Biene Club

One step further: make the PRD and the evals the same artifact. Write down what good means. That one thing builds the feature and grades it. Run your PRD. venturebeat.com/security/eva...

Almost everything we've learned building AI features starts in one place: deciding what "good" means, in writing, before you build. We'll post notes on how to actually do that. Starting simple, working up.

👋 is there an eval for ai-driven kernel *maintenance*? not generation, but migration. like, cuda→hip, gfx942→gfx950, rocm n→n+1, etc. migration has a built-in verifier (the old kernel is the spec), so it should be way more benchmarkable than greenfield kernel generation who's working on this

First AI feature? Stuck between demo and production? Running live agents and hoping for the best? We're Biene Club. We build your AI, prove it works, and help you adapt. How we do it: biene.club/approach

10 short videos about LLM infrastructure to help you appreciate Pages 12-18 of the DeepSeek-v3 paper (arxiv.org/abs/2412.19437) www.youtube.com/watch?v=76gu...

Wrote an intro to evals for long-context Q&A systems: • How it differs from basic Q&A • What dimensions & metrics to eval on • How to build llm-evaluators • How to build eval datasets • Benchmarks: narratives, technical docs, multi-docs eugeneyan.com/writing/qa-e...

You built an AI feature. How do you know it's good enough to give to your users? We developed a method where your intent becomes a testable definition of "good," and built Beeline to show you how accessible this method is. Read it, then steal it 🐝 www.biene.club/blog/2026-07...