Multi-part explorations of deep topics
Introductory posts on the Bernoulli model: noisy Booleans, Bloom filters as the one-sided special case, and computing with known false-positive and false-negative rates. The research hub and the monograph hold the full treatment.
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A from-scratch tour of why each neural architecture works, built as a pure-Python library you can read. Eight bite-sized posts, and a full monograph.
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AI alignment, moral agency, superintelligence, and the futures we might build
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Building a NumPy quantum simulator from a single qubit to Shor's algorithm, density matrices, noise, and error correction. Nothing asserted that the code cannot show.
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A four-part interactive series exploring how representation choices — tabular, linear, neural — shape what reinforcement learning agents can learn, and what they silently bet on.
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From Solomonoff induction to modern language models: sequential prediction through information theory, Bayesian methods, and neural approaches
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Posts influenced by Structure and Interpretation of Computer Programs, on abstraction, composition, and computation as a medium for expressing ideas
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Maximum likelihood estimation for series systems with masked failure data, from master's thesis to R package ecosystem
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A pedagogical series exploring generic programming through the lens of abstract algebra
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Essays on induction, inference, and the search for useful representations
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Digital preservation tools and philosophy. Graceful degradation, plain-text archival, and building systems that outlast their creators.
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A paradigm for computing with values whose meaning is hidden behind a one-way trapdoor. Cipher maps, approximate homomorphisms, and a framework for evaluating functions on opaque encodings.
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A pedagogical series exploring information theory by construction in C++23
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