{R}R 開発ノート

合計 25 件の記事が見つかりました。

The Engineering of Intent, Chapter 14: The 30-Day Pilot Framework

Chapter 14 of The Engineering of Intent blog series. Every successful AI-native transformation starts as a thirty-day pilot on a single well-scoped project. A teaser on how to scope the first project, the week-by-week playbook, the five-question graduation rubric, and the three pilots that show what works and what doesn't.
2026-04-30

The Engineering of Intent, Chapter 11: The Art of Agentic Debugging

Chapter 11 of The Engineering of Intent blog series. Debugging in the AI-native regime is archaeology — the code may have been written by an agent you supervised loosely. A teaser on the self-correction loop, control-flow visualization, bisection under velocity, and the caching heisenbug that took hours manually but fifteen minutes with the agent.
2026-04-27

The Engineering of Intent, Chapter 10: The Five-Layer Quality Gate Stack

Chapter 10 of The Engineering of Intent blog series. Every AI-generated change must pass five layers of automated gates before a human sees it. A teaser on linting, strict types, SAST, test synthesis, and agentic E2E — plus the anti-patterns that quietly invalidate the stack.
2026-04-26

The Engineering of Intent, Chapter 9: Advanced Context Engineering

Chapter 9 of The Engineering of Intent blog series. Context engineering is the highest-leverage activity in AI-native development. A teaser on the Context Pack, the Layered Prompt, the A/B test that proved more context isn't better context, and the three anti-patterns that quietly kill agent quality.
2026-04-25

The Engineering of Intent, Chapter 7: The GenDD Execution Loop

Chapter 7 of The Engineering of Intent blog series. Generative-Driven Development replaces your ceremony set with a fractal five-step loop: Context, Plan, Confirm, Execute, Validate. A teaser on each step, what goes wrong when it's skipped, and the payments team that cut one-hour cycles down to ten minutes.
2026-04-23

The Engineering of Intent, Chapter 6: Autonomous Orchestration Frameworks

Chapter 6 of The Engineering of Intent blog series. Editors run one agent at a time; orchestration runs many. A teaser on task-specific personalities, memory banks, when to orchestrate (and when not), the 14,000-test case study, and the economics of multi-agent pipelines.
2026-04-22

The Engineering of Intent, Chapter 5: Agentic Editors and Flow States

Chapter 5 of The Engineering of Intent blog series. The editor is where the wiring meets your hands. A teaser on the three generations of editor, how semantic search amplifies your codebase's virtues and vices, the flow killers that destroy productivity, and the shortcut rebind that doubled a team lead's output.
2026-04-21

The Engineering of Intent, Chapter 2: Cognitive Load and Material Disengagement

Chapter 2 of The Engineering of Intent blog series. When the agent does most of the typing, the real failure mode is the engineer who has stopped engaging. A teaser on material disengagement, impressionistic scanning, the autocomplete trap, decision fatigue, and the seven habits of engaged engineers.
2026-04-18

The Engineering of Intent, Chapter 1: The Triadic Relationship Model

Chapter 1 of The Engineering of Intent blog series. Software used to be a dyad between engineer and machine. Now a third actor — the AI agent — has joined permanently. A teaser covering the Triadic Relationship Model, the CMDP view of software, and the six failure modes every AI-native team needs to name.
2026-04-17

Frictionless SaaS, Chapter 23: Pattern Libraries and Proven Approaches

Frameworks are nice. Patterns are what you actually ship. A teaser for Chapter 23 of Frictionless SaaS, introducing the Fast Activation Pattern Library, the Frictionless Onboarding Catalog, and a set of high-performing product patterns borrowed from the SaaS companies that get activation right.
2026-04-13

OpenClaw Engineering, Chapter 11: Continuous Learning with OpenClaw-RL

How OpenClaw-RL extracts training signals from conversations and uses them to improve agent behavior continuously. From binary feedback to token-level distillation, agents learn from every interaction without retraining the base model.
2026-03-26

OpenClaw Engineering, Chapter 10: Multi-Agent Systems

Build teams of specialized agents that work in concert. Learn how to architect planners, coders, critics, and surveyors, coordinate them via channels, and use adversarial collaboration and taste gates for high-quality output.
2026-03-25

OpenClaw Engineering, Chapter 9: Scheduling and Deterministic Orchestration

Time-based automation for agents: cron jobs for simple periodic tasks and the Lobster workflow engine for complex, deterministic, resumable multi-step pipelines with human approval gates.
2026-03-24

Chapter 19 – Measuring AI Effectiveness

Chapter 19 of Master Claude Chat, Cowork and Code tackles the question every team eventually asks: is our AI actually working? Learn to build metrics frameworks, structured evaluations, and workflow acceleration measurements that prove (or disprove) AI's value.
2026-03-19

Chapter 17 – Guardrails and Governance

Chapter 17 of Master Claude Chat, Cowork and Code moves from understanding risks to implementing controls — permission isolation, tool allow-lists, human-in-the-loop approval workflows, validation hooks, and enterprise-grade audit logging.
2026-03-18

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 11: CI/CD Integration and Automation — Claude Code in Your Pipeline

Chapter 11 of Master Claude Chat, Cowork and Code shows how to deploy Claude Code into CI/CD pipelines — GitHub Actions, GitLab CI, automated PR reviews, security audits, documentation sync, cost management, and production safety patterns.
2026-03-12

Chapter 10: Safe Legacy Code Refactoring — Horror Stories and the Discipline That Prevents Them

Chapter 10 of Master Claude Chat, Cowork and Code tackles the hardest problem in AI-assisted development — refactoring legacy code without introducing subtle bugs. Covers characterization tests, incremental verification, PR review, and catching hallucinations.
2026-03-11

Chapter 6: What Is Claude Cowork? — The Desktop Agent That Touches Your Files

Chapter 6 of Master Claude Chat, Cowork and Code introduces Claude Cowork — a sandboxed desktop agent that automates file management, data extraction, and cross-application workflows on your local machine.
2026-03-07

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

Art of Coding, Chapter 14: Code Reviews and Pair Programming

Code reviews as mentorship and collaboration. How to write for reviewers, offer critique with respect, and build a team culture grounded in feedback.
2026-01-10

7.4 Why QR Is Often Preferred

An in-depth, accessible explanation of why QR decomposition is the preferred method for solving least squares problems and ensuring numerical stability. Covers orthogonality, rank deficiency, Householder reflections, and the broader role of QR in scientific computing, with a smooth transition into eigenvalues and eigenvectors.
2025-10-05

1.4 A Brief Tour of Real-World Failures

A clear, accessible tour of real-world numerical failures in AI, ML, optimization, and simulation—showing how mathematically correct algorithms break inside real computers, and preparing the reader for Chapter 2 on floating-point reality.
2025-09-06

1.1 What Breaks Real AI Systems

Many AI failures come from numerical instability, not algorithms. This guide explains what actually breaks AI systems and why numerical linear algebra matters.
2025-09-03

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