I made privacy first book tracker with recommendations

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Hi HN! I'm Ivan, a developer and avid reader who was constantly frustrated by the "what to read next?" problem after finishing a great book. I tried tracking books on my phone, but it felt clunky and unreliable—I worried about losing my data when switching devices. When I looked at existing online solutions, they felt overly complex and bloated for what should be a simple task. So I built MyRead: a fast, modern book tracker that's both private and persistent, running entirely in your browser. Core philosophy: your data belongs to you. Everything is stored exclusively in your browser's localStorage, making the app fast, offline-capable, and completely private. Key features: - All data lives in your browser. No cloud, no sign-up required. - Back up your entire library or move between devices using JSON/CSV files. Your data is never locked in. - Instead of a black box, plug in your own API key (OpenAI, Google AI, or OpenRouter). The app generates detailed prompts based on your library and sends them directly from your browser to your chosen provider. I never see your data or API key. - Clean interface for progress, notes, and ratings, plus a Tinder-like swipe UI for discovering recommendations. Technical details: - Stack: React, TypeScript, Vite, Tailwind CSS, shadcn/ui - Fully client-side PWA - Client-side prompt generation analyzing your books, wishlist, genres, authors, and past recommendation feedback The BYOK model is central to the privacy approach—your reading data and AI interactions stay completely between you and your chosen provider. I'll be here all day for questions and would love feedback from the HN community. Thanks for checking it out!

Visit product View on Hacker NewsJun 26, 2025

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81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

79%79% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: plus, exclusive, interface · Missing: platform, intuitive, reviews

52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

40%40% 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

17%17% 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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