![[P] We built a Weights & Biases for Autoresearch - track steps, compare experiments, and share results](https://preview.redd.it/lv7w6809f7qg1.png?width=140&height=75&auto=webp&s=e77e7b54776d5a33eb092415d26190352ad20577) | Been working on this with my team at Weco. If you're running autoresearch loops (automated ML research pipelines), you've probably hit the same wall we did — there's no clean way to monitor what's happening across steps, compare runs, or share results with collaborators. We built tooling that gives you W&B-style tracking but designed specifically for autoresearch workflows: step-by-step monitoring, performance analysis, and shareable run dashboards. Demo link here: https://x.com/zhengyaojiang/status/2034687066659791345 Would love feedback from anyone doing autoresearch... What metrics do you actually care about tracking? [link] [comments] |
[P] We built a Weights & Biases for Autoresearch - track steps, compare experiments, and share results
Reddit r/MachineLearning / 3/20/2026
📰 NewsTools & Practical UsageModels & Research
Key Points
- A team at Weco built a new Weights & Biases–style tracking tool tailored for autoresearch workflows to monitor steps, compare runs, and share results with collaborators.
- The tool aims to fill gaps in autoresearch monitoring by offering step-by-step tracking, performance analysis, and shareable dashboards.
- A demo link is provided and feedback is requested on which metrics matter most for autoresearch pipelines.
- The project emphasizes collaborative experimentation in automated ML workflows and invites community input on useful metrics.
