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FirstVersion – Rebuilding the humble beginnings of great products

Hacker News

FirstVersion – Rebuilding the humble beginnings of great products

FirstVersion is a journey through time, showcasing the humble beginnings of today’s tech giants by recreating their very first versions. These rebuilds remind us that every major company started with a simple Version 1.0, focused on solving a specific problem. As Paul Graham noted in 2008, success follows patterns, though they're not always obvious. While complex solutions might seem impressive, history shows us that the most impactful ideas often start with simplicity. The rebuilds are generated with the assistance of AI and collaboratively refined and maintained by the community, with ongoing updates provided for free.

Visit product View on Hacker NewsDec 30, 2024

Actual performance

4points

Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting

70%70% 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 HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

34%34% predicted probability of success on Product Hunt, 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

32%32% 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: way · Missing: mobile apps, ios, personal

32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

17%17% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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