IssueScout – Find open source issues worth contributing to

Hacker News

I built IssueScout to solve a problem I had as a beginner looking to contribute to open source: GitHub has hundreds of thousands of "good first issue" labeled issues, but there's no way to know if the repo behind one is actively maintained or if the issue is actually beginner-friendly. IssueScout adds two things on top of GitHub's search: 1. A Community Health Score (0-100) per repository — computed from 7 factors: CONTRIBUTING.md, license, code of conduct, recent activity, star count, issue response time, and PR merge rate. A score of 80+ means someone will actually review your PR. 2. AI Difficulty Estimation — a rule-based keyword analyzer runs first. If confidence is below 80%, it falls back to GPT-4o-mini. A purple sparkle shows when AI was used. Architecture choices that might be interesting to HN: - Each user's GitHub OAuth token powers their own API requests (5K/hr per user) instead of a single server PAT. Scales linearly with users. - Two-level caching: issue difficulty cached 24h, repo health cached permanently with stale-while-revalidate at 48h. The IndexedRepo collection grows over time into a shared database of scored repos. - Two-phase progressive loading: raw GitHub results return instantly, enrichment fills in asynchronously. No spinners. - Rule-based AI first, LLM fallback only when needed. Keeps costs near zero for most queries. Stack: Next.js 16, TypeScript, MongoDB Atlas, GitHub GraphQL API, OpenAI GPT-4o-mini, Vercel. Live at https://issuescout.dev — sign in with GitHub to try it. MIT licensed. Full architecture docs: https://github.com/turazashvili/issuescout.dev/blob/main/doc...

Visit product View on Hacker NewsFeb 28, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, openai, single · 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.

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

52%52% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

47%47% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

18%18% 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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