vibedraft
TrustMRRMost tools bulk-generate X posts or bulk-schedule them. None do both. VibeDraft connects them: tell Claude your topics in one chat, get two weeks of voice-matched posts auto-queued into peak engagemen
Most tools bulk-generate X posts or bulk-schedule them. None do both. VibeDraft connects them: tell Claude your topics in one chat, get two weeks of voice-matched posts auto-queued into peak engagement slots. Built for 9-5 indie hackers who need to set up the week in 30 seconds.
Share cardActual performance
Did not reach leaderboard
Traction signals
Domain Rating3
Launch Intel predictions
Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: claude · Missing: mac, agents, macos54%54% 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.
Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue26%26% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive24%24% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting14%14% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency10%10% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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