I built Unlearning Comparator, a visual analytics toolkit to help researchers and developers compare how different machine unlearning methods work. It provides a unified workflow to test for accuracy, efficiency, and privacy. You can check out the live demo linked in the post, and the source code is on GitHub: https://github.com/gnueaj/Machine-Unlearning-Comparator Our accompanying paper is currently under review at IEEE T
Article URL: https://github.com/golioth/tinymcp
Comments URL: https://news.ycombinator.com/item?id=44491460
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Article URL: https://markushimmel.de/blog/my-first-verified-imperative-program/
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https://markushimmel.de/blog/my-first-verified-imperative-program/
Article URL: https://sus-lang.org/
Comments URL: https://news.ycombinator.com/item?id=44491860
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Article URL: https://spectrum.ieee.org/ai-intersection-monitoring
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Article URL: https://thetumultuousunicornofdarkness.github.io/CPU-X/
Comments URL: https://news.ycombinator.com/item?id=44490386
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Article URL: https://github.com/trknhr/ai-docs
Comments URL: https://news.ycombinator.com/item?id=44490399
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Hello HN!
As a long term NYC resident, I have read countless articles on ideas tweaking subway services, but always found them hard to follow without visual aid. So over the long weekend I decided to build one. It has all the basic features: trains would spawn at their origin, stop at stations, and slow down if it gets too close to another. You can also design custom routes by piecing tracks together.
Have fun, and let me know what you think!