Modern coding agents default to grep-and-read when they touch a new repo. That choice is expensive, slow, and produces brittle edits. A new wave of zero-dependency C++ tools is replacing that loop with a ranked, deterministic call graph — and the economics of AI-assisted coding shift with them.
A working engineer’s end-to-end guide to assembling a transformer language model in PyTorch: from BPE tokenization to multi-head attention, rotary embeddings, and a real training loop on a single GPU.
A working engineer’s guide to building an LLM agent from zero: the planning loop, tool integration, memory, guardrails, and the production patterns that turn a demo into a service.
How to design, build, and ship Model Context Protocol servers that turn LLMs into reliable, tool-using agents — with patterns drawn from Claude Desktop, Cursor, and real production deployments.
Most agent demos die in production. Here’s a practitioner’s playbook for designing LLM agents that are observable, testable, and actually ship.