AI Code Reviews with deep code understanding

Hacker News

hey HN! There's lots of AI Code Review tools by now. Our experience with these tools has been quite poor so far so we decided to tackle this space differently. We started building Callstack as a deep code understanding engine (you can check out the UI here: https://graph.callstack.ai ) to understand complex codebases to a granular structural level. The premise for this effort is to serve an LLM with a deterministic context retrieval over a complex codebases which impacts the performance by a large margin. With few experiments this enabled us to perform really well over simple tasks so we have shipped code reviews, talked to users and today it runs in production at companies of all sizes. Today we are launching self-service for the Code Reviewer. It's completely free for individual use and open-source repos. It's also fully private, it runs as a Github / Gitlab action (the only 3rd party API request is to Anthropic) and supports 9 major languages. You can try it out in less than 3 minutes with no credit card required, just install it on your Github / Gitlab repo. Would love to know your feedback!

Visit product View on Hacker NewsOct 31, 2024

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

95%95% 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 · Strong signals: supports, started · Missing: reddit linkedin, podcasting, created

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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.

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

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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

Incorrect prediction on native model

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