Meal planning – without the mental load

Hacker News

TLDR; I applied the concept of "don't make me think" to the task of selecting meals, and assembling the shopping list for your grocery run. The basic idea for what I wanted is very simple: Rather than making a shopping list, I wanted to create a re-usable 'meal', with a list of ingredients I'd need to add to my shopping list to make that meal. Then, after selecting meals for the week I'd have a quick 'check' step, where I'm prompted to check the cupboard for each ingredient, before it's added to the shopping list (ie: I'll need ground beef to make tacos, but I already HAVE ground beef in the freezer). I originally built this out just for myself, but the result has been such a helpful and stress-free experience that I thought others might appreciate it as well. I think this tool could be well-suited for younger folks that are new to the labour of meal-planning. College students, newlyweds, and families with young children. You can try it out without needing to register or provide any personal information, and I'd love your feedback! https://supperstock.ca/

Visit product View on Hacker NewsMay 27, 2024

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

89%89% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

13%13% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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