What popular startup sites looked like when they launched

Hacker News

Hi HN, I’m super excited to share my weekend project here and I really hope you find it inspirational and useful. I've spent most of Bank Holiday weekend here in the UK (Memorial day weekend for those in the US) going through Wayback machine and Web archive to create a timeline of how popular startup websites have evolved over time since their launch. As the (now) famous quote from Reid Hoffman goes: "If you are not embarrassed by the first version of your product, you have launched too late." Hope this helps inspire more founders to just get out their and start building. I'm going to continue to add more startups weekly to the database. Please feel free to share any suggestions or sites that should be included. P.s You can even submit your own site to be featured :) This is the first version of the project and I’m aiming to add new features in the coming weeks (maybe an e-book?), and also regularly update the database so any kind of feedback will be greatly appreciated! Built with no-code using Webflow. You can check out the site here: https://famousfirstwebsites.com

Visit product View on Hacker NewsJun 1, 2021

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Actual performance

6points

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Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, new, using · Missing: agents, macos, agent

70%70% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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.

Hacker NewsStrong engagement from HN community · Strong signals: excited, io · Missing: https docs, just released, exist

65%65% 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

38%38% 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

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

18%18% 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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