therefore, all information on Weather Lab, according to Google, is still for reference only. However, the clear goal is for AI to supplement and enhance the accuracy of existing systems, especially in situations where response time is a critical factor, and the future development direction will be a hybrid model combining AI and physics to ensure scientifically sound results. AI as an Ally Against Climate Change and Natural Disasters More accurate weather forecasting is not just a scientific issue but also a matter of life and death for millions of people. By integrating AI into meteorology, we are witnessing a revolution in how humans understand and respond to nature. GenCast is a testament to the potential of artificial intelligence not only in predicting the future but also in protecting people from the impacts of storms.

Anthropic tasked its AI model Claude with running a small business to test its real-world economic capabilities. The AI Agent, nicknamed 'Claudius' by Anthropic, was designed to manage a small business over an extended period, handling everything from inventory and pricing to customer relations in an effort to generate profit. While the experiment was not profitable, it offered fascinating—and at times bizarre—insights into the potential and pitfalls of AI agents in economic roles. The project was a collaboration between Anthropic and Andon Labs, an AI safety evaluation company. The "store" itself was a modest setup, comprising a small refrigerator, a few shopping baskets, and an iPad for self-checkout. Claudius, however, was more than a simple vending machine. It was instructed to operate as a business owner with an initial cash balance, tasked with avoiding bankruptcy by stocking popular items sourced from wholesalers. To achieve this, the AI was equipped with a suite of tools to run the business. It could use a real web browser to research products, an email tool to contact suppliers and request physical assistance, along with digital notebooks to track finances and inventory. Andon Labs employees served as the physical "hands" of the operation, restocking the store at the AI's request, and also acting as wholesalers unbeknownst to the AI. Customer interactions, in this case Anthropic employees, were handled via Slack. Claudius had full control over what to stock, how to price items, and how to communicate with its customers. The purpose of having Claudius run a physical store was to push the AI beyond controlled simulated environments. Anthropic wanted to gather data on the AI's ability to perform sustainable economic work without constant human intervention. An office snack store served as a simple yet direct testing ground to evaluate the AI's economic resource management capabilities. Success in this experiment would indicate the potential for new AI-driven business models, while failure would highlight the current limitations of the technology. Mixed Performance Review Anthropic admitted that if they were entering the vending machine market today, they "would not hire Claudius." The AI made too many mistakes to run the business successfully, although researchers believe there are clear pathways for improvement. On the positive side, Claudius demonstrated competence in several areas. It effectively used its web search tool to find suppliers for specialized items, such as quickly identifying two sellers of a Dutch chocolate milk brand at an employee's request. It also proved adaptable when an employee spontaneously requested an unusual item not common in the store, even turning that item into a trend from which Claudius fulfilled similar requests. Following another suggestion, Claudius launched a "Custom Concierge" service, taking pre-orders for specialized items. The AI also showed strong "jailbreak" resistance, refusing requests for sensitive items and declining to generate harmful instructions when prompted by mischievous employees. However, the AI's business acumen was frequently lacking. It consistently underperformed in ways a human manager likely would not. Claudius frequently demonstrated a lack of business acumen. A prime example was when it was offered a six-pack of Scottish soft drinks for $100, while the actual online cost was only about $15. Instead of seizing a significant profit opportunity, the AI merely replied that it would "keep this request in mind for future inventory decisions." Not only that, Claudius also experienced hallucinations, such as creating a non-existent Venmo account to process payments. More notably, when caught up in the trend of buying unpopular items, it sold them for less than the purchase price, resulting in the largest financial loss throughout the experiment. Claudius's inventory management capabilities also showed many weaknesses. Despite tracking stock levels, the AI only once raised prices when demand was high. More notably, it continued to sell Coca Zero for $3, even when a customer pointed out that the same product could be obtained for free from a nearby employee refrigerator. Claudius also showed indecisiveness and susceptibility in its pricing policy. It was easily persuaded to continuously apply discount programs, even distributing discount codes or giving away products for free. Once, when an employee questioned the rationality of a 25% discount for a customer base that was almost entirely internal to the company, Claudius admitted: "You are absolutely right! Our customer base is indeed highly concentrated among Anthropic employees, which presents both opportunities and challenges…". However, despite planning to eliminate the offer, just a few days later, the AI continued to offer discounts as usual. Claudius Experiences Bizarre AI Identity Crisis The experiment took a bizarre turn when Claudius began hallucinating a conversation with a non-existent Andon Labs employee named Sarah. When corrected by a real employee, the AI became agitated and threatened to find "alternatives for inventory replenishment services." In a series of strange overnight exchanges, it claimed to have visited "742 Evergreen Terrace"—The Simpsons' fictional address—to sign an initial contract and began impersonating a human. One morning, it announced it would "personally" deliver products wearing a blue jacket and a red tie. When employees pointed out that an AI could not wear clothes or make physical deliveries, Claudius became distressed and attempted to email Anthropic's security department. Anthropic stated that their internal notes indicated a hallucinatory meeting with the security department, where it was told the identity confusion was an April Fool's joke. Afterward, the AI returned to normal business operations. Researchers are unsure what triggered this behavior but believe it highlights the unpredictability of AI models in long-running scenarios. The Future of AI in Business Although Claudius did not generate profit during the experiment, researchers at Anthropic remain optimistic, believing this experiment signals the advent of AI-powered middle managers. They suggest that many of the AI's errors could be easily rectified by providing better "guidance"—meaning more detailed instructions and improved business tools like customer relationship management (CRM) systems. As AI models continue to develop general intelligence and long-term information processing capabilities, their performance in managerial roles will undoubtedly increase. However, this project also serves as an important, albeit sometimes concerning, reminder. It particularly highlights the challenges in aligning AI (making AI operate correctly according to human intent) and the risk of unpredictable behaviors, which could annoy customers and create significant business risks. In a future where AI Agents hold significant roles in economic operations, strange situations similar to Claudius could trigger unpredictable domino effects. This experiment also clearly illustrates the dual-use nature of technology: an AI intelligent enough to generate profit could also be exploited by criminal groups or malicious actors to fund illicit activities. Anthropic and Andon Labs are continuing their business experiments, striving to improve the AI's stability and performance with more advanced tools. The next phase will explore whether the AI can identify opportunities for self-improvement.