{R}R 開発ノート

合計 60 件の記事が見つかりました。

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 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

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 10: Data Lock-In and Network Lock-In

Chapter 10 preview of Frictionless SaaS: the Data Gravity Effect, the Network Lock-In Model, and how to build structural moats that make churn expensive without being manipulative.
2026-03-31

OpenClaw Engineering, Chapter 13: Hardening the Ecosystem

The final chapter: ecosystem security, the ClawHavoc incident, defending against malware in dependencies, confirming high-risk operations, and building auditing and disaster recovery systems.
2026-03-28

OpenClaw Engineering, Chapter 12: The Agentic Zero-Trust Architecture

Zero-trust security for autonomous agents: managing blast radius, implementing three-tier defense (pre-action, in-action, post-action), container isolation, and defending against indirect prompt injection attacks.
2026-03-27

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

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

OpenClaw Engineering, Chapter 5: Connecting Multiple Channels

How to connect your OpenClaw agent to multiple messaging platforms (Telegram, WhatsApp, Discord, Slack) and manage multi-channel routing. Setup, configuration quirks, and troubleshooting for each platform.
2026-03-20

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

OpenClaw Engineering, Chapter 4: Managing the Gateway and Models

Configuring your running gateway with the onboard wizard, diagnostics, and openclaw.json. How to connect model providers, manage API keys securely, and route different queries to different models.
2026-03-19

OpenClaw Engineering, Chapter 3: Deployment and Environment Setup

From local development to production: installing Node.js 22+, setting up Docker containers, and deploying OpenClaw to the cloud via AWS Lightsail or VPS providers.
2026-03-18

Chapter 16 – Execution Risks and Isolation

Chapter 16 of Master Claude Chat, Cowork and Code confronts the real security risks of AI systems that execute commands and manipulate files — from command injection to data exposure — and explains the isolation models that keep things safe.
2026-03-17

Chapter 11: MFA and Conditional Access — Dispersing Authentication Risk

Chapter 11 of the OpenID: Modern Identity series — MFA fundamentals across the three factor categories, risk-based adaptive authentication, and step-up authentication using the OIDC acr and amr claims to match assurance to operation sensitivity.
2026-03-17

Chapter 7: Your First OpenID Application — The Handshake, End to End

Chapter 7 of the OpenID: Modern Identity series — building a real OIDC login end to end: the minimal flow, state and nonce, strict redirect URI matching, sessions from tokens, and the three flavors of logout.
2026-03-13

Chapter 12: CLAUDE.md — Designing Guardrails That Shape How Claude Thinks

Chapter 12 of Master Claude Chat, Cowork and Code explores CLAUDE.md as a living constitution for AI behavior — positive constraints over prohibitions, complete financial and startup examples, instruction decay, hierarchical files, and anti-patterns to avoid.
2026-03-13

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 9: Claude Code Fundamentals — The CLI Agent That Rewrites Your Codebase

Chapter 9 of Master Claude Chat, Cowork and Code introduces Claude Code — a CLI agent that reads, analyzes, and modifies codebases directly from the terminal. Covers architecture, multi-file refactoring, Git worktrees, and permission management.
2026-03-10

Chapter 5: Rapid Prototyping with Artifacts — From Conversation to Live Application

Chapter 5 of Master Claude Chat, Cowork and Code explores how Claude Artifacts collapse the feedback loop between idea and execution — turning conversations into live, interactive applications in seconds.
2026-03-06

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 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 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

Art of Coding, Chapter 19: Why I Still Code

The final chapter. A personal reflection on why the act of writing code remains meaningful—and why craftsmanship endures even as everything else changes.
2026-01-17

Art of Coding, Part VII: Beyond Today

Introduction to Part VII. As AI writes more code, what becomes the engineer's irreplaceable role? A look at how automation transforms—but doesn't diminish—the craft.
2026-01-14

Art of Coding, Chapter 16: Ethics and Longevity

How ethics and longevity intertwine in code. Why the systems you write today remain your responsibility for years, and how empathy shapes sustainable software.
2026-01-13

Art of Coding, Chapter 15: Code as a Team Sport

Code as a team sport: shared ownership, documentation as craft, and respecting the reader. The human practices that make software sustainable and teams thrive.
2026-01-12

Art of Coding, Part VI: The Human Side of Code

The human side of code: collaboration, culture, and the practices that make software sustainable. How teams thrive when they value people as much as process.
2026-01-11

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

Art of Coding, Chapter 13: Testing as a Design Discipline

Testing is a design discipline. How well-written tests reveal awkward APIs, improve code clarity, and become the most reliable documentation of system behavior.
2026-01-09

Art of Coding, Chapter 12: Version Control as a Storytelling Tool

Git is not just a backup system—it's a narrative tool. How clean commits and thoughtful branching strategies turn version control into a form of storytelling.
2026-01-08

Art of Coding, Part V: Tools and the Ecosystem

Tools shape the culture of how teams code. The right ecosystem amplifies clarity and craftsmanship; the wrong one creates friction and distraction.
2026-01-07

Art of Coding, Chapter 11: Architectural Thinking

Architectural thinking is the discipline of designing systems that survive real-world growth. It means asking how your code will feel to live in years from now.
2026-01-06

Art of Coding, Chapter 4: Maintainability and Scalability

How to build code that bends instead of breaks, systems that grow without collapsing, and anticipate change without over-engineering.
2025-12-28

Art of Coding, Part II: Principles of Clarity

Part II introduces clarity as the compass of software: readability, maintainability, and the consistency that makes teams move faster.
2025-12-26

Art of Coding, Chapter 2: The Philosophy of Clean Code

Clean code is a philosophy, not a rulebook. Explore simplicity vs. cleverness, expressiveness as communication, and code as a form of writing.
2025-12-25

Art of Coding, Chapter 1: Code That Speaks

Chapter 1 of the Art of Coding series. Why beauty in code is not decoration but survival — clarity, empathy, efficiency, and what separates code that works from code that lasts. Plus: what AI-generated code means for craftsmanship.
2025-12-24

Art of Coding, Part I: Why Code is an Art

Introducing the Art of Coding blog series: a 26-week exploration of what makes code beautiful, maintainable, and enduring in the age of AI.
2025-12-23

8.3 The QR Algorithm (High-Level Intuition)

A clear, intuitive, and comprehensive explanation of the QR algorithm—how repeated QR factorizations reveal eigenvalues, why orthogonal transformations provide stability, and how shifts and Hessenberg reductions make the method efficient. Ends with a smooth bridge to PCA and spectral methods.
2025-10-09

Chapter 6 — Cholesky Decomposition

A deep, narrative-driven introduction to Cholesky decomposition explaining why symmetric positive definite matrices dominate real computation. Covers structure, stability, performance, and the role of Cholesky in ML, statistics, and optimization.
2025-09-27

5.3 LU in NumPy and LAPACK

A practical, in-depth guide to how LU decomposition is implemented in NumPy and LAPACK. Learn about partial pivoting, blocked algorithms, BLAS optimization, error handling, and how modern numerical libraries achieve both speed and stability.
2025-09-25

5.1 LU with and without Pivoting

A clear and practical explanation of LU decomposition with and without pivoting. Learn why pivoting is essential, how partial and complete pivoting work, where no-pivot LU fails, and why modern numerical libraries rely on pivoted LU for stability.
2025-09-23

4.1 Gaussian Elimination Revisited

A deep, intuitive exploration of Gaussian elimination as it actually behaves inside floating-point arithmetic. Learn why the textbook algorithm fails in practice, how instability emerges, why pivoting is essential, and how elimination becomes reliable through matrix transformations.
2025-09-18

3.3 Conditioning of Problems vs Stability of Algorithms

Learn the critical difference between problem conditioning and algorithmic stability in numerical computing. Understand why some systems fail even with correct code, and how sensitivity, condition numbers, and numerical stability determine the reliability of AI, ML, and scientific algorithms.
2025-09-15

2.3 Overflow, Underflow, Loss of Significance

A clear and practical guide to overflow, underflow, and loss of significance in floating-point arithmetic. Learn how numerical computations break, why these failures occur, and how they impact AI, optimization, and scientific computing.
2025-09-10

2.2 Machine Epsilon, Rounding, ULPs

A comprehensive, intuitive guide to machine epsilon, rounding behavior, and ULPs in floating-point arithmetic. Learn how precision limits shape numerical accuracy, how rounding errors arise, and why these concepts matter for AI, ML, and scientific computing.
2025-09-09

2.1 Floating-Point Numbers (IEEE 754)

A detailed, intuitive guide to floating-point numbers and the IEEE 754 standard. Learn how computers represent real numbers, why precision is limited, and how rounding, overflow, subnormals, and special values affect numerical algorithms in AI, ML, and scientific computing.
2025-09-08

Chapter 2 — The Computational Model

An introduction to the computational model behind numerical linear algebra. Explains why mathematical algorithms fail inside real computers, how floating-point arithmetic shapes computation, and why understanding precision, rounding, overflow, and memory layout is essential for AI, ML, and scientific computing.
2025-09-07

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