our vision
AI research is becoming an engineering discipline.
The era of single-PhD-single-paper is ending. The best results now come from systematic experimentation at scale, not individual flashes of insight.
The researchers who win will be the ones who run the most informative experiments per unit of compute, not the most experiments total.
Agents are good at writing code. They are bad at understanding what happened.
Autoresearch today is a 630-line system prompt. It works because the codebase fits in a context window. It breaks the moment you try anything real.
The bottleneck is not reasoning quality. It is context starvation. Agents make bad decisions because they receive almost no structured information about what actually happened during training.
The missing layer is intelligence infrastructure.
Someone needs to log everything that happens during a training run without slowing it down. Compress it into structured, multi-resolution summaries. Assemble the right context for each decision. Feed it to the agent at the right time.
This is not a dashboard. This is not experiment tracking. This is a new category of infrastructure that sits between your training code and the agent that decides what to do next.
No compute should sit idle.
Every GPU cycle not running an experiment is money lost. Every hour an engineer spends manually analyzing W&B dashboards is an hour the agent should have spent planning the next run.
Heterogeneous compute matters. The 3090 sitting unused in your lab can run ablations that inform what the H100 cluster does next.
The right interface is not a dashboard.
Dashboards are for humans staring at screens. The future of research interaction is: you send a voice memo from the train with a new idea, and the system implements it, tests it, and tells you what happened.
The agent should explain its findings to you. Not the other way around.
We are building this because we need it.
We are ML researchers at Harvard and MIT. We train models every day. We have wasted weeks on bugs we should have caught in the first 60 seconds of a run.
We built autolab because the tools we needed did not exist.
Open research, closed loop.
The logging layer is open source. The intelligence layer is our product. We give you deep visibility into your training runs for free. We charge for the system that turns that visibility into better experiments.