I made a game for my 2.5 yr old niece and she loved it

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https://github.com/MichaelDiBernardo/bugs?tab=readme-ov-file... My 2.5-year-old niece likes insects. Last week, I saw her zooming into photos of bugs on her mom's phone, and I had an idea for her first "videogame." I thought she might enjoy a simple game where she could look under rocks for bugs. But, to keep it surprising, sometimes there should be non-bugs under the rocks. I wanted it to have a geocities-era blursed vibe to it. My partner decided that the surprises should be emojis of our niece’s favorite things. I chose to make it a progressive web app, because that way my niece could recognize the app on her mom's phone by the bug icon (she can't read yet.) You can find the game at https://bugs.mikedebo.com . Make sure to ask your parents if it's OK first. I'd love to share videos of my niece playing it for the first time, but for obvious reasons I won't be doing that. There was a lot of excited shrieking involved, which made me very happy.

Visit product View on Hacker NewsAug 29, 2024

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

70%70% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

50%50% 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 · Strong signals: video, way · Missing: mobile apps, ios, personal

44%44% 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

43%43% predicted probability of success on AppSumo, 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

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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