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Gachari, a tiny cultural gachapon I built solo (webapp)

Hacker News

Gachari, a tiny cultural gachapon I built solo (webapp)

I’ve always been passionate about Japan, and during my last trip I discovered gachapons (capsule vending machines). I wanted a small ritual to bring that feeling back every day, so I built Gachari: a webapp where you draw a random cultural capsule every 12h (word, sound, haiku, anecdote). I made it solo in nights and weekends over the last month. It’s simple, but I like the vibe. Would love some feedback.

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Actual performance

3points
1comments
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, tiny · Missing: agents, macos, agent
77%77% 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.
TrustMRRLess likely to generate early MRR · Strong signals: month, 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
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
33%33% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
33%33% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
22%22% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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