Context Engineering: The Art and Science of Prompt Architecture

Deep dive into context engineering techniques, from basic prompt design to advanced multi-modal context architectures that power next-generation AI systems

Tiny CLM: Teaching a 1.7B Model to Manage Its Own Memory for $5

Context Language Models says a model can learn to edit its own memory, and that a compute-cost reward teaches it to edit cheaply. I tested both on a 1.7B model, one GPU and $4.65. The first claim held. The second taught me something the paper didn't.

Context Language Models, Dissected: Letting the Model Edit Its Own Context, the Math Behind It, and Where I Disagree

A section-by-section review of Context Language Models (Shao et al., 2026): the formalism, prefix-reuse FLOPs, stepwise GRPO with a success-gated efficiency advantage, and Suffix Cache Reuse, with refreshers along the way.

Prefill, Decode, and the Quiet Third Job: Reviewing vLLM's Guide to Disaggregated Serving

Why long prompts make every stream stutter, when splitting prefill from decode pays off, and why tokenization belongs off the GPU, with a first-principles simulator that reproduces the guide's benchmark.