XD

XDeck โ€“ An ๐• client app for macOS as a TweetDeck alternative

Hacker News

XDeck โ€“ An ๐• client app for macOS as a TweetDeck alternative

Hi everyone, This is my first post on Hacker News. I'm disappointed that TweetDeck is no longer available, so I built a simplified TweetDeck-like application. It's very simple, and you can't add/remove/move columns like you can in TweetDeck. That's something I'll add in the future, but it's still useful enough for me. I hope you find it useful too.

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch โ†’
Product HuntOn track for Day 1 leaderboard ยท Strong signals: mac, macos, new ยท Missing: agents, agent, cursor
57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersIH features products with proven revenue ยท Missing: supports, reddit linkedin, podcasting
50%50% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience ยท Strong signals: hacker news, io ยท Missing: https docs, excited, just released
47%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 ยท Missing: plus, platform, intuitive
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR ยท Missing: mobile apps, ios, personal
34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience ยท Missing: arr, mrr, revenue
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers ยท Missing: web3, chat, crypto
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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