Forum where posts slowly disappear unless interacted with

Hacker News

I'm always nervous to post online (this is my very first HN post), so I built an anonymous forum where posts are automatically deleted after 24 hours. Every upvote or comment resets the clock. Some things I like about this concept: - The sheer volume of information on the internet is overwhelming - disappearing.chat keeps that volume low - Unpopular content slowly fades away, so you can visually watch bad takes disappear - Content is always fresh because even popular things will eventually stop getting interaction - Takes the pressure off that your content is going to stick around forever fwiw this just started as a toy project to play around with the Next.js app directory, Tailwind and deploying to Vercel, but I figured I'd get it fully functional and share it.

Visit product View on Hacker NewsFeb 6, 2024

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Did not reach leaderboard

Launch Intel predictions

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

77%77% 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.

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

71%71% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

57%57% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: visual · Missing: mac, agents, macos

36%36% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

29%29% 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

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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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