The author reflects on the resurgence of low-level optimization in AI development, specifically for Large Language Models (LLMs). They recall their early career optimizing matrix multiplications on 8-bit machines and later, in 2008, building a neural network for the British Library that relied on the same fundamental operations: matrix multiplication and activation functions. This core concept, they note, remains central to modern LLMs, with knowledge encoded in weight matrices, analogous to synapses. The author also shares an anecdote about developing shader programs in HLSL for GPUs, a skill that proved unexpectedly useful years later when they began building their own inference engine. This engine, initially written in HLSL and later rewritten in WGSL with the assistance of an AI agent, allows for direct kernel-level optimization within a browser. AI
IMPACT Highlights the ongoing importance of low-level optimization and GPU programming for efficient LLM inference.
RANK_REASON The item is a personal reflection on the evolution of programming and AI development, rather than a new release or significant industry event.
- British Library
- C#
- Claude
- DirectX
- Hugging Face
- Intel
- LINQPad
- MNIST database
- PoopScanner
- Raspberry Pi
- V-Tune
- WebGPU Shading Language
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