BOOLR, a digital logic simulator

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I am a Dutch high school student and last year I had to do a large project of my own choice. Together with two other guys I decided to do a project about digital logic. We had an ambitious plan: building an 8-bits computer. At that time I started learning programming and I already knew some HTML and CSS. So we decided to not build a physical computer, but one running in a self-built simulator. I was going to build the simulator, the other guys took care of the computer. I was hard, our school hadn't taught us anything about computers and programming so we had to learn everything ourselves. Our mentor told us our project was impossible to do, but we didn't listen and we worked hard. After a year we did it, I have built the simulator, called BOOLR, and the other guys have built the 8-bits computer, called Tineke. Our mentor was really impressed, we got a 10/10 grade. I've created BOOLR with JavaScript and Electron, the UI with HTML & CSS. ------------- About & Download website: http://boolr.me , feel free to download it and play around with it, it has a tutorial mode to show how everything works. On Github: https://github.com/ggbrw/boolr , I'm new to programming so don't blame me for the ugly and messy code. Next year I am going to study computer science so I hope to learn more about organizing and optimizing my code, but I am really proud of the result.

Visit product View on Hacker NewsMay 14, 2017

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

85%85% 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.

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

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

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: computer, new, physical · Missing: mac, agents, macos

49%49% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

43%43% 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

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.

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