Taming agents in the wild
San Francisco, CA
Joined May 2026
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bryan retweeted
We’re committing a portion of Anthropic’s shares to the Anthropic Public Benefit Fund, a new wholly owned entity dedicated entirely to the public benefit.
It will start off with 5% of our shares. For every 50% increase in the company’s value over a set baseline, we can contribute up to an extra 2%, subject to our board’s discretion.
In total, this means that roughly 20% of Anthropic’s shares are committed to the public good. Our co-founders have already pledged to donate 80% of their equity, and around half our employees are donating a portion of their equity as part of a donation matching program we’ve run since our founding.
In the post below, we share more details about our new fund.
anthropic.com/news/public-be…
bryan retweeted
NOBODY is connecting the dots on these Gen Z protests.
India. France. Spain. Chile.
Different signs. Same problem.
Young people can't get hired.
Can't afford rent.
Can't afford a house.
Can't see a future.
At the same time, AI is quietly deleting the jobs they were supposed to start with.
Entry-level hiring has COLLAPSED since 2024.
You can't tell a generation to "work hard" when the first rung of the ladder is gone.
The streets are the only place left.
Follow + 🔔. This is going global.
bryan retweeted
Scaling self-verification with DeepSeek V4 Flash beats Claude Fable 5 on Terminal-Bench 2.1, while being 11x cheaper 💰
As open-source models become more capable, they can now generate large numbers of high-quality candidate solutions and verify their own outputs at very low cost.
For example, we find that sampling just 5 solutions with DeepSeek V4 Flash and ranking them using the same model with LLM-as-a-Verifier can lead to a significant boost in accuracy (79% → 88%), outperforming closed frontier models on Terminal-Bench.
Try it out today: github.com/llm-as-a-verifier…
More on verification scaling in my previous post.
How can we extract richer signals from AI Feedback?
Introducing LLM-as-a-Verifier✨— a simple verification scaling framework that achieves SOTA on agentic benchmarks 🚀
The key idea:
- Use fine-grained scoring granularity (e.g., 1-20 instead of the standard 1-5 scale)
- Take the expectation over the full logprob distribution of score tokens
- Scale repeated evaluation and criteria decomposition
You can use these fine-grained signals for more effective test-time scaling, RL, and agent monitoring! It achieves SOTA across Terminal-Bench V2, SWE-Bench Verified, RoboRewardBench, and MedAgentBench 👑
Advised by @Azaliamirh @istoica05 @drmapavone @chelseabfinn
🧵👇
bryan retweeted
AI to go, please. 🤏⚡
@saranormous shows how big AI can travel light with the NVIDIA Jetson Orin Nano Super. It may fit in your hand, but it is ready for your biggest robotics ideas. 🤖
Learn more 👉 nvda.ws/45tCfaE
bryan retweeted
OH: “i’ve switched to Kimi from claude for a bunch of work. it’s just so much more fun because it just does the thing instead of lecturing you”
Woke lobotomized models are the enemy of American competitiveness.