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How to control Codex from your phone with ChatGPT app
By Nam
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. [IMAGE:/image/news%2Fquet-ma-qr-de-ket-noi-chatgpt-mobile-app-voi-codex.webp|Scan QR code to connect ChatGPT mobile app to Codex|Scan QR code to connect ChatGPT mobile app to Codex] 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.
What Is Hermes Agent? Nous Research's Self-Learning AI
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.
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19 Jun, 2026
Gemini powers Argentina and Messi at World Cup 2026
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.
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17 Jun, 2026
AI Technology at World Cup 2026: A Complete Overview
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.
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16 Jun, 2026
Anthropic launches the highly powerful Claude Fable 5 model
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.
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10 Jun, 2026
Claude Code self-orchestrates work with Dynamic Workflows
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.
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5 Jun, 2026
Microsoft launches 7 new AI models to challenge OpenAI
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.
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4 Jun, 2026
Claude Code, NotebookLM, and Obsidian for Smarter Research
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.
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2 Jun, 2026
What is an agent harness? The framework that helps AI work efficiently
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.
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1 Jun, 2026
Claude Opus 4.8 launches: what is new in Anthropic's strongest model?
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.
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29 May, 2026
Create a free mini app with just a few clicks using Google AI Studio
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.
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24 May, 2026
Google I/O 2026: Flow gets a major upgrade with Gemini Omni
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.
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21 May, 2026
Google I/O 2026: Antigravity 2.0 Major Improvements, but Interface Resembles Codex
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.
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20 May, 2026
Firefox's shake to summarize feature is now available on android
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.
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19 May, 2026