GumshoeOS – AI Generated Mystery Game OS

Hacker News

Hi Everyone, I wanted to play a mystery game where i have all the evidence/reports in physical form and find out who is the suspect. Having documents in physical form is hard to get so I created a digital game which generates a new mystery, evidence, police report and interviews from people, based on these things i have to find the suspect. I created this in the form of OS so that you can see the docs in the folder and have the feel that you are investigating something real :D Currently, the stories and images are generated by the AI which is a little expensive but I just want to share it anyway. If you like it you can donate so that I can keep it alive haha. (I just ask the email to login into the system, I don't do anything else with it. If you dont want to provide your email you can give a fake one) URL: https://gumshoeos.site

Visit product View on Hacker NewsOct 22, 2025

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

67%67% 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, email, physical · Missing: mac, agents, macos

59%59% 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

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

41%41% 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 · Missing: https docs, excited, just released

28%28% 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 · Missing: arr, mrr, revenue

17%17% 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.

Correct prediction on native model

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