Py
PyIng – Ingredient parser
Hacker NewsPyIng – Ingredient parser
For far to long ingredient parsers been unavailable to the public. Either due to obsene complexity: https://github.com/nytimes/ingredient-phrase-tagger Or because of the dreaded paywall: https://github.com/mtlynch/zestful-client Wait no longer, I introduce PyIng. An easy to use python package for changing this "2 ounces of spicy melon" into this {name: melon, unit: ounces, qty: 2.0}. https://github.com/whitew1994WW/PyIng
Share cardActual performance
107points
31comments
Made the leaderboard
Launch Intel predictions
Analyze your own launch →Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released63%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.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting35%35% 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, personal34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos19%19% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto3%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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