This is what TDD was supposed to be
We formally verified 123 open-source projects in a partnership with Anthropic. Formal verification at scale is here!
Now we’re launching OSS Verify to scale it to the software we all depend on. We prove it’s crash-free or send you the patches it needs. Free for open-source. Let’s bootstrap the verified software ecosystem: theorem.dev/oss-verify
Sami Jawhar retweeted
DEADLINE ANNOUNCED: Apply by April 26th to LASR Labs AI Safety research program. This is a 3-month paid opportunity to write a paper in a small team with expert supervision. Past work has been published at workshops/conferences. Previous alumni now at Open AI & UKAISI.
Sami Jawhar retweeted
METR is running a pilot field experiment to measure how AI tools affect open source developer productivity.
If you're an open source developer who wants to make $150/hour to work on issues of your own choosing - consider expressing interest 👇
Sami Jawhar retweeted
Can frontier models cost-effectively accelerate ML workloads via optimizing GPU kernels? Our take at METR: yes, and they’re improving pretty steeply – but it’s easy to miss these capabilities without good elicitation and “fair” compute spend.

ALT We measure average speedup achieved on an improved version of KernelBench, comparing our “KernelAgent” to the KernelBench leaderboard. The pink line represents KernelAgent using each model of 4o, sonnet, o1, and o3-mini, and the red line represents the performance of the best soluton found by models so far. Using the same models as the original KernelBench paper, KernelAgent achieves a speedup of 1.81x vs KernelBench's 1.05x. We attribute this to scaffolding improvements and higher test-time compute spend -- as these tasks are easily checkible, we do best-of-k across agent attempts and spend around $20 per task per model (over an estimated <$1 for results on the KernelBench leaderboard).
Sami Jawhar retweeted
How close are current AI agents to automating AI R&D? Our new ML research engineering benchmark (RE-Bench) addresses this question by directly comparing frontier models such as Claude 3.5 Sonnet and o1-preview with 50+ human experts on 7 challenging research engineering tasks.
Sami Jawhar retweeted
How well can LLM agents complete diverse tasks compared to skilled humans? Our preliminary results indicate that our baseline agents based on several public models (Claude 3.5 Sonnet and GPT-4o) complete a proportion of tasks similar to what humans can do in ~30 minutes. 🧵
@smfleming great talk at TCPW, and an exciting response to my question about the link with awareness/intrusive thoughts. Looking forward to exploring that connection further
Sami Jawhar retweeted
We received FDA authorization to measure ketamine’s psychedelic effect on the brain using @kernelco Flow with our partner @CybinInc
Sami Jawhar retweeted
Anyone looking for a broad and deep perspective on current computational neuroscience & its intersection with AI could do a lot worse than going through the entire back catalouge of @pgmid 's BrainInspired podcast braininspired.co/
Sami Jawhar retweeted
Announcing @KernelCo Flux: the World’s Most Powerful Brain Interface
kernel.com/news/announcing-k…
It's rough, meandering, and overlong, but at least I finally finished getting my thoughts down in a somewhat intelligible form. Something I've been thinking about for a while: what does the mind process tells us about responsibility and justice?
medium.com/@thecybermonk/the…
The latest in my series of articles on the "mind process" is a practical guide to mental training and mindfulness. It's not woo, it's just physics!
medium.com/@thecybermonk/pra…












