ArchiveJump – Browser extension to instantly jump to archived articles

Hacker News

Hi HN! I built ArchiveJump, a browser extension that automatically redirects article links from major news sites to their archived versions on Archive.ph. THE PROBLEM: Reading articles from sites like NYT, WSJ, The Atlantic, etc. often hits paywalls. While Archive.ph exists, manually copying URLs and searching is tedious. THE SOLUTION: ArchiveJump detects when you click article links from 15+ supported news sites and either: - Searches Archive.ph for existing archived versions, OR - Jumps directly to the latest archived version (toggle option) KEY FEATURES: • Smart detection (only article links, not homepages) • Two modes: search archive.ph OR jump to latest version • Easy toggle controls via popup • Manual "jump" button for any page • Visual feedback (blue/green notifications) TECHNICAL DETAILS: - Vanilla JavaScript, Manifest V3 - Works on Chrome/Edge - No data collection, settings stored locally - Content script + popup interface The extension respects sites' content while leveraging publicly available archives. It's particularly useful for research, fact-checking, or when you've hit monthly limits. GitHub: https://github.com/dhrm1k/ArchiveJump Install: Load unpacked in Developer Mode (Chrome Web Store submission coming) Would love feedback on the UX, additional sites to support, or feature suggestions! Note: This tool uses Archive.ph, which archives publicly available content. Please respect publishers by subscribing if you regularly read their content.

Visit product View on Hacker NewsJun 28, 2025

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

70%70% 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.

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

63%63% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntOn track for Day 1 leaderboard · Strong signals: new, visual · Missing: mac, agents, macos

50%50% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

26%26% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · 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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