Web Scraping and Crawling – FetchRelay

Hacker News

Hey everyone. I am Suraj - building FetchRelay. FetchRelay converts public webpages into structured data - md, text, links.. Features include single page extraction, running crawl jobs, batch jobs, and webpage change monitoring. You can try it out via the browser based workbench, API, or MCP for agents. There are also some nice tools you can try out : website to markdown, website to text and, url extractor. Extraction works with HTTP fetching and browser rederning. ou can also run crawl, batch jobs, and page monitors. The webparser integration is generic and works for a lot of a pages. I'm still working on login failures and captcha touchpoints. Do try it out and integrate it in your workflows - would appreciate the feedback. You get free monthly credits. Happy to answer questions on the implementation. Thanks.

Visit product View on Hacker NewsSep 7, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, mcp · Missing: mac, macos, cursor

87%87% 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 HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

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

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

55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

27%27% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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

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

20%20% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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