Codegen – The IDE for Refactors

Hacker News

Hey HN! We're excited to introduce Codegen - the IDE for Refactors. Codegen leverages static analysis to parse your codebase "graph", then wraps this in a high-level python SDK to enable safe & instantaneous codemods. We bundle this with an online editor/execution environment and closely coupled chat assistant in an online IDE. We focus on programming tasks on huge enterprise codebases where: - the output state is known (and easy to verify) - the "blast radius" is large - the difficult part is getting your codebase into the right shape. Codegen provides an AI-accelerated workflow for rapidly building and merging safe codemods to accomplish the above. Customers like Ramp use Codegen for killer apps including: Adding type coverage Removing feature flags Documentation generation Eliminating circular imports Identifying critical code paths and more. We will be publishing demos and case studies in the coming weeks demonstrating the power of our approach and the impact it's had on customer and OSS codebases. Today, we support the JS/TS and Python ecosystems and work on multiple 50mm+ line codebases. We would love any and all feedback. And please reach out if you're interested in a demo (on your own codebase) at codegen.com. Thanks

Visit product View on Hacker NewsAug 20, 2024

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

76%76% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntOn track for Day 1 leaderboard · Strong signals: apps, tasks, code · Missing: mac, agents, macos

75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

39%39% 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

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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