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Streaky – GitHub Streak Monitor with Distributed Cron Processing

Hacker News

Streaky – GitHub Streak Monitor with Distributed Cron Processing

Hi HN! I built Streaky to solve a personal problem - I kept losing my GitHub streak on busy days. It monitors your contribution streak and sends notifications to Discord/Telegram before it breaks. What makes it interesting technically: 1. Distributed Cron Processing : Used Cloudflare Service Bindings to bypass the 30-second CPU limit. Each user gets processed in an isolated Worker instance with its own CPU budget. 2. Idempotent Queue System : D1-based queue with atomic operations prevents duplicate processing when cron jobs overlap or retry. 3. Zero-Knowledge Security : GitHub tokens never stored (OAuth refresh flow), webhooks encrypted with AES-256-GCM, notifications sent via isolated Rust proxy. 4. Rate Limit Solution : Cloudflare Workers use shared IP pools which trigger rate limits from Discord/Telegram. Solved by routing notifications through a dedicated Rust server on Koyeb. Tech Stack: - Frontend: Next.js 15, React 19, TypeScript - Backend: Cloudflare Workers + D1 (SQLite) - Infrastructure: Rust notification proxy - Auth: GitHub OAuth via NextAuth.js v5 Live demo : https://streakyy.vercel.app The project is fully open-source under MIT license. Happy to answer any questions about the architecture or implementation

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, open · Missing: mac, agents, macos
53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs
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
24%24% 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.

Incorrect prediction on native model

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