ChatGPT Health Launches With Medical Records and Apple Health

Sol, Terra, and Luna make GPT-5.6 look more like a product family than a single model. The naming also signals what OpenAI is trying to change: users no longer have to choose only between an expensive flagship and a much smaller model. Instead, they get three capability tiers designed for different workloads. The important caveat is that GPT-5.6 is currently in limited preview, and OpenAI says it is not available in ChatGPT during this preview period.On the other side, Anthropic positions Claude Fable 5 as a frontier model for reasoning, software engineering, scientific research, and long horizon agentic work. The useful question is therefore not simply which model is smarter. It is which product architecture helps a team complete real work with predictable quality, latency, and cost.What GPT-5.6 actually isAccording to OpenAI's preview announcement, GPT-5.6 consists of Sol, Terra, and Luna. Sol is the flagship and most capable option, Terra is a strong lower cost model, and Luna is the fastest and most cost efficient member of the family.The important change is how OpenAI divides demand into three tiers. A research team might use Sol for a difficult reasoning problem, a product team might run most daily work on Terra, and a high volume system might use Luna for thousands of short requests. This looks more like an infrastructure strategy than the launch of a single new chatbot.Availability matters: OpenAI says GPT-5.6 is not available in ChatGPT during the preview. An experience in an API, developer tool, or partner platform should not be treated as the final ChatGPT experience.Sol is designed for difficult, extended workSol is positioned as the strongest GPT-5.6 model for deep reasoning, complex coding, and long multi step tasks. A software team might ask it to understand a repository, identify the cause of a bug, propose a minimal patch, and write regression tests. Sol's value is not answering a short question quickly. It is maintaining the objective while working through a longer chain of decisions.OpenAI also highlights stronger cyber capability as reasoning increases. That can be useful for authorized security testing and vulnerability analysis, but it also makes access controls, logging, sandboxing, and human approval more important.Terra aims for the practical middleTerra targets the broadest category of work: document analysis, content production, application development, research synthesis, and operational support. If Sol is the specialist called for the hardest problem, Terra is the strong team member expected to work throughout the day without making every request unnecessarily expensive.A marketing team could use Terra to read market reports, extract insights, build an outline, and draft several content variants. A development team could use it for code review, test generation, and tickets with a clear scope. This tier could become the default if its real world quality remains consistent.Luna prioritizes speed and scaleLuna is designed for low latency and lower cost. Classification, conversation summaries, field extraction, drafting, and ticket routing do not always require the strongest model. In these cases, response time and total operating cost matter more than maximum reasoning capability.Fast does not mean suitable for everything. If a task requires source verification, a long plan, or a code change with a large blast radius, a team should move it to Terra or Sol instead of forcing Luna beyond its intended role.Claude Fable 5 takes a different routeAnthropic presents Claude Fable 5 as a frontier model for reasoning, software engineering, vision, scientific research, and long horizon agentic work. Instead of emphasizing three product tiers in one generation, Anthropic's message focuses on the capability of a powerful model working inside the Claude ecosystem.This difference changes deployment decisions. With GPT-5.6, an engineering team might build a router that sends each request to Sol, Terra, or Luna. With Fable 5, the focus may be on optimizing prompts, tools, context, and reasoning budgets around one primary model. Neither approach is universally better because the answer depends on workload and operational maturity.A fair comparison: Do not run one prompt and declare a winner. Build a test set covering short tasks, long reasoning, coding, extraction, and recovery from errors. Measure accuracy, latency, the number of human corrections, and the total cost of a completed task.Coding and agentic work depend on the surrounding toolsBoth GPT-5.6 Sol and Claude Fable 5 target complex software work, but the practical experience depends heavily on the system around the model. The ability to read a repository, execute commands, observe results, and correct mistakes can matter as much as a benchmark score. For OpenAI workflows, the Codex page is a useful starting point for understanding how a model participates in coding work.Fable 5 may be attractive to teams already invested in Claude and long running agentic workflows. Read our Claude Fable 5 coverage for more context on Anthropic's positioning and the types of work it targets.What early forum experience tells usEarly discussions on Reddit and developer communities focus on how different Sol, Terra, and Luna feel in real work. Some users describe Sol as the better fit for multi step tasks, Terra as the practical option for routine work, and Luna as the interesting choice for speed. These observations match OpenAI's positioning, but they do not establish a precise quality gap.Forum reports are useful because they reveal the questions real users care about. However, they are self selected evidence. People may use different prompts, access levels, integrations, and preview versions. A result from a developer platform does not guarantee the same result when a model eventually appears in ChatGPT.Early positivesThe three tiers make it easier to understand which model belongs to which workload.Luna creates a clear expectation of low latency for high volume systems.Terra could become a default if it delivers stable quality at a practical cost.Sol is expected to be stronger for coding, long reasoning, and tasks with several verification steps.Open questionsHow large the practical quality gap between Sol and Terra will be on common workloads.The total cost after retries, corrections, and human review are included.How Luna behaves with long prompts and many constraints.Whether performance remains stable as GPT-5.6 expands beyond preview access.Forum reports are not benchmarks: Community experience should help you choose test cases, not make a production purchasing decision by itself.Comparing GPT-5.6 and Fable 5 by workloadWriting and document analysisTerra appears positioned for most document work because it balances capability and cost. Fable 5 may be attractive when documents are long, questions are complex, and the model must maintain an argument across a large context. A useful evaluation should score citation accuracy, structural consistency, and how much editing is required before publication.Software development and debuggingSol and Fable 5 are both candidates for difficult coding tasks. A representative test should include reading existing code, identifying the root cause, producing a minimal fix, writing tests, and explaining risk. Asking a model to create an isolated function from scratch does not reflect how well it works in a real repository.High volume processingLuna has the clearest positioning advantage when speed and cost dominate. At thousands of extraction or classification requests per day, a small difference in price and latency can have a large effect. Fable 5 may be unnecessarily expensive for a workload that only needs short, structured outputs.Research and long reasoningSol and Fable 5 should be compared with tasks that have verifiable outcomes rather than open questions that merely sound impressive. Give both models the same research material and ask them to identify assumptions, detect contradictions, propose an experiment, and explain what evidence is missing. The better model is the one that helps users discover errors faster, not the one that writes the longest answer.Should you choose Sol, Terra, Luna, or Fable 5?If you want maximum capability inside the OpenAI ecosystem, Sol is the first model to test. If you need a strong model for regular use, Terra has the more practical position. If your workload contains many short and repetitive tasks, Luna could reduce operating cost. Fable 5 remains relevant for teams invested in Claude or focused on long reasoning and agentic work.Because GPT-5.6 is still in preview, replacing an entire production workload would be premature. Run the models in parallel on real but sanitized data, record failures, and use the same criteria for every candidate.A test plan you can use nowSelect 20 tasks that represent real work, including easy and difficult cases.Run each task on Sol, Terra, Luna, and Fable 5 when access allows.Score accuracy, response time, total cost, and required human correction.Track severe failures separately instead of relying only on averages.Choose a model for each workload category rather than forcing one model to do everything.Is GPT-5.6 worth switching to now?The most important change in GPT-5.6 may not be Sol's raw capability. It is OpenAI's decision to turn one model generation into three operational tiers. That could help organizations control cost, but only if they can classify workloads and route requests intelligently.The practical next step is to build a small benchmark from your own data. If Sol wins difficult tasks, Terra is good enough for routine work, and Luna handles high volume requests reliably, the three tier architecture has real value. If Fable 5 remains more consistent on long reasoning, a multi model strategy may still be better than committing to one provider.

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

Automating Excel and Google Sheets reporting is no longer exclusive to software engineers. With the rapid evolution of AI models like GPT, office workers can now create custom workflow automation tools using simple instructions with Codex, freeing up hours of repetitive daily tasks. Why Excel Formulas and VBA Are No Longer Enough For weekly recurring reports or automated integrations with email, Slack, and messaging apps, traditional methods like nested Excel formulas or recording VBA (Visual Basic for Applications) macros require specific technical skills and break easily whenever a single column in the source file changes. This is the exact gap that OpenAI Codex fills: you describe precisely what needs to be done in natural language, and Codex generates complete Python or Google Apps Script code ready to run in seconds. Codex Is Not a Single Product A common misconception is how Codex is accessed: it can be used via CLI in the terminal, IDE extensions in VS Code, Codex Web on the cloud at https://chatgpt.com/codex/cloud for developers and coding enthusiasts, as well as desktop applications for both macOS and Windows. For office workers unfamiliar with command-line tools, the easiest way to start is downloading the Codex desktop app. It lets you manage multiple agents simultaneously right from a visual interface without opening a terminal or configuring API keys—just log in with your existing ChatGPT account. What an Example Prompt Looks Like You only need to provide Codex with a detailed prompt like: "Write a Python script that reads the Excel file 'sales_raw.xlsx', filters orders with status 'Completed', calculates total revenue by branch, and exports the result to 'revenue_report.xlsx' with dark blue header styling." Codex will instantly generate standard, high-quality code, and you simply run the script to get your finalized report. Automating Local Excel Reports with Python and Codex For Excel files stored locally, combining Codex with two popular Python libraries—pandas and openpyxl—delivers outstanding processing speed. Pandas handles hundreds of thousands of data rows in seconds, while openpyxl manages cell formatting, header colors, and formula insertion as illustrated in the example above. Automating Google Sheets in the Cloud When your team collaborates on Google Sheets instead of offline Excel files, the Python library gspread or Google Apps Script is the ideal choice. Codex can write code that connects directly to the Google Sheets API via a Service Account (JSON credential file) to read and write data continuously without opening a browser. A Sample Workflow Automatically pull new form submissions from Google Forms into the spreadsheet Automatically categorize customer feedback by priority Automatically send daily summary emails to leadership at 17:00 This entire pipeline runs in the background with zero manual intervention once configured. 4-Step Implementation Process for Non-Coders Step 1 - Standardize input data: Ensure the Excel or Google Sheets file has clean, clear headers without arbitrary merged cells. Step 2 - Write clear prompts for Codex: Explicitly mention file names, column names, filtering/calculation steps, and desired output formats. Step 3 - Test and paste errors for AI auto-debugging: If the script encounters an error, copy the full error traceback and paste it back into Codex for automatic correction. Step 4 - Schedule automatic runs: Use Windows Task Scheduler (Windows) or Cron jobs (macOS/Linux) to run the script automatically on a set schedule. Risks and Considerations Before Handing Reports Over to AI Codex is powerful but not completely free; however, simple operations with Excel or Google Sheets consume very little quota, so light users can comfortably rely on the free tier. If heavier workloads are required, consider Plus or Pro plans—avoid the Go tier as Codex capabilities there offer little advantage over the free plan. Never paste system passwords, financial records, or real customer data into public AI chat interfaces. When asking Codex to generate code, always substitute sensitive details with dummy data of the same structure. For critical validations such as monetary amounts or tax formulas, do not trust AI-generated code blindly on the first run. Always cross-check the output during the first 1-2 executions to ensure the logic perfectly matches your actual business requirements. Automating reports with Codex does not make you a programmer, nor should it. The true value lies in understanding your own data and business processes well enough to describe them clearly to AI—Codex handles the coding. If your company has weekly recurring reports, start today by picking the simplest one, writing a prompt following the sample above, and running your first automated test.

In March 2026, the Model Context Protocol (MCP) SDK reached 97 million monthly downloads, a 970-fold increase just 18 months after its launch. OpenAI, Google DeepMind, Microsoft, and AWS have all integrated MCP into their products. By December 2025, Anthropic officially handed over MCP to the Linux Foundation to make it an open industry standard. MCP is no longer just Anthropic's internal project, but the foundational infrastructure upon which nearly the entire AI industry is building. What is MCP? Model Context Protocol (MCP) is an open protocol released by Anthropic in November 2024. It enables AI models to connect with external data and tools such as Gmail, GitHub, Notion, and internal databases using a single unified standard, eliminating the need for custom integrations per tool. The easiest way to visualize MCP is by comparing it to USB-C. Before USB-C, every device required its own charger cable, creating a clutter of different cables. USB-C emerged to give every device a single standardized port that works everywhere. MCP does the same for AI, providing seamless connectivity to any tool, platform, or data source supporting the standard without custom code for every model-tool combination. How Does MCP Work? Host-Client-Server Architecture MCP is not just a simple two-tier client-server system as commonly thought, but actually defines three distinct roles: Host: The AI application you interact with directly, such as Claude Desktop, Claude Code, or an AI-enabled IDE. Acting as the central orchestrator, the Host manages access permissions and security policies for the entire session. Client: Created by the Host, each Client connects to exactly one Server and handles bidirectional communication between Host and Server. Server: A server connecting directly to the native tool or platform (such as Google Drive, Slack, Email, Calendar, or Database). The Server exposes capabilities and executes actions for the AI. When you connect 3 MCP servers in Claude Desktop, the Host is actually managing 3 distinct Clients, each speaking to its designated Server. Three Core Primitives: Tools, Resources, Prompts Tools: Executable functions that the AI can call to perform actions, such as send_email, create_issue, or search_database. Resources: Data that the AI can read to supply context to the LLM, such as files, records, Notion pages, or database entries. Prompts: Pre-built command templates supplied by the Server to guide the AI on how to use tools effectively for specific tasks or enable quick user triggers. A Concrete Example Suppose you ask Claude "Which recent email mentions contract ABC?". Claude Desktop (Host) initializes a Client connecting to the Gmail MCP Server. This Server calls the Gmail API to search for relevant emails and returns the result in standard MCP format. Claude reads the response and answers you in natural language. For multi-step workflows, like summarizing a YouTube video and saving the summary to Google Drive, Claude calls two different MCP Servers sequentially within the same task, requiring zero manual context switching from you. MCP does not create intelligence on its own; it is simply a standardized connectivity layer. Response quality still depends on the underlying AI model and how well the MCP server implements its tools. How Does MCP Differ from Traditional APIs or Plugins? Before MCP, if you wanted 5 different AI models (Claude, GPT, Gemini, Llama, Mistral) to connect to 5 services (Gmail, Slack, GitHub, Notion, Jira), you theoretically needed to write 25 separate integration pairs — an N×M problem. MCP solves this problem by standardizing the protocol in the middle. We only need to write one MCP server, and every MCP-compliant AI model can use it immediately. The required integrations drop from N×M down to N+M. Traditional Plugins: Each AI platform maintains its own plugin system (e.g., GPT Actions, custom Claude tool use), which cannot be used cross-platform. Traditional APIs: Developers must read documentation, write custom API calling code, and handle authentication per service, typically limited to fixed request-response patterns. MCP: A universal standard — write once and use across the entire MCP-supported AI ecosystem, featuring continuous bidirectional communication where AI can both pull data (read schedules) and push actions (create events) within a single session. When Are Traditional APIs Still Better Than MCP? MCP's flexibility does not mean it is always the best choice. For systems requiring absolute precision and deterministic behavior — such as banking operations like balance checks or wire transfers — traditional APIs with fixed, strictly controlled workflows remain safer. MCP is best suited when you need AI to autonomously decide which tool to call and in what order based on conversational context, rather than rigid transactions requiring strict risk control. Why Is the Entire AI Industry Racing to Adopt MCP? The adoption rate of MCP is unprecedented for a new tech standard. In March 2025, OpenAI officially supported MCP in its Agents SDK and ChatGPT Desktop, despite Anthropic being a direct competitor. By mid-2025, Google DeepMind integrated MCP into the Gemini API. Microsoft brought MCP support to VS Code Copilot, reaching General Availability in July 2025. The biggest turning point occurred on December 9, 2025, when Anthropic handed MCP over to the Agentic AI Foundation (AAIF) under the Linux Foundation. OpenAI and Block co-founded the foundation, while AWS, Google, Microsoft, Cloudflare, and Bloomberg joined as platinum members. This signaled clearly that MCP was no longer Anthropic's proprietary technology, but shared infrastructure that even competitors wanted to build together. Even hardware companies have joined in by opening MCP endpoints for their devices, including smartwatches and heart rate monitors. By July 2026, MCP released its largest spec update to date (2026-07-28), moving the core protocol to stateless, adding an Extensions framework, and introducing OAuth/OpenID Connect authorization. This eliminated the final hurdles for enterprise production deployments. As of 2026, over 10,000 public MCP servers are running in production, and 28% of Fortune 500 companies have deployed custom internal MCP servers. A notable indicator: OpenAI deprecated its proprietary Assistants API in favor of MCP, setting a hard sunset date for mid-2026. When a direct competitor abandons its own standard for Anthropic's open standard, market validation speaks louder than any statement. Practical Application: How to Use MCP with Claude For Claude.ai or Claude Desktop users, connecting an MCP server requires no coding skills. Navigate to Settings → Extensions to view available MCP servers (Google Drive, Notion, Slack, GitHub, Asana...) or add a custom server via URL. Once connected, Claude automatically knows when to invoke specific tools based on your prompts. Here are a few real-world use cases I use daily for 4AIVN editorial work: Claude + Google Drive MCP: Ask directly "Find last week's Gemini 3.7 article outline" instead of searching Drive manually. Claude + GitHub MCP: Review pull requests and read issues without leaving the chat window. Claude + Notion MCP: Update the content calendar database while brainstorming article ideas. Every connected MCP server is granted read/write permissions to your real data. Before enabling an unfamiliar server, verify its developer and requested permissions, especially for servers outside official listings. MCP Will Undoubtedly Keep Growing The most remarkable aspect of MCP is not the protocol itself, but how rapidly it is becoming an implicit standard when users evaluate AI tools. Just as laptop buyers now ask "Does it have USB-C?", in 1-2 years asking "Does this tool have an MCP server?" will likely become a key evaluation criterion for any SaaS or device. This is no longer just a game for OpenAI, Google, or Anthropic; any enterprise or product without MCP integration, regardless of how good it is, risks falling at a disadvantage as users grow accustomed to asking AI directly instead of opening apps manually. For small and medium enterprises, including those in Vietnam, this represents an opportunity rather than pressure. Writing an MCP server does not demand massive infrastructure like building an AI model — wrapping an existing API according to MCP spec is enough for your product to "speak" with Claude, ChatGPT, or any MCP-compliant AI client. Early movers gain a clear competitive edge while user habits are still forming.