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Get inspired by this curated gallery, made with marimo: the open-source reactive Python notebook.
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Interactive false-positive traps showing how feature attribution, probing, SAEs, and circuits 'find' structure in pure noise.
Why diffusion models don't need noise conditioning, derived in closed form on a toy circles dataset.
by Akash Sharma
A paper explainer for 'Diverse Task Experts Are Dense Around Pretrained Weights' with interactive loss landscapes and a RandOpt implementation.
How language models represent statistics like mean and variance on curved activation manifolds, with steering experiments extended to geographic data.
