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Mental Models for Startup Founders

Hacker News

Mental Models for Startup Founders

Hello HN, I launched Wingify/VWO (Visual Website Optimizer) here on HN in 2010. The initial momentum and feedback I got from this place was a key reason I was able to profitably bootstrap the company to roughly ~$30MN ARR. Over the last 2 years, I have been writing a book for startup founders that's informed by my experience with Wingify and many failed attempts before it. It's finally done, so thought of launching it on the same forum where it all started for me :) There are a total of 68 mental models covering various aspects of building a startup: - Choosing markets - Building products - Ecosystems and partners - Thinking about Moats - Approaching marketing - B2B v/s B2C - Hiring & culture Unlike other books, I'm not sharing my story and neither take a very prescriptive approach. Rather, I use mental models to shine light and provide a tractable way of looking at problems an entrepreneur encounters during her startup. I understand that entrepreneurship cannot be systemized, but I'm hoping some of the mental models I share help in clearer thinking and faster decisions. Would love your feedback on the book: https://invertedpassion.com/free-book-mental-models-for-star... If you find it useful, please share it with others in your network.

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
84%84% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, visual · Missing: mac, agents, macos
67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
62%62% 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 · 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 · Strong signals: entrepreneurs, way · Missing: mobile apps, ios, personal
30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr, profit · Missing: mrr, revenue, saas
29%29% 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, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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