Spec Coding by Capacity
Product HuntVibe Coding with a planning assistant to build with clarity
Share cardActual performance
310upvotes
40comments
Made the leaderboard
Traction signals
Makers1
Launch Intel predictions
Analyze your own launch →Product HuntOn track for Day 1 leaderboard · Strong signals: coding · Missing: mac, agents, macos68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
BetaListSuited for BetaList early-adopters · Missing: web3, chat, crypto55%55% predicted probability of success on BetaList, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive39%39% 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, personal26%26% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting25%25% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released23%23% predicted probability of success on Hacker News, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Correct prediction on native model
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