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The code essentially gives you a way to rotate a Transformer's internal coordinate system into a canonical basis that aligns with its own weight matrices in a lossless way. By absorbing the normalization gains directly into the adjacent weights and using orthogonal matrices built from the singular vectors of the model, you can transform architectures like Qwen or Pythia without altering their outputs or perplexity scores. Applying this transform reveals the actual hidden geometric structures o...

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The goal was to bring down the cost at the context eng. level. We do it with Layout Memoization. Instead of dumping HTML into the context window, we have built a continual learning browser harness (read only for now). We have built an early prototype for you to try out, where you can: 1. Spins up a browser instance 2. Extract any structured or tabular data from anywhere on the open-web 3. And you can do all this at the cost of a vector search Would love to hear your thoughts on this. Thanks for ...

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