Mike Lewis

A strong systems page because his work repeatedly shows up where inference efficiency meets usable long context, especially in attention sinks, StreamingLLM, post-training quantization, and later long-context head designs.

A high-signal researcher for the systems side of modern AI, especially where reinforcement learning, memory-efficient large-model training, and long-context inference meet.

A strong researcher to follow for efficient and long-context LLM systems, especially where inference tricks and memory management make large models practical to run.

One of the clearest researchers to follow for efficient AI systems, especially the line of work that makes large models smaller, faster, and easier to deploy without giving up too much quality.

A strong person to follow if you care about open-weight language models and retrieval-heavy NLP systems, especially the line from RoBERTa and RAG into LLaMA-era model development.

A stronger page than the old stub because his work cuts across two important threads in modern language models: early retrieval-augmented generation systems like Atlas and the later LLaMA open-weight model line.