My

My never ending weekend project - recikeep.com

Hacker News

My never ending weekend project - recikeep.com

I'm nearing the finish line on a side-project that has taken me longer than I care to admit. I'd really like to get some feedback from you guys if you would: http://recikeep.com The site is centered around a bookmarklet with some extra server-side processing. You "recikeep" a recipe and the ingredients are all parsed out into Qty/Measure/Ingredient/Prep (as best as I can), and then you build menus from what you have kept. Since the ingredients are parsed out, I can generate a shopping list from your menu that's a combination of all of the ingredients across the recipes. Any feedback would be greatly appreciated. Thanks!

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AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
52%52% 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 · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
36%36% 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
12%12% 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
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

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