Codegen – SWE Agents for any repo

Hacker News

Hey folks, excited to launch Codegen: a platform for SWE agents. Codegen lets you prompt agents to perform programming tasks across code, Github, Linear, Slack and more. Ask for impressively complex code modifications and watch it autonomously produce pull requests, respond to feedback, diagnose issues, run tests and more. Try the following: - "@codegen Can you add stripe checkout to my frontend?" - "@codegen Who is the top contributor to my repo last week?" - "@codegen Can you research and flesh out this Linear ticket?" Codegen has full access to an isolated dev machine, ability to search the web, and is freakishly smart due to the incredible agentic capabilities of Claude 3.7. This is based on the Claude Code architecture: a single-strand agent with tool use. We also support an API ( https://docs.codegen.com/introduction/api ) for programmatically triggering agents. Rig them up to run as part of your CI, etc. Give it a spin! Free to try at codegen.com. Would love any feedback that folks are willing to provide!

Visit product View on Hacker NewsApr 18, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agents, agent · Missing: macos, cursor, model

99%99% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, io · Missing: https docs, just released, exist

64%64% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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