{R}R Dev Notes

Found total of 13 articles.

The Engineering of Intent, Chapter 37: Context Scaling — Just-In-Time Retrieval for Million-Line Codebases

Chapter 37 of The Engineering of Intent blog series. Hand-authored Context Packs don't scale past a million lines. A teaser on Just-In-Time Context, retrieval via MCP, the three governors that prevent runaway retrieval, and a concrete pipeline from a 3.8M-line codebase.

2026-05-23

The Engineering of Intent, Chapter 4: The Model Context Protocol (MCP)

Chapter 4 of The Engineering of Intent blog series. MCP is to agents what HTTP was to the early Web — a common protocol that turns bespoke integrations into reusable infrastructure. A teaser on host/client/server roles, the anatomy of a good tool, the six anti-patterns, and the security pitfalls every team trips over.

2026-04-20

The Engineering of Intent, Chapter 3: Context Momentum and Path Dependence

Chapter 3 of The Engineering of Intent blog series. Agents amplify project momentum — good patterns propagate, bad ones propagate just as fast. A teaser on the First Prompt Trap, context rot, the physics of convention drift, and the ten-thousand-dollar rule for decision rigor.

2026-04-19

Chapter 22: Identity in AI Systems — When the "User" Is an Agent

Chapter 22 of the OpenID: Modern Identity series — identity for AI systems: LLM authentication, the Model Context Protocol (MCP), Dynamic Client Registration for ephemeral agents, and the emerging patterns for trusting autonomous non-human actors.

2026-03-28

Chapter 20 – The Next Decade of AI Coworkers

Chapter 20 of Master Claude Chat, Cowork and Code looks ahead — from conversational AI to embedded infrastructure, from chat interfaces to computer use, and the trust and responsibility questions that will define how AI reshapes work over the next decade.

2026-03-20

Chapter 15 – Managing Context Rot and Entropy

Chapter 15 of Master Claude Chat, Cowork and Code tackles the silent failure mode of long-running AI sessions — context rot. Learn strategies for context compression, structured state management, and thinking like an operations team to keep Claude sharp over time.

2026-03-16

Chapter 14 – Connecting Systems with the Model Context Protocol (MCP)

Chapter 14 of Master Claude Chat, Cowork and Code explores the Model Context Protocol — the universal bridge that lets Claude connect to Slack, GitHub, Jira, Google Drive, and more, turning isolated AI into a deeply integrated workflow partner.

2026-03-15

Chapter 13: Encapsulating Knowledge with Agent Skills — From Conversations to Autonomous Procedures

Chapter 13 of Master Claude Chat, Cowork and Code introduces Skills — reusable, encapsulated procedures that Claude executes autonomously. Covers SKILL.md structure, YAML frontmatter, trigger descriptions, and the Skills Library pattern for team distribution.

2026-03-14

Master Claude, Chapter 4: Context Persistence with Claude Projects — Solving the AI Amnesia Problem

Chapter 4 of Master Claude Chat, Cowork and Code explains how Claude Projects solve the AI amnesia problem with persistent context — custom instructions, knowledge bases, and shared team workspaces that remember your architecture, conventions, and patterns across every conversation.

2026-03-05

Master Claude, Chapter 3: Understanding Entropy and Prompting Fundamentals — Why Your Prompts Fail and How to Fix Them

Chapter 3 of Master Claude Chat, Cowork and Code explains why some prompts work and others fail — through the lens of entropy and probability. Covers XML-structured prompting, chain-of-thought reasoning, multishot examples, and a standard prompt template you can use immediately.

2026-03-04

Master Claude, Chapter 2: The Three Pillars of Claude — Chat, Cowork, and Code

Claude is not one product — it is three. Chat for reasoning, Cowork for desktop automation, Code for terminal-based development. Chapter 2 of Master Claude Chat, Cowork and Code explains the architecture of each and the decision framework for choosing the right one.

2026-03-03

Master Claude, Chapter 1: The Evolution of Large Language Models — From Markov Chains to Context Engineering

Chapter 1 of Master Claude Chat, Cowork and Code traces the journey from statistical text prediction to reasoning engines — and explains why context engineering, not bigger models, is where the next leap in AI productivity comes from.

2026-03-02

Master Claude Chat, Cowork and Code – The Complete Blog Series

The complete index for the Master Claude Chat, Cowork and Code blog series — 20 chapter teasers covering everything from prompting fundamentals to multi-agent architectures, security governance, and the future of AI-powered work.

2026-03-01

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