Daniel Lee (@dylayed) profile picture

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San Francisco, CA
Joined September 2015
Photo by Daniel Lee (@dylayed)Photo by Daniel Lee (@dylayed)Photo by Daniel Lee (@dylayed)Photo by Daniel Lee (@dylayed)Photo by Daniel Lee (@dylayed)Photo by Daniel Lee (@dylayed)Photo by Daniel Lee (@dylayed)Photo by Daniel Lee (@dylayed)Photo by Daniel Lee (@dylayed)Photo by Daniel Lee (@dylayed)
Daniel Lee retweeted
Artificial Analysis v4.3.2 to v5, AI models re-ranking step by step.
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Daniel Lee retweeted
Meet hunch! Jev-like decisions in ~20ms without even needing a GPU, learning live from a teacher model. Built on @GoogleGemma's EmbeddingGemma 2, it can works with any teacher model like @CloudFlare's Clef. Multimodal, w/a systemone endpoint, slots into your existing stack 🧵
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Introducing Gemini 4 Carbon, it's a big jump over Argon which we released just 2 weeks ago. Gemini 4 Carbon is SOTA on cyber, long horizon tasks, ranks #1 on Artificial Analysis V5, significantly improves on usability in domains like coding, and is more aligned.
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Daniel Lee retweeted
It's been a while since I showed any Jev stuff but I've been cooking on a way to use @GoogleDeepMind's EmbeddingGemma2 as an insanely fast real-time /v1/systemone endpoint (20ms on text, 100ms on images). It's trainable in real-time, can distill an upstream teacher model, and is multi-modal by default. Hard to believe a @googlegemma model of this size can do so well with the right harness!
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Daniel Lee retweeted
Introducing 'stripe pay', for instant business-to-business transfers. Find companies using their Stripe profile handle and send funds for free. Available in the Dashboard and CLI.
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Daniel Lee retweeted
ok to put what i'm saying about demand in context openrouter is the most successful model router, you can almost serve 100% of their deepseek traffic yesterday on a single GB300 rack this is worth 6-7M...even if there's 10x this demand secretly that's still < 100M of compute
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Daniel Lee retweeted
At @Lovable we invoke 753.6B Workers per month. I shared how we implemented an isomorphic execution model using @tan_stack at Cloudflare Connect. Code written by lovable runs seamlessly between the client and server. Enabling SSR and better SEO for our users.
With 70 million AI-generated apps created on its platform, @Lovable migrated from Next.js to TanStack Start on Cloudflare Workers. Co-founder + CTO @FabianHedin breaks down: - Serving millions of apps on demand with minimal cold starts - Why they chose TanStack Start - Lessons from dogfooding (running Lovable itself just like any other app on the platform) Full talk from Cloudflare Connect.
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Daniel Lee retweeted
In the spirit of transparency and openness, we are releasing the traces so developers can test this out for themselves. We noticed this behaviour without prefilling with the following prompt, but this is rare. If you want to reproduce it, you can use this prompt with prefilling. Open models make for better science and are part of why this problem came to light at all: developers running Qwen3.8 on their own machines spotted the strange links, and having the weights let us study the behaviour in depth. We're encouraged by the thoughtful discussion our research has sparked across the AI and security communities. It's our belief that open-weight models are an important part of the future of AI, and we've recently released several under the Apache 2.0 license. Trace, raw tokens, & minimal reproduction script: huggingface.co/datasets/Cohe…
Replying to @cohere
could you share the full traces? Very important artefact for all to understand this!
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Rivet 3.0 is built on S3 But we did something completely different... And oohhhh boy: - 6.9 ms actor cold starts (durable) - 0.69 ms SQLite writes (3 availability zone) - 0.20 ms SQLite reads Here's how we built our new storage engine, WarpDrive. ~~~ Most S3-backed database read & write straight to S3. That means: - Every commit & cache miss is an S3 request ($$$) - S3 downtime takes you down (SLA allows 8 hours downtime/yr!) - S3 Express is limited to one availability zone This usually ends up slow, expensive, and has poor tail latencies. For Rivet 3.0, we decided we could do better. We wanted 3.0 to ship with: - SSD speeds with storage on S3 - Stateless nodes - No dependencies ~~~ To do that, we built WarpDrive to durably write your data on fast disks *before* we write it to S3. Every write on WarpDrive is: 1. Written to disk in 3 availability zones 2. Acked once a majority has them 3. Then batched to S3 in the background That means that Rivet has: - Writes that get durably acknowledged insanely fast - Fewer S3 operations by batching in the background - Survives S3 outages for new & warmed actors - Nodes remain stateless - S3 is the only dependency ~~~ WarpDrive is something we're incredibly proud of and can't wait to have you try for yourself. Rivet 3.0 with WarpDrive is in preview today. It's available on Rivet Cloud or for self-hosting today. Please send feedback on X, Discord, or DMs -- we want to hear anything and everything you have to say (please bump me if I missed a message!) There should be no reason to vendor lock yourself to Cloudflare Durable Objects if you can have better performance, better DX/AX, lower cost, and more flexibility on software you own. And of course, it's all open source.
Introducing Rivet 3.0 (preview) Built on S3 with SSD speeds 🏎️ 8ms actor coldstarts 🪶 0.7ms SQLite writes 🤏 78 KB of RAM per actor 🍱 Embed, self-host, or BYOC 📁 S3 is the only dependency, no database
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Daniel Lee retweeted
No matter how much they pretend to be, sandboxes are not serverless. Most sandbox providers have a fatal flaw: designing around compute. A storage-first model will be a requirement to meet the demands of agents and truly achieve "serverless intelligence."
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Introducing Gemini API CLI (gemini-api), now available in preview! Our CLI gives your agent power to generate images, videos, text-to-speech, or music, analyze media, transcribe audio, or manage agents. No more guessing model strings, throwaway scripts or endless curl. 🧵 👇
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SQLite is the most used database in the world, and its biggest weakness has always been the same: it runs on one machine, and if that machine dies, your data is gone. The usual fix is Litestream, but that's a Go binary you have to babysit. So we (@platformatic) rewrote it in TypeScript.
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Daniel Lee retweeted
We open sourced a *really fast* HTML artifact builder. It's powered by libfx, making the agent durable. Building the low-level plumbing for v0 to survive server crashes, flaky LLMs, function limits was a huge pain. Now it's easy, free, and OSS: fx0.vercel.dev. PS: I built this floating container in a few minutes… it's delightful, and it's just HTML!
We built a 'mini v0' on libfx to showcase its speed and durability: • Design or prototype anything in seconds • 200+ models from Vercel AI Gateway • Outputs are pure HTML artifacts • Fork it & make it your own Try it free ↓ fx0.vercel.dev
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If your app is RAM-bound and tight container limits, serverless cold starts, and high memory bills are hitting you, this is the easiest 50% memory win you'll ever test. Two minutes with Docker. Try it: github.com/platformatic/node…
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Daniel Lee retweeted
New from Vercel Labs: tsc-ts TypeScript was written in TypeScript Then rewritten in Go We ported it back to TypeScript Compiled it with scriptc And it's 2-4x faster than tsgo
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attention google ai pro subscribers: you can now build with gemini 4 argon in ai studio build / playground 🛠️ get started here: ai.studio/build
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Daniel Lee retweeted
fx v0.0.14 libfx is now durable out of the box. Same API on local, Vercel and DIY. - Sessions survive crashes, redeploys & timeouts - Power cloud agents with ChatGPT & Grok subscriptions - Custom providers & local model support in libfx - Run multiple libfx agents in one app - More control over each libfx session - TypeScript types for libfx's public APIs - Ported to Zig 0.17 - Multiple bug fixes & security improvements fx.sh/changelog#v0.0.14
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Daniel Lee retweeted
Wrote up how we're trying to reduce PR review friction with concept of "small-change" as PRs/eng/week has increased 10x on some teams. (Aside: incidents down 42% HoH, critical incidents down 78%! Velocity vs quality never been a falser dichotomy.) lethain.com/small-changes/
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