CodeRoutine – one tech article per day, AI summaries and podcasts

Hacker News

Hi HN, I’ve been building CodeRoutine to solve a problem I had: a lightweight way to learn consistently without infinite feeds. The app shows one handpicked CS/tech article each day and helps you build a reading habit. No accounts required; data stays on device (except minimal anonymous analytics like read count). What it does - Daily article: one curated piece per day across CS/software topics - Streaks/progress: mark “read” within 24h; see your streaks and topic XP (+1 per topic) - AI summaries: quick key points when time is tight - Podcast mode: auto-generated audio to listen on the go - Translations: summaries in IT/ES/DE/FR - Offline reading (summaries), favorites, dark/light theme - Privacy-first: local by default; optional cloud sync planned Under the hood - React Native + Expo - Firebase Firestore for articles; anonymous analytics (read count, likes, dislikes) - Google Cloud (Functions, Vertex AI) for summaries, translations, podcasts - Push notifications via Expo; no authentication required for core use Why I built it - I keep a long backlog of tech articles but often don’t finish them. “One article per day” adds urgency without overwhelm, and the streak mechanic keeps me honest. Try it / Source - GitHub (OSS, MIT): https://github.com/edodusi/coderoutine-oss - Android (Google Play): https://play.google.com/store/apps/details?id=com.edodusi.co... What I’m looking for - Feedback on habit design (24h window, streaks): motivating or annoying? - Technical critiques (Firebase vs REST API boundaries, AI generation, push/analytics) - Ideas for improving the reading experience in the WebView - Bug reports and PRs are welcome Notes - Works fully without accounts; pricing is transparent (free app; optional subscription for advanced AI features like podcasts/summaries). Happy to answer questions and share more details. Thanks!

Visit product View on Hacker NewsSep 30, 2025

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

70%70% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

35%35% 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

25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: subscription · 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 · Strong signals: audio · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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