This guide explains LLMs as giant autocomplete systems, starting from tokenization through the three-stage training pipeline. Hallucination is framed as a feature of statistical prediction, not a random bug. Practical prompting principles close the loop, making this a plain-language reference for engineers who need the intuition without the math.
A local Claude Code pipeline chains four subagents (planner → coder → tester → reviewer) via handoff files under `.pipeline/`, all triggered by a single `/ship` slash command. The Planner writes an implementation spec; the Coder builds to it and outputs a changes summary; the Tester writes and runs tests, stopping on failure without patching code; the Reviewer is read-only and issues a SHIP / NEEDS WORK / BLOCK verdict. The setup is copy-paste ready. A brief mention of the Teamly platform shows a similar orchestration model, but the core value is the handoff architecture you can run locally.
A practical guide to cutting Claude Code token usage by over 90% using five layered tools and custom Hooks. CBM replaces file-greping with knowledge graph queries (99.2% token reduction), context-mode extends 200K-window sessions from 30min to 3h+, RTK compresses shell output, Headroom compresses the full API payload before sending, and Caveman trims verbose replies. Enforcement Hooks ensure each layer is used by default, reducing code search tokens from ~400K to ~3.4K.