# RepoLens

- **Event:** [Meta Global AI Developer Hackathon](https://cerebralvalley.ai/e/meta-ai-developer-hackathon)
- **When:** Oct 26, 12:00 PM – Nov 4, 9:00 PM (EST)
- **Where:** Online
- **Team:** [Gautam Gottipati](https://cerebralvalley.ai/u/gautam_gottipati)
- **GitHub:** https://github.com/GautamGottipati/RepoLens
- **Demo video:** https://youtu.be/L5uLESwVO9s
- **Gallery:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1365

RepoLens is live : https://repolens.net/


RepoLens is a learning environment for developers that turns any GitHub repository into a hands-on experience. Paste a repo URL, and RepoLens
  investigates the code, maps how it works, and lets you run and question it — so you learn the system by touching it, not by reading about it.

  Unlike regular chat applications, which answer from training data, RepoLens works from the repository itself:

  1. Verified architecture, cited to source. It generates the system's architecture — components, flows, modules — with every claim tagged to the source files and lines responsible, checked against the actual commit. Nothing is asserted without evidence.

  2. Hands-on sandbox, intelligently chosen. It figures out the best way to run the project — a data playground, a real CLI, a library console  — and builds a sandbox for it, with Muse Spark 1.3 reasoning over the hardest setup decisions. You get a working environment, not a setup  guide.

  3. Experimental answers, not textbook answers. When you ask "why?", it can execute the repository on the fly, run a real experiment, measure  the result, and explain what it observed — with statistics computed by deterministic code, never invented by the model.

  All reasoning runs on Muse Spark via the Meta Model API, orchestrated by a cost-aware tiered router: muse-spark-1.1 for fast triage, 1.2 as  the everyday workhorse, and 1.3 for the deepest reasoning. Research uses the same key through built-in web search. Agents choose actions, deterministic tools act, and application code validates everything a model returns — so the system shows its work, every step of the way.

## More from Meta Global AI Developer Hackathon

- [Questory](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1362)
- [Ares](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1363)
- [Orbit Fitting Room](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1364)
- [Glint](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1366)
- [SAF-Integration](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1367)
- [ALMA PROJECT TEAM](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1368)

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