Code as Agent Harness reframes the relationship between application code and AI agents. Instead of writing prompts, engineers design harnesses — structured code layers that define tools, guardrails, and orchestration logic for autonomous systems.
How modern AI serving systems split a single model across many accelerators. Covers tensor, pipeline, and expert parallelism, KV-cache sharding, and production patterns from vLLM, TensorRT-LLM, and DeepSeek.
How to design, build, and operate production systems on the Claude API — covering architecture, streaming, tool use, prompt caching, evals, and cost control.
Most RAG failures start long before the LLM is ever called. This post walks through the five hardest ingestion problems and the patterns teams use to fix them in production.
Agentic AI systems drift in capability long before they fail outright. This post breaks down the mechanisms, the production signals, and the engineering practices that keep agents sharp.