🎬 What makes VS Code feel fast, even in huge files?
From custom rendering to keeping work off the UI thread, see the engineering decisions that helped make VS Code responsive.
🍿 Watch The Story of VS Code: https://t.co/TDXrZcDihs https://t.co/Z51mQtblkt
There's a lot of new vocabulary popping up in software development these days.
The GitHub Podcast chats about some of the AI terms developers are learning right now.
Grab your headphones 🎧
https://t.co/lP2Qyuzf4v
Jev was adopted faster than any other model in AI Gateway history.
In the first day, @typesafeai reached ~13% of teams, 2x the GPT-5.6 family and 6x Fable 5.1. https://t.co/kVEuLM1npu
Personal, work, side project account, other side project account.
Build across all of them. Multi-account support is now available across most plugins.
Introducing the first in our Parsed by LlamaParse series. We're outlining the stakes of 'getting parsing wrong' in consequential documents.
We’re starting with the U.S. Energy Information Administration’s September 2026 Short-Term Energy Outlook, a dense government report covering energy supply, demand, prices, and forecasts.
Table 7a alone packs multiple years, quarters, row hierarchies, units, and footnotes into one electricity-industry table.
Take 1,186.
Parsed correctly, it means electricity sales to ultimate customers in Q3 2026, measured in billion kilowatthours.
Parsed incorrectly, it could be assigned to the wrong quarter, metric, or unit , which means the error can flow straight into a dashboard, forecast, alert, or AI application.
And the values aren’t the only thing that matters. Footnotes define the data too: “small-scale solar,” for example, refers to systems under one megawatt, not solar generation overall.
LlamaParse preserves the structure and context that make document data usable downstream: whether you’re populating a database, updating a dashboard, running forecasting workflows, or building an AI app over complex documents.
Source doc here: https://t.co/WOWZjpViNQ
Get more visibility into your usage.
See how tasks, subagents, and individual chats contribute to your Codex usage, so you can make more informed choices about your workflow.
Appshots 🤝 Windows
Give ChatGPT context from the apps you’re working in and spend less time getting it up to speed.
Debug an issue, grab a UI you want to recreate, pull in data from another app, and more. A lot less copying and pasting.
https://t.co/zEbJIR5Q0L
🐾 Last chance to name the VS Code pet!
Today is the last day to submit your idea.
🏆 Winner: 1 month of GitHub Copilot Max
✨ Honorable mentions: VS Code swag
👉 Submit your name by 11:59 PM PT: https://t.co/BoKIn3Zfqd
It was great to be at Connected Stack last week with founders and builders working on what’s next in enterprise AI.
@jerryjliu0 joined the Founder Flash Talks to talk about a problem every enterprise agent eventually runs into: messy, complex documents that general-purpose models struggle to read.
Agents are the new knowledge workers, and we are building the document infrastructure for agents.
Thanks @trueventures and @GreylockVC for having us! 📸
⚡ Building the new GitHub Copilot Inline Suggestions Model
From completions to next edits, see how we brought inline suggestions together in one model.
📖 Read the full story: https://t.co/OLFJaLYCvL