Codegen - AI developer that analyzes + improves codebase 24/7 in Slack

@kay_arkain great point - if you are doing something that requires extensive manual debugging, then I would recommend a more in-the-loop development process, for example Windsurf/Cursor/Cody. If, however, the bugs are something that would be caught by your tests/linter/CI, then Codegen is capable of reacting to your tests!a No need to pull down the changes.

Where Codegen really shines through is in scenarios like small bug fixes, replicating boilerplate, asking questions about your code, etc., and you want the result fast

@faizanjan_ Thanks! We've implemented many optimizations to make it work well with large repositories, including optimized search functionality and more. That being said, in our experience the current generation of foundation models (Claude 3.7 etc.) are actually incredibly skilled at navigating large repositories even given a limited toolset. Try it yourself and I think you'll find it capable of doing really valuable work even in large repos or scenarios with hundreds of microservices!

@samuel_briskar 🚀

@jonurbonas thanks! Team members working on different parts of the same project should have no issues. Each codegen agent gets it's own development environment and submits a unique PR - you and I can each be chatting up an agent in separate slack threads and produce outputs independently. You can resolve merge conflicts etc. with your regular version control flow, e.g. via Github. Hope that answers your question!

@winrey Thanks! We are also super bullish on Linear. It's easy to spin up hundreds of agents that produce real, valuable output, but managing/triaging that output can be a pain. Our linear integration makes managing these agents much easier!