Interactive scavenger hunts based on audio

Hacker News

This is my first ever submission to HN. Hope to receive some constructive criticism. Plan to submit more. ZapHunt is an interactive audio scavenger hunt, where in each audio episode leads to another after finding an item in your home. No sign up needed. This idea came from my journey as a parent — my daughter used to be overstimulated by noisy toys and screen time (not much anymore). I imagined as my kid grew older, I would want to find something to entertain without too much screen involvement. I noticed everyone liked scavenger hunts but they are like TODO lists, they get boring after a certain point of time. So, I combined it with a twist. Scavenger hunt with stories. Although, I intended for kids ages 7+, If you are a kid at heart you can try and give me feedback. I’d love feedback from the community on a few things: Did you want to play another after completing the whole story ? Did the audio interactions feel smooth or confusing? Should I pivot ZapHunt’s audio scavenger hunt concept to a new format? Are there any industries or areas where these kinds of interactive audio experiences could be useful? I usually lean towards edutainment, but I'm wondering if there are other niche areas that might benefit from this idea, especially ones with fewer restrictions/laws around it ? Any bugs, friction points, or parts that felt off?

Visit product View on Hacker NewsJul 27, 2025

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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.

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

58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

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

38%38% predicted probability of success on TrustMRR, 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

31%31% 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 · Strong signals: active · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto

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

Correct prediction on native model

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