A teardown electronics quiz app made by me and a friend Ive never met

Hacker News

I found a partly broken electronic doorbell on the ground and realized I never would have guessed that this was a doorbell. So I posted in my sideproject community (relentlessly.no) an idea to make a quiz app where we break open many gadgets, take pictures and let people guess what gadgets they are! Ive been working on and off for about three months on this with a fullstack and a designer that i met in our sideproject community <3 We know its pretty basic but would love to see if anyone finds it interesting. there are like five free tasks before a 3 usd season 1 Whats crazy is two things: Ive never met the designer. And we didnt have ONE single meeting digitally or physically while making this. all asynchronous work through slack. Man I LOVE that. And now i own 33% of a basic app. Time of my life!

Visit product View on Hacker NewsJul 29, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: slack, single, physical · Missing: mac, agents, macos

84%84% 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

79%79% 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 · Missing: https docs, excited, just released

58%58% 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: month · Missing: mobile apps, ios, personal

40%40% 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

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

18%18% predicted probability of success on AppSumo, 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.

Incorrect prediction on native model

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