The author frames tokenizer design as an integer linear program, relaxes it to a continuous LP, and uses cutting planes to close the gap between fractional and integral solutions. They automate cut discovery with Codex, apply cycle constraints on overlapping token edges, and report provably optimal tokenizers on small pretokenized datasets.

This article breaks down how an LLM turns your prompt into streamed tokens, covering tokenization, embeddings, transformer attention, and the two-phase pipeline of compute-bound prefill and memory-bound decode. It explains KV caching, quantization, and metrics like Time to First Token and Inter-Token Latency to show why inference speed depends on both compute and memory.

A16z outlines 17 key developments expected in the crypto landscape by 2026, focusing on innovations in stablecoins, tokenization of real-world assets, and the transformation of financial systems through blockchain technology. The article emphasizes the role of stablecoins in modernizing payment infrastructures and the potential for personalized wealth management accessible to a broader audience.