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The missing manual for ingredient substitutions
Hacker NewsThe missing manual for ingredient substitutions
I’m new to cooking, and one of the things that frustrated me right away was how often a recipe calls for something I don’t have: arrowroot, Aleppo peppers, or some random spice that isn’t in my pantry. I ended up putting together a simple site that catalogs ingredient substitutions in plain English: ingredientsubstitutes.com. It’s not fancy, but it’s been surprisingly useful for me (and hopefully others). Curious: how do you all normally handle substitutions?
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Product HuntOn track for Day 1 leaderboard · Strong signals: new, plain · Missing: mac, agents, macos69%69% 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, podcasting56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: calls · Missing: plus, platform, intuitive55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released51%51% 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, personal31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit25%25% 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, crypto1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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