GetZlib – Verified Z-Library Links, Apps, and Tor (Auto-Updated)

Hacker News

Hi HN, I built GetZlib ( https://getzlib.com ), a small static site that keeps a verified, auto-updating list of working Z-Library links — including official domains, TOR addresses, and app download pages. The problem: most mirror lists online are either outdated, SEO spam, or full of ads/popups. So I made a cleaner version. How it works Next.js 15 (mostly static pages) Cloudflare Pages for hosting Scheduled Worker (cron) checks links daily Results stored in KV → rendered as JSON → served statically TOR links validated through a bridge No tracking, no client JS (except analytics) That’s basically it — a minimal “single source of truth” for Z-Library access. Looking for feedback on: More reliable ways to check onion links Whether link history / uptime charts are worth adding Better architecture for long-term link verification It’s a small side project, but I'd love ideas from the community. Thanks for reading!

Visit product View on Hacker NewsNov 26, 2025

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2points

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

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

43%43% 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 HuntUnlikely to reach the leaderboard · Strong signals: apps, single · Missing: mac, agents, macos

38%38% 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, way · Missing: mobile apps, ios, personal

38%38% 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.

Correct prediction on native model

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