lorenss

founder @hud_evals. prev cogsci @yale. open to talking about natural intelligence, flowers, and the human condition

[lorenss @seeklis]

Shop and train https://t.co/ldyQLLbn2F

[DataVendor @datavendor_ai]

We're announcing the public beta of https://t.co/YgMb6QF79k

We spent the last two years building the best solution for people buying and selling data, whether you're a researcher in a lab or a company trying to sell data.

Data is the fundamental bottleneck for AI progress, and DataVendor will be the infra that solves it.

[lorenss @seeklis]

QA your coding tasks on HUD before training on @FireworksAI_HQ 🙏

[hud @hud_evals]

New cookbook with @FireworksAI_HQ! Prepare your environment and taskset in HUD, then train a model on them with Fireworks.

[hud @hud_evals]

We're releasing a new cookbook with @daytonaio!

You can now build a coding environment in 80 lines, evaluate at up to 256 parallel sandboxes, and immediately train on the graded rollouts.

[lorenss @seeklis]

Some of the most impressive robotics environments done at hud!

[hud @hud_evals]

We created Assemble Bench, a benchmark for robot models based on the NIST standard for small-parts assembly + a synthetic post-training approach we refer to as CG-DAgger.

Our goal was to explore simulated robotics environments and to see if they - like environments for coding agents - could serve as training environments for vision-language-action and world-action models (VLAs, WAMs).

[hud @hud_evals]

Today, HUD is excited to share our Series A funding!

We are the platform for building high quality post training datasets. Over 50 businesses use HUD to build RL environments, sell them to AI labs, or train their own models from them.

Our mission is to enable a generation of data entrepreneurs. The previous generation built apps to impact the world. We believe people will build infrastructure around data, both digital and physical, that align AIs to their specific goals.

We’ve raised $16M in total funding from @Standard_Cap, @ycombinator, @ExceptionalCap, @Liquid2V, @twentytwovc , amazing angels such as @dylan522p from @SemiAnalysis_, @tszzl, @ivanburazin, @theo, and more!

[lorenss @seeklis]

we're wrong when we say RL will only lead to spikes in domain-specific intelligence. just like world models emerged from scaling single-token language inference, what emerges through scaled reasoning in deepseek is implicit self-reflection.