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Canary – Monitor any URL for changes, get AI-powered intelligence

Hacker News

Canary – Monitor any URL for changes, get AI-powered intelligence

Canary watches any URL and alerts you when something changes — competitor pricing, API endpoints, docs, job boards, status pages. Changes are scored 1-10 by signal strength and summarised by AI. Built solo. Landing page live today. Automated crawler running on GitHub Actions posting real detected changes to Bluesky hourly and X daily. canary-landing-one.vercel.app Happy to answer questions about the architecture — particularly proud of the URL deduplication approach where one crawl serves multiple subscribers.

Visit product View on Hacker NewsMar 28, 2026

Actual performance

2points

2comments

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

44%44% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

32%32% 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 · Strong signals: subscribers · Missing: arr, mrr, revenue

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

15%15% predicted probability of success on AppSumo, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

12%12% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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