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Weekly newsletter with tactical frameworks from 50 $1M+ founders

Hacker News

Weekly newsletter with tactical frameworks from 50 $1M+ founders

Hi HN, I've spent the last 6 months interviewing founders who scaled to $1M+ ARR to understand what actually worked vs. what just sounds good in retrospect. The surprising finding: Almost none of them had a grand vision at the start. They just solved one specific problem exceptionally well, then expanded. Some patterns I found: - They talked to 10-20 customers per week in early days (not surveys, real conversations) - They picked narrow niches first ("X for dentists" beats "X for everyone") - They charged money immediately, even with rough MVPs - They averaged 18 months before their first hire - They picked "boring ideas in exciting markets" over trendy tech I'm documenting these insights in a weekly newsletter for founders: https://doanything.com/preview/uXalImXcFZk No growth hacks or motivational fluff - just tactical frameworks from real builders who've done it. Would love to hear what patterns you've noticed in successful founders, or what's worked in your own journey.

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Actual performance

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Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
69%69% 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.
TrustMRRFits verified-revenue profile · Strong signals: month · Missing: mobile apps, ios, personal
52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
46%46% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos
38%38% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr, growth · Missing: mrr, revenue, profit
28%28% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: builder · Missing: plus, platform, intuitive
26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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