Bo

Booklet – Async forums as an alternative to chat

Hacker News

Booklet – Async forums as an alternative to chat

I built Booklet to solve the problem of too many chat messages at work. Booklet updates classic internet forums and email groups to have a modern UI and high polish. It organizes communications into threads, and summarizes activities into a neat email newsletter - so members can stay updated without having to stay logged in. The async format promotes deeper discussions, while also increasing engagement by making conversations easy to follow. My goal is to make communications more asynchronous - so that I can get back to work, instead of slacking all day. Most early communities have been hobby groups, but my goal is to mature Booklet into a tool that sits alongside Slack in companies. Try it out, and let me know what you think!

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Actual performance

183points
119comments
Made the leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: slack, new, email · Missing: mac, agents, macos
73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
57%57% 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 · Strong signals: communications · Missing: plus, platform, intuitive
56%56% 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
39%39% 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
22%22% 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.

Correct prediction on native model

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