Inside A High School Hackathon – Assemble, August 2022

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Hi HN, I want to share this short documentary on a 183 person hackathon that I (age 17) ran with my friends this summer. Coding changed my life. However, I've always struggled to find a space with folks my age who also find joy in coding, especially since the pandemic begun. My friends & I wanted to run an amazing in-person hackathon as soon as it was safe to do so, and Assemble was that event. To try and help other teenagers organise their own hackathons, we’ve open sourced all of our planning documents - including finances, marketing emails, website code, run-of-show, etc - at https://github.com/hackclub/assemble . Hoping that Assemble will resonate with people here, and happy to answer any questions about the organising process!

Visit product View on Hacker NewsSep 23, 2022

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

90%90% 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: email, coding, code · Missing: mac, agents, macos

65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

50%50% 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 · Strong signals: soon · Missing: plus, platform, intuitive

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

26%26% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: finances · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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