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Lead.dev – The Startup Competition for Devs
Hacker NewsLead.dev – The Startup Competition for Devs
hey hn, I made & launched lead.dev - a free, gamified way of launching your startup. Get feedback, find new users & compete on the daily, weekly, monthly & all time MRR leaderboards. Example profile: lead.dev/lewis Stack: - nextjs 15 (w/ partial pre rendering + ssr) - shadcn UI components - v0/cursor - trigger.dev - authjs - prisms - vercel
Share cardActual performance
15points
7comments
Made the leaderboard
Launch Intel predictions
Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: cursor, user, new · Missing: mac, agents, macos89%89% 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.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive39%39% 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, podcasting38%38% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released37%37% 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: month, monthly, users · Missing: mobile apps, ios, personal33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: mrr · Missing: arr, revenue, profit32%32% 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, crypto17%17% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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