Lunchbox Ninja

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Lunchbox Ninja

[resubmitting because my previous post accidentally didn't include the main link] After a long period of finding entire lunches uneaten at the end of the day, we started asking our kids each day what they want for lunch the next day. While it wasn't the only thing that changed, giving them the choice has definitely had a positive impact on how much food gets wasted and how often kids come home hungry. This year, I took it a step further by letting the kids "order" their lunches using a visual form on my phone. They absolutely love it!!! I'm considering building an app to make this easy for other parents to do. If this sounds interesting to you, please consider registering your interest at https://areyouinterested.co/site/Lunchbox-Ninja

Visit product View on Hacker NewsOct 6, 2020

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

81%81% 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.

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

62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

59%59% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntOn track for Day 1 leaderboard · Strong signals: visual, using · Missing: mac, agents, macos

53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

45%45% 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

19%19% 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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