I like to train deep neural nets.

Joined March 2019
Yameen retweeted
Introducing Kimi K3.1 Pro: our most capable 3T-class open-weight model. 🔹 1M-token context, 2.8T total / 104B active params (MoE), Multimodal. 🔹 Full-project coding: games in Unreal, Unity & Godot. 🔹 Excels at parametric 3D modeling in Blender & CAD. 🔹 More reliable at finance work: Accurate modeling with hallucination down by 65%. 🔹 Matching the strongest proprietary models across coding, agentic and knowledge benchmarks. Kimi K3.1 Pro and K3.1 Pro Highspeed (2–3× faster) are now live on Kimi.ai, Kimi Work, Kimi Code, and the Kimi API. Open weights by Nov 4, 2026. 🔗 API: platform.kimi.ai 🔗 Tech blog: kimi.ai/blog/kimi-k3-1-pro
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Yameen retweeted
Real life, baby
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Yameen retweeted
You can use your Claude plan in Amp now. Free and open to everyone. (By very very very very popular request.) ampcode.com/news/use-your-cl…
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Seems like Anthropic built /code-review and /design into Opus 5.5 itself. Kind of useless doing code review on Opus 5.5 code. Only adversarial reviews or formal verifications make sense.
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Claude Opus 5.5 just took a big drop on NerfBench. Yesterday it was scoring above launch. Today it's at 94.2%. GPT 6 Astra: 98.0% Sonnet 5.5: 100.9% GPT 6.1 Sol: 106.7% 94.2% is still inside normal variance, so we can't call it a nerf yet. But we're watching Opus 5.5 very closely.
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GPT‑6.1 Sol and Sonnet 5.5 leap up BuildingBench’s leaderboard! 🏗️ 🥈 Sol reshapes the cost–quality frontier greatly! It scores 85.1, up from GPT‑6 Sol’s 69.6—taking second place while cutting median cost from ~$1.89 to ~$1.70 per building. That edges past GPT‑6 Astra (ultra, 84.3) at 76% lower cost. Just 1.7 points behind Opus 5.5 (max, 86.8), at roughly 1/27 the cost. 🚀 Sonnet 5.5 (max) climbs to #5 with 76.9, up from Sonnet 5’s 54.4—a 22.5-point leap! The gain comes with higher cost: ~$39.99 vs. $15.03 per building. Two big quality jumps. Very different price tags.
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Yameen retweeted
Burnout happens when you work too hard with not enough meaning. "He who has a why to live for can bear almost any how" — Nietzsche
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"It will add slop and you end up with unmaintainable mess of a codebase" More and more convinced that Accumulating Slop theory is no longer true. Might've been 6 months ago, but now? 1. Not convinced it will add "slop" But even if: 2. Not convinced it'd be unmaintainable
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Yameen retweeted
Introducing Mo, the world's first AI QA engineer Point it at your app and 100+ agents bug bash it in parallel. Every bug comes back with repro steps, logs, and a video In our evals vs Codex with Playwright MCP: → 3.8x more bugs → 9x more bugs per hour → half the cost per bug RT + reply "Mo" for $250 in free credits. Drop your app URL, Mo will bug bash it and DM you the report today
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Introducing Eleven v4 and Eleven v4 Turbo, our fastest and most emotive voice models yet. Ranked #1 by Artificial Analysis.
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We are releasing cf, our new command-line tool that mirrors the entire Cloudflare API and supports programmatic TypeScript configuration. We are also open-sourcing Forge, our internal SDK generator. cfl.re/3VECjml #BirthdayWeek
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Yameen retweeted
The number of programmers worldwide who can beat a collaborating pair of frontier models rounds to zero.
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Yameen retweeted
I stayed up til 2am and spent $1,000 benchmarking Jev Router so you don't have to. Performance on DeepSWE was roughly the same as GPT-6 Astra on low. It costs slightly more, and it took almost 5x longer to run.
Introducing typesafe/jev-router: a cache-aware model router powered by Jev and @typesafeai The Jev Router picks the best model and reasoning effort for each request, balancing quality, speed, and cost. Here's how it works 👇🏻
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Claude Opus 5.5 made this video entirely with code.
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This is an extremely good watch. The things that felt novel/interesting to me: 1. Lock down your agents Humans tend to like 'sharp knife' abstractions - that are powerful, but you can cut yourself if your use them wrong. Lauren says agents perform much better in extremely locked-down environments. Abstractions are designed so they can't screw up, and lint rules enforce it. They built a whole internal framework (Dune) to keep the agent on track. That helps optimise agents that don't have a large context window to work productively in your codebase. 2. Create verification infrastructure To trust the results of any agent, you either need to sit and watch it OR have it provide evidence of its improvement. This has always made sense to me, but Lauren really pushes it hard here: - Invest in custom CLI's that let the agent drive the app and measure its performance - Make the app "factory ready" from the get-go - i.e. deployable to an environment where the agent can mess about with it 3. Feature Maps Lauren's software factory (what she calls an 'outer loop') often requires the agent to break down vague bug reports from users and to turn those into potential fixes. To aid that, they built a 'feature map' of all the main features in their application, which describe exactly how the app is supposed to function. This has become essential for helping the agent navigate the codebase, and figure out quickly how things are supposed to work. It's maintained along with the codebase, and kept in sync via automations. This is the kind of documentation I usually warn against. It goes stale quickly and can confuse agents if it's not kept up to date. But Lauren's team are using it as critical navigation infrastructure, and it makes it possible for agents to explore faster and better - even on a large codebase. So it sounds like navigation docs like this are worth it if they enable new behavior. Banger talk - watch the whole thing on 2x.
here's how i shipped 2,500 PRs last month to production this was originally supposed to be for Cursor Compile in London. i couldn't make it since i was livestreaming for Grok @Bot Galaxy so i'm making it available for free here on X! watch it on 2x speed, i talk slowly
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Yameen retweeted
You can now generate an entire 3blue1brown style video from any research paper with Opus 5.5. Here’s a 8min video summary of “Regularized Recursive Self Improvement of Agent Harnesses”. The 90%ile educational YouTuber is fully automated.
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I wanted to quickly create a short video with Opus 5.5 about the history of AI, from "Attention is all you need" to AGI. No stock footage, no image or video generators: every frame is rendered from code. This 3-minute film was made 100% in code by Claude in Claude Code: ~7,400 lines of React/TypeScript (Remotion), every image drawn in SVG and Canvas, an open-source TTS voice, and a score synthesized in Python. No stock footage. No image generators. Id say pretty cool! Tookl about 1h and 7% weekly rates.
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wow!
OPUS 5.5 BENCHMARKS JUST DROPPED. It beats Fable 5.1 and GPT 6 Astra across the board. Agentic coding, knowledge work, reasoning, computer use, visual recognition. Opus 5.5 leads every single one. At half the price of Fable 5.1. Anthropic finally shipped the model we have been begging for. Smart enough to lead the frontier and affordable enough to actually use. Testing it now.
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iPhone 18 Pro Review: Better. But at What Cost?
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Even when humans were reading all of the code, we still had no definition of what good code was. So much energy has been expended in a futile attempt to define that. Does it work? Yes... Do the tests pass? Yes... Can another person easily modify it? Yes... Then why is it bad? Because it doesn't adhere to the Liskov Substitution Principle. 😡
"AI writes terrible code." Does it work? Yes. Do the tests pass? Yes. Can another AI understand and modify it? Yes. Then I have terrible news about how much your definition of terrible code matter. Humans aren't reading this stuff for much longer, many already stopped.
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