Starting today, you can connect multiple accounts with most plugins in ChatGPT! 🎉 Bring context from your work and personal accounts into the same conversation. Connect your accounts in the plugin directory: Devs: this works automatically, with no changes needed. But, to make the experience for users of your plugin even better, add a profile tool to your MCP server so ChatGPT can label the accounts:

We're adding support for AGENTS.md to Claude Code. Starting today in version 2.1.277, if there is no CLAUDE.md in a folder, Claude will check for and use AGENTS.md. You can toggle this behavior in /config.

One of the most underrated features in the ChatGPT desktop app: Appshots. Appshots take the context on your screen and bring it into the desktop app, allowing you to share instead of describe. You’ll be surprised at how useful it is. To get started, press both Command keys on macOS, or both alt keys on Windows.

This is my harness tier list as of now. What im counting is: models i can use in them (thats why cursor on B, otherwise would be S), how good they look, speed, and how comfortable they are to use. Codex + T3 Code are my favorites, i love codex's ui/ux and computer use, for T3 Code its the fact i can use multiple providers there, and their best-in-class mobile app, which i love.

Astra for Law: Frontier intelligence built for your practice. A new offering powered by GPT-6 Astra with tools, settings, and context to support the expertise and judgment of lawyers and legal technology firms.

Procrastination has met its match. ChatGPT is now in Word. You can add ChatGPT to Microsoft Word to turn rough notes into first drafts, untangle tricky paragraphs, proofread, and get suggested edits—all without leaving your document. It can even spot formatting issues. Which means a few less things to fix later 🔍

Today we rolled out Astra to every engineer at Databricks (N=~3500). Some notes that may be helpful to others: 1. Astra unambiguously out performs our previous highest-end models (Opus 5, Sol 5.6) on highly complex tasks, especially those related to high level system design or long range horizontal tasks. 2. Engineers given Astra increased overall coding spend by around 60% compared to baseline. 3. It is not clear Astra meaningfully improves on medium/low complexity coding tasks compared to earlier models. We suspect those tasks are mostly saturated (i.e. perfectly executed) by existing models. 4. We learned above by piloting Astra with around 200 users to gain signal on both quality and cost. We use Unity Gateway to do cohort-based experiments for all new models. 5. We give engineers a sub-budget specific to Astra to encourage them to use Astra selectively on complex tasks while preferring lower cost models for everyday tasks. Our engineers are able to mix-and-match tools and models within their overall budget envelope (we also allow for increased budgets through various mechanisms). These budgets are defined in Unity Gateway and regularly revisited. Note: We do not have robust comparisons of Astra-vs-Fable because we have net yet rolled out Fable widely due to data retention policies.

We are releasing the latest version of BrokenArXiv and ArXivMath! These benchmarks now focus on conjectures that were refuted in the last month on ArXiv, and models are executed within a harness instead of directly via API. Performance remains impressive, with GPT-6 Astra on top