HN
HN Pulse – stay updated on Hacker News from your menu bar
Hacker NewsHN Pulse – stay updated on Hacker News from your menu bar
HN Pulse is a minimal desktop app that sits in your menu bar and notifies you when Hacker News stories cross a custom threshold or match your keywords. No RSS, no browser tab — just quiet, relevant updates. Built with Electron and JavaScript. Open source (MIT): https://github.com/acapilleri/hn-pulse Download (macOS Universal build): https://github.com/acapilleri/hn-pulse/releases/download/v1.... Feedback and ideas welcome!
Share cardActual performance
3points
1comments
Did not reach leaderboard
Launch Intel predictions
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting67%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: mac, macos, new · Missing: agents, agent, cursor56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: open source, hacker news, ide · Missing: https docs, excited, just released47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive34%34% 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, personal31%31% 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, revenue13%13% 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, crypto2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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