caniscrape – Analyze anti-bot protections before scraping (CLI and Web)

Hacker News

I built this after hitting an infinite loop pagination trap on Newegg and wasting hours building a scraper that never had a chance. caniscrape analyzes a URL and tells you: - What protections are active (WAF, CAPTCHA, rate limits, TLS fingerprinting, honeypots) - Difficulty score (0-10) - What tools/approach you'll need Install: pip install caniscrape Usage: caniscrape https://example.com Web version: https://caniscrape.org NOTE: Very hard sites like Amazon or YouTube will show as 0 because their bot protections are essentially invisible, I will fix this in a future update. NOTE FOR WEB VERSION: The web version uses a cloud IP which is blacklisted by major sites. I will add proxy rotations to defeat this but for now, the website is only good for easy/medium sites.

Visit product View on Hacker NewsOct 22, 2025

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Actual performance

3points

2comments

Did not reach leaderboard

Launch Intel predictions

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

66%66% 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.

Hacker NewsStrong engagement from HN community · Strong signals: io · 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.

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

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos

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

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

14%14% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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