Category: systems
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Compression Is All You Need: measuring mathematical progress
TLDR: A mathematical abstraction is valuable when it compresses downstream work: proofs become shorter, repeated patterns disappear, and the library becomes easier to extend.
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Heuristic Learning: maintaining a learning system in code
TLDR: Heuristic Learning treats iterative agent work as maintaining a verifiable software system. Feedback updates code, tests, rules, state representations, and memory rather than neural network weights.
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CS336: Lecture 1 - Language Modeling as Engineering
TLDR: Modern LM work is easiest to understand by building the stack yourself, because tokenization, data, compute, and evaluation are all leaky engineering choices.
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CS336: Lecture 2 - PyTorch and resource accounting
Lecture 2 is about making training cost concrete: tensors, dtypes, memory, FLOPs, autograd, optimizers, data loading, checkpoints, and mixed precision all have resource prices.
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AMP: automatic mixed precision as a dispatch policy
TLDR: AMP is not "turn the model into half precision." It is a runtime policy that runs safe, high-throughput ops in lower precision while protecting numerically sensitive paths.
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Claude Code Source: an agent as an operating-system process
Reading Claude Code through an operating-system lens makes the agent runtime concrete: context preparation, tools, permissions, subprocesses, cancellation, compaction, plugins, and exit paths.