Cross-repository understanding using static analysis and selective AI

Hacker News

Built this because I kept losing context when working across multiple services. You know the problem: "what breaks if I change this auth function?" requires manually tracing through 5+ repositories to find all the dependencies. Technical approach: - Tree-sitter AST parsing for reliable dependency extraction across languages - Graph storage for efficient relationship traversal - AI only for semantic pattern matching on structured data (not raw code analysis) - Real-time impact analysis without the accuracy problems of pure semantic approaches Key insight: Don't trust AI for detailed code analysis (research shows 15-20% error rates), but use it for broader connections between components that static analysis has already verified. Differentiation from existing tools: - Sourcegraph: excellent single-repo navigation, limited cross-repo understanding - Glean: searches documentation, this analyzes actual code relationships - GitHub dependency graph: package-level tracking, this maps business logic flow Works whether you have microservices or complex monorepo with multiple domains. Planning to open source core components since existing solutions are enterprise-only. Early feedback welcome.

Visit product View on Hacker NewsJul 21, 2025

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

71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

Hacker NewsMay not resonate with HN audience · Strong signals: exist, open source, existing · Missing: https docs, excited, just released

40%40% 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: efficient · Missing: plus, platform, intuitive

33%33% 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

14%14% 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.

Correct prediction on native model

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