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MNIST64 – 99.5% accuracy MNIST BNN on a stock Commodore 64

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MNIST64 – 99.5% accuracy MNIST BNN on a stock Commodore 64

I built an 8-bit binarized convolutional neural network that recognizes handwritten digits on C64 with state-of-the-art accuracy. I also designed a cascade model to get interactive response, and implemented everything with self-modifying cycle optimized 6502 assembly - resulting 0.653s average inference time. Link to the paper, the full 6502 source code, and a video demo. https://jarnoh.github.io/mnist64/paper.pdf https://github.com/jarnoh/mnist64 https://youtu.be/Z_S0IZenW_E I'm interested to hear your feedback and hoping for someone to beat these numbers!

Visit productView on Hacker NewsSep 19, 2026
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3points
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Did not reach leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
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nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
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Product HuntUnlikely to reach the leaderboard · Strong signals: model, code · Missing: mac, agents, macos
50%50% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal
47%47% 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
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
13%13% 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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