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Eulix - Code navigation for large codebases

Hacker News

Eulix - Code navigation for large codebases

Hey I've been working on Eulix, a tool for navigating large codebases. It parses a repository into symbols, call graphs and other structural information, then combines that with keyword and semantic retrieval to find relevant code. I tested it on OpenStack (~6.9M LOC / 29k files). One query about Nova's PCI passthrough scheduling pulled back the relevant filters, helpers and related call paths in well under a second once indexed. Some queries don't need an LLM at all, since Eulix can answer directly from the structured codebase data. It's open source and runs locally: https://github.com/Nurysso/eulix I'd especially like feedback from people who've worked on code search, static analysis, or large monorepos. on a side note it may be able to handle 30M+ loc codebase too, I haven't been able to test such huge repos cause I don't have a good enough gpu to embed parsers output! :)

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Product HuntOn track for Day 1 leaderboard · Strong signals: code, open · Missing: mac, agents, macos
86%86% 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.
Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released
67%67% 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 · Missing: supports, reddit linkedin, podcasting
57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
35%35% predicted probability of success on TrustMRR, 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 · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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