Munchy - a recipe search engine (pt.2)

Hacker News

Last year, I made a post to hacker news showing off a demo of a recipe search engine. The product was unfinished; there was less than 600 recipes, the search engine was primitive, and the UX was mediocre at best. After receiving a lot of feedback from the hacker news community, I went at improving the product. We now have over 150,000 recipes, are using a more sophisticated search algorithm, and have implemented over 5 additional filters to filter by your dietary preferences and needs. We are building a search engine that will help anyone access recipes that are nutritional. I believe everyone should have access to the dinner they want, no matter their dietary needs or ingredients they have.

Visit product View on Hacker NewsAug 3, 2022

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: hacker news, 000, io · Missing: https docs, excited, just released

79%79% 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 · Missing: supports, reddit linkedin, podcasting

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

Product HuntUnlikely to reach the leaderboard · Strong signals: new, using · Missing: mac, agents, macos

48%48% 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

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

36%36% 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

1%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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