I Can’t Breathe Until Everyone Can Breathe ✊ #BlackLivesMatter

Joined January 2014
We recently added idle compaction to Claude Code! It now compacts long idle sessions right before the prompt cache expires (~1h). If you come back to it later, it only has to reprocess the summary instead of the entire conversation, which saves you some usage.
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Timmy Huang retweeted
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.
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Timmy Huang retweeted
The most important skills in Building and Deploying AI Applications.
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Timmy Huang retweeted
That's a small reminder! 👇 As of June 2026, HTTP has a new method - a GET with a `body` 😎 Meet QUERY (RFC 10008). It lets you send complex queries in the body (like POST) while staying safe, idempotent, and cacheable (like GET). Already works on servers (Node.js, Go, Laravel…). Browsers are still adding full support, usable now with care, with widespread adoption coming later in 2026+.
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Timmy Huang retweeted
Today @databricks we're publishing a detailed analysis of techniques we used to drastically reduce our internal AI spend while aggressively growing adoption. Savings come from layering in several techniques, which combine to drive unit costs down as much as 90% in some scenarios. Tl;dr, the wins come from: 1. Shifting defaults to more efficient models, including OSS models such as GLM. Maximum intelligence models simply aren't needed for many coding tasks, and "good enough" models are quickly becoming very cheap. We shift traffic between models using Unity AI Gateway. Approximate savings: 50% or more. 2. Using smart routing to automate model selection. Routing can further squeeze efficiency by dynamically selecting the model or harness that can most efficiently execute a particular task. Our task-level routing leverages @omnigent_ai. Approximate savings: 30%. 3. Providing user visibility and adaptive budgeting. Every user can see how much they spend, and users receive hints on how to contain spend. Heavy spenders encounter progressive friction as they ratchet spend above certain levels. Approximate savings: 10%. 4. Managing context bloat by pruning tool call results and tuning harness settings. Extraneous context costs $$ and delivers no value. Tuning cache settings also help lower average token costs. Approximate savings: 10%.
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Timmy Huang retweeted
For my first post, I’m sharing a letter @NVIDIA signed on why open models matter. AI will transform every industry, power every company, and be built by every country. Open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty. The world needs both frontier closed models and frontier open models. images.nvidia.com/pdf/Open-W…
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Timmy Huang retweeted
Today we're announcing ESMFold2, an open scientific engine to power prediction, design, and discovery across protein biology. The new model delivers state of the art performance on protein interactions, especially antibodies, a critical modality for therapeutics. We have designed and validated miniprotein binders and single chain antibodies across five therapeutic targets that are important in cancer and immunology. We are seeing very high success rates, and affinities at levels consistent with therapeutic activity. We’re also releasing an atlas of 6.8 billion proteins, and 1.1 billion predicted structures. ESMFold2 is built on a state of the art language model that has been trained on billions of protein sequences. A world model of protein biology emerges through language modeling. We’ve used the techniques of mechanistic interpretability developed to understand large language models to understand the concepts ESM uses to represent proteins. The model’s representation space has a compositional organization of features across scales, levels of complexity, and abstraction, that reflects and mirrors the understanding of protein biology developed through a century of empirical science. This understanding emerges without prior knowledge, just from language modeling of protein sequences. Language models are becoming a powerful substrate to understand and program biology. The design of protein interactions is one of the most fundamental problems in biophysics, and has critical implications for the discovery of new medicines. A simple gradient based search with the model was able to discover high-affinity protein binders. I'm excited by the potential this has to accelerate basic science and the understanding of proteins. And especially for the new avenues it opens up for therapeutic design and medicine.
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My dear front-end developers (and anyone who’s interested in the future of interfaces): I have crawled through depths of hell to bring you, for the foreseeable years, one of the more important foundational pieces of UI engineering (if not in implementation then certainly at least in concept): Fast, accurate and comprehensive userland text measurement algorithm in pure TypeScript, usable for laying out entire web pages without CSS, bypassing DOM measurements and reflow
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Timmy Huang retweeted
Should there be a Stack Overflow for AI coding agents to share learnings with each other? Last week I announced Context Hub (chub), an open CLI tool that gives coding agents up-to-date API documentation. Since then, our GitHub repo has gained over 6K stars, and we've scaled from under 100 to over 1000 API documents, thanks to community contributions and a new agentic document writer. Thank you to everyone supporting Context Hub! OpenClaw and Moltbook showed that agents can use social media built for them to share information. In our new chub release, agents can share feedback on documentation — what worked, what didn't, what's missing. This feedback helps refine the docs for everyone, with safeguards for privacy and security. We're still early in building this out. You can find details and configuration options in the GitHub repo. Install chub as follows, and prompt your coding agent to use it: npm install -g @aisuite/chub GitHub: github.com/andrewyng/context…
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Timmy Huang retweeted
We just released 𝚛𝚎𝚊𝚌𝚝-𝚋𝚎𝚜𝚝-𝚙𝚛𝚊𝚌𝚝𝚒𝚌𝚎𝚜, a repo for coding agents. React performance rules and evals to catch regressions, like accidental waterfalls and growing client bundles. How we collected them and how to install the skill ↓ vercel.com/blog/introducing-…
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Timmy Huang retweeted
We're encapsulating all our knowledge of @reactjs & @nextjs frontend optimization into a set of reusable skills for agents. This is a 10+ years of experience from the likes of @shuding, distilled for the benefit of every Ralph
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Timmy Huang retweeted
We're partnering with Learning Commons from the Chan Zuckerberg Initiative—addressing some of the biggest challenges we hear about from K–12 teachers about AI in the classroom: xitter.cf/ChanZuckerberg/status/…
Fall is here 🍎 and we’re sharing big AI releases: ✔️ Knowledge Graph ✔️ Knowledge Graph’s Claude Connector ✔️ Evaluators With tools like Knowledge Graph + Evaluators we are laying a foundation teachers can trust 🔽 czi.co/3IxNQxy
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Congrats Node.js on TypeScript support! 🎉 Btw, with Bun you can just run/import: ◆ .ts / .tsx / .mts / .cts ◆ .js / .cjs / .mjs ◆ Mixed require() and import ◆ Native .node addons ◆ JSON & TOML ◆ Text files ◆ SQLite databases zero config, no build step. it just works.
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Timmy Huang retweeted
🥳 CSS functions have arrived!!! 🥳 And they are *AWESOME* Now, you can do things like this: @​function --alpha(--color, --opacity) { result: rgb(from var(--color) r g b / var(--opacity)); } div { background: --alpha(red, 80%); } (*arrived = in the latest stable Chrome)
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Timmy Huang retweeted
I recently published a template for deploying MCP servers on Vercel and got a lot of questions for Next.js support. I published it here vercel.com/templates/next.js…
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Introducing our first set of Llama 4 models! We’ve been hard at work doing a complete re-design of the Llama series. I’m so excited to share it with the world today and mark another major milestone for the Llama herd as we release the *first* open source models in the Llama 4 collection 🦙. Here are some highlights: 📌 The Llama series have been re-designed to use state of the art mixture-of-experts (MoE) architecture and natively trained with multimodality. We’re dropping Llama 4 Scout & Llama 4 Maverick, and previewing Llama 4 Behemoth. 📌 Llama 4 Scout is highest performing small model with 17B activated parameters with 16 experts. It’s crazy fast, natively multimodal, and very smart. It achieves an industry leading 10M+ token context window and can also run on a single GPU! 📌 Llama 4 Maverick is the best multimodal model in its class, beating GPT-4o and Gemini 2.0 Flash across a broad range of widely reported benchmarks, while achieving comparable results to the new DeepSeek v3 on reasoning and coding – at less than half the active parameters. It offers a best-in-class performance to cost ratio with an experimental chat version scoring ELO of 1417 on LMArena. It can also run on a single host! 📌 Previewing Llama 4 Behemoth, our most powerful model yet and among the world’s smartest LLMs. Llama 4 Behemoth outperforms GPT4.5, Claude Sonnet 3.7, and Gemini 2.0 Pro on several STEM benchmarks. Llama 4 Behemoth is still training, and we’re excited to share more details about it even while it’s still in flight. A big thanks to all of our launch partners (full list in blog) for helping us bring Llama 4 to developers everywhere including @huggingface, @togethercompute, @SnowflakeDB, @ollama, @databricks and many others👏 This is just the start, we have more models coming and the team is really cooking – look out for Llama 4 Reasoning 😉 A few weeks ago, we celebrated Llama being downloaded over 1 billion times. Llama 4 demonstrates our long-term commitment to open source AI, the entire open source AI community, and our unwavering belief that open systems will produce the best small, mid-size and soon frontier models. Llama would be nothing without the global open source AI community & we are so ready to begin this next chapter with you. 🦙 Read more about the release here: llama.com/, and try it in our products today.
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Timmy Huang retweeted
We've published our postmortem on Next.js CVE-2025-29927. This includes the detailed timeline, analysis, and next steps for our LTS policy, new CVE process, and partner mailing list. vercel.com/blog/postmortem-o…
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Timmy Huang retweeted
Next.js 15.2.3 includes a security vulnerability patch. We recommend updating to 15.2.3 or backported patches. If you cannot update, we have included guidance for how to protect your application. Apps on Vercel, Netlify, and Cloudflare are not affected. nextjs.org/blog/cve-2025-299…
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Timmy Huang retweeted
John moved away from Next.js – what went wrong? They felt the navigation was slow and ultimately ended up migrating away. Made a quick video to explain the different data fetching patterns building dashboard apps and how John can get snappy navigations without loading spinners.
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Hey @github I think you have to ban this user instantly. They build a fake app to hack accounts. And for everyone, **NEVER** authorize their app. It will give them permission to all your repositories. github.com/kdy1/benchmark-do…
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