A recipe parser to get rid of the cruft. I need you to break it

Hacker News

I made a recipe parser to get rid of all the cruft on recipes. Check it out: https://recipeplz.fly.dev/ Has this been done? Yes. Does this project use AI? Also yes...but barely. What it does do is grab various forms of metadata that recipe sites often provide for SEO reasons (as per schema.org Recipe spec). It's commonly ld+json, but can be other formats as well. The trick is this: What the numerous recipe sites output is not always the same - there are many variations of the schema.org specs used for Recipes, so this project attempts to correct for that. It's just a bunch of conditionals. If all else fails, it uses AI as a fallback to parse a recipe and return valid ld+json Recipe data based on Schema.org's Recipe spec. I even say "please" to the AI overlords. If a request for a recipe seems to take a long time, you'll know it's pinging OpenAI's API (the Chat API via the 3.5-turbo model). I'd love to see if you can find a recipe that breaks it!

Visit product View on Hacker NewsMay 23, 2023

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

59%59% 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.

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

57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

49%49% 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: way · Missing: mobile apps, ios, personal

36%36% 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

30%30% 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

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

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

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

Correct prediction on native model

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