I made a website to clean up recipe websites

Hacker News

Hi hn! It has always annoyed me that recipe websites are so cluttered and have so much (in my opinion) useless fluff, so I made a little website to solve that problem! One good thing about SEO for once is that Google has pretty much forced the internet to adopt JSON-LD for recipes so many recipes are directly extractable and for others I have several fall back parsers including an indieweb parser :) If you do find a website that is broken feel free to click the flag button or share it as I would love to fix any edge cases

Visit product View on Hacker NewsApr 27, 2026

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

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

nativeThis product was originally launched on this platform.

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

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

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

52%52% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

32%32% 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

15%15% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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