Moved my Codeabbey puzzles website to opensource

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Back in september 2013 I started my website with programming puzzles, now there are 400+, but this time it is not about boasting :) I considered making it opensource many times, but only this June, after I left yet another day job, I found some willpower to do necessary cleanup and customization. To deploy and configure it, there are provided instructions in a number of steps, also teachin what is where in the code - but to make things quick, there is also brief video-demo. With it one can launch the site at the free web-hosting, for example, in less than half an hour! I hope the thing could be useful for programming teachers - and not only programming. I tried, as a proof-of-concept, start physics-dedicated website. With small javascript tinkering it could host quizzes etc - so it is possible to turn it to even to psychology exercises (though I don't think it is worth it).

Visit product View on Hacker NewsAug 27, 2024

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

77%77% 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.

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

46%46% predicted probability of success on AppSumo, 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

44%44% 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: video · Missing: mobile apps, ios, personal

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: code, open · Missing: mac, agents, macos

35%35% predicted probability of success on Product Hunt, 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 · 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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