Runo – Scraping API that returns typed JSON, not raw HTML

Hacker News

Currently, most scraping APIs aren’t capable of much more then fetching. You get a dump of html back and you’re on your own. I built Runo to take a schema and return typed, validated JSON directly. No parsers or selectors and nothing to maintain. Runo takes a schema: { "field": "price", "type": "float", "example": 29.99 } { "field": "inStock", "type": "boolean", "example": true } You get the exact data you want back, typed and clean. Nothing else to do. JS rendering is handled automatically. First it’s plain fetch, if that fails it escalates to playwright stealth. Nothing to configure on your end.

Visit product View on Hacker NewsMay 20, 2026

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

70%70% 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 HuntOn track for Day 1 leaderboard · Strong signals: apis, plain · Missing: mac, agents, macos

64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

45%45% 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

27%27% 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

27%27% 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

26%26% 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

18%18% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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