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I built SemHub to perform semantic search on GitHub issues

Hacker News

I built SemHub to perform semantic search on GitHub issues

I built SemHub because I was not satisfied with the default search experience on GitHub. For example: - No way to easily search across multiple repos - No way to easily see open and closed issues at the same time - You have to use search terms that exactly the title or body of the issue At Coder, we have multiple open-source and private repos. To better track issues across these repos, we built a semantic search feature into our internal tool, which worked surprisingly well! We thought the larger open-source community may appreciate a similar solution. One thing led to another and...here is SemHub! 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. Depending on the number of issues of the repo, it could take a while to complete. 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. Feel free to ask any questions and I will do my best to answer them. I have also left a little easter egg, please do not share it in the comments if you find it! I am very keen to receive feedback on SemHub and to hear pain points you face when using GitHub or maintaining an open source repo generally. This an experimental product with no plans to monetize. Where possible, I have also tried to improve on the default GitHub UX. Regarding open sourcing the code, we may do this once the deployment story is clearer — right now, it’s deployed on a mix of AWS Cloudfront + Cloudflare Workers and Workflow + Supabase. Thanks for reading all the way to the end!

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Product HuntOn track for Day 1 leaderboard · Strong signals: using, code, open · Missing: mac, agents, macos
89%89% 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
64%64% 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
60%60% 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 · 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 · Strong signals: way · Missing: mobile apps, ios, personal
33%33% 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
10%10% 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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