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Reciped.io Solving What's for Dinner?

Hacker News

Reciped.io Solving What's for Dinner?

This is my first ShowHN, so go easy :). Public facing side is sort of a wikipedia of recipes, and ingredients, everything in markdown. When logged in, you can add recipes to your recipe book, and then it's drag and drop weekly meal planning. Grocery list gets auto-populated by the meals in your weekly list and sorted by section in the grocery store (produce, meat, dairy, spice aisle, frozen foods, ect). Grocery list itself is built for mobile, so you just swipe the item off the list as you grab it at the store. Recipes are easily forkable, so you can take a recipe and change it to suit your needs.

Visit product View on Hacker NewsAug 8, 2022

Actual performance

2points

9comments

Did not reach leaderboard

Launch Intel predictions

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

60%60% 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

58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

52%52% 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

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

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

34%34% 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, revenue

17%17% 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

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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