I'
I've built a bot that pins links to the bottom of Slack channels
Hacker NewsI've built a bot that pins links to the bottom of Slack channels
I'm launching this bot today. It allows you to easily configure self-service links on a channel-by-channel basis, saving time and avoiding unnecessary chat. Bookmarks for Slack. The bot works by monitoring Slack and periodically reposting pinned links if there have been messages in the meantime - ensuring that important information is always accessible. Old messages are cleaned up to maintain a pristine channel history. Gif here: https://imgur.com/gallery/hoTwSH3 You can get it here: https://preactive.chat
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Did not reach leaderboard
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting71%71% 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 NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released49%49% 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: way · Missing: mobile apps, ios, personal47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: slack · Missing: mac, agents, macos44%44% 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, intuitive35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue14%14% 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, cryptocurrency3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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