Issues search on GitHub sucks, that's why I built SemHub

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I built SemHub because issues search on GitHub sucks. We probably use it so frequently each day that we've developed schlep blindness to how much it sucks. For example: - No way to easily search across multiple repos - No way to easily see open and closed issues at the same time - Search query has to exactly match the text in the title or body of the issue That's why I built SemHub. The main innovation is that I use vector embeddings to enable semantic search. Preliminary feedback from open source maintainers all say that it works much better than GitHub's own search. To try it out, you can navigate to the homepage and just start searching. If the repo has not yet been loaded, you’ll see a prompt to index the repo. When the indexing is complete, you can search the repo. We also support indexing private repos, that would require logging in and granting SemHub permission to access your repo. You can then search across your own customized collection of repos. I am the sole developer of SemHub and I am happy to talk about the tech stack and my experience building this. This an experimental product with no plans to monetize. Feel free to ask any questions and I will do my best to answer them. I am currently working on a longer blog post to outline what I’ve learnt building this. I have also left a little easter egg, please do not share it in the comments if you find it!

Visit product View on Hacker NewsFeb 27, 2025

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

90%90% 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

54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

51%51% 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 · Strong signals: way · Missing: mobile apps, ios, personal

38%38% 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

38%38% 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

11%11% 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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