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There is a well-known paper related to a statistical zero-knowledge proof about Kolmogorov complexity, but this proof introduced is considered a perfect ZKP

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I think I heard that 77 was factored as well

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Maybe truth here, but also Microsoft didn't lead their latest round, which isn't a great sign for their moat

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Contract law research

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Methodology for comparison: train zstd dictionary on enwik9. Then build my dictionary as most common words in enwik9. Mine does 13% better because of the way I discovered how you can generate dictionary replacement symbols

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This is a direct comparison to zstd with a dictionary actually

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Syracuse airport. You're telling me just because there's 2 feet of snow on the runway that you can't fly?

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Getting higher z-scores now. But you could always just try running the code yourself

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I used the z-score. How can you claim that the digits of pi are random, yet a random forest classifier predicted better than the distribution probability. Your claim implicitly means "there is no structure." The hard thing to understand is that the classifier didn't see the test set, so what structure did it learn? At the very least this is an interesting question

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Better to use statistical significance tests to talk about what is "far more likely"

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It doesn't predict better than even, it predicts better than the distribution probability

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This doesn't _break_ sha256, just opens the door to breaking sha256 with machine learning

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Old people not listening to young people

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Would greatly appreciate any peer review for this work

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My dad didn't use it for a long time but has used it now and then recently and has found it to be very impressive

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Thanks this is a good point, I'll change proof to evidence

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I'm not predicting the number I'm predicting number%2==0. The model predicted better than the distribution probability

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Thanks for teaching me some important statistics!

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Also, I am testing different ranges of digits other than first 10,000, but the problem with other ranges is that the distribution of digits is highly imbalanced and the model is not showing statistical significance, but models have a harder time when the distribution of classes is not 50/50, so I think its not quite fair to evaluate the model on these ranges.

So why do you think the first 10,000 digits are somewhat predictable?

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The issue is not with getting the digits, the issue is with running a large model for larger digit ranges. I tried running with 10,000,000 digits and haven't gotten a prediction yet.

Submissions

Tell HN: Perfect – not statistical – ZKP for Kolmogorov complexity

Compressor "firn" improves ZSTD by 13%

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The digits of pi are not random

Show HN: New compression algorithm beats ZSTD by 14%

Show HN: KeyPush: Mix of Bing/ChatGPT

Full Self-Driving Struggles

Show HN: Git Searcher, search commits for an added or removed string

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