{R}R 開発ノート

合計 71 件の記事が見つかりました。

The Engineering of Intent, Chapter 12: The GenDD Pod

Chapter 12 of The Engineering of Intent blog series. Three people, continuous flow, almost no ceremony — the team structure that actually ships AI-native work sustainably. A teaser on the Agentic Product Lead, Engineer, and QA roles, the transition from Agile, and the case study where a SaaS company went from 72 engineers to 24.
2026-04-28

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 8: The Four Pillars of AI Architecture

Chapter 8 of The Engineering of Intent blog series. Every durable AI-native project has the same four pillars — Vibes, Specs, Skills, and Agents — and most teams over-invest in one and neglect the rest. A teaser on the pillars, the healthy cycle, and the rebalancing that cut a company's regression rate by 80%.
2026-04-24

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 21: Operations and Scalability Without Friction

Why growing SaaS companies hit a wall that is not a product problem or a sales problem — it is an operations problem. A teaser for Chapter 21 of Frictionless SaaS covering the Event-Driven Operations Architecture and the Scalability Without Headcount Principle.
2026-04-11

Frictionless SaaS, Chapter 20: Organizational Design for Frictionless Delivery

The Experience Ownership Model and the Behavior Design Canvas — two reorganizations that stop user experience from falling through the cracks between departments.
2026-04-10

Frictionless SaaS, Chapter 18: Building Knowledge Into Your Product

The Zero-Support Design Model, Contextual Help Architecture, and four AI Assistant Design Patterns that turn your product into its own best documentation.
2026-04-08

Frictionless SaaS Chapter 16: The Power of Self-Service

Chapter 16 preview of Frictionless SaaS: the Self-Serve Maturity Model, the Independence Principle, and how self-serve billing and account management turn scalability into a competitive moat.
2026-04-06

Frictionless SaaS, Chapter 8: Designing for Habit - Why Retention Is Your Real Growth Engine

Chapter 8 of the Frictionless SaaS blog series. Retention is the multiplier on every dollar of acquisition you'll ever spend. The Habit Loop Engine, the Return Reason Architecture, and the DAU/WAU signals that tell you whether you're building a habit or a one-night stand.
2026-03-29

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

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

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

OpenClaw Engineering, Chapter 8: Event-Driven Workflows

How OpenClaw agents spring into action automatically via hooks, webhooks, and TypeScript handlers—without waiting for human invocation. From internal events to CI/CD pipelines.
2026-03-23

Frictionless SaaS, Chapter 2: The SAFE Journey — A Map of Where Your Users Actually Quit

Chapter 2 of the Frictionless SaaS blog series. The SAFE Journey Framework breaks the user lifecycle into Signup, Activation, Frequency, and Expansion — each with different friction, different metrics, and different fixes. Plus: why Time to Value is the most important retention metric in early-stage SaaS.
2026-03-23

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

Chapter 18 – Sub-Agents and Multi-Agent Collaboration

Chapter 18 of Master Claude Chat, Cowork and Code explores multi-agent architecture — how to decompose complex problems into specialized sub-agents, coordinate parallel execution, and synthesize results into coherent outputs.
2026-03-18

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 6: Discovery and Metadata — How Clients and Providers Find Each Other

Chapter 6 of the OpenID: Modern Identity series — how OIDC discovery, .well-known/openid-configuration, JWKS, and Dynamic Client Registration allow clients and providers to find each other without hand-crafted configuration.
2026-03-12

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 8: Scheduled Tasks and Autonomous Execution — Making Claude Work While You Sleep

Chapter 8 of Master Claude Chat, Cowork and Code covers scheduled automation with Claude Cowork — cron-based recurring workflows, sleep/connectivity handling, error strategies, and applying GTD principles to AI task automation.
2026-03-09

Chapter 7: Plugins and Domain Specialization — Turning Claude Into Your Organization's Expert

Chapter 7 of Master Claude Chat, Cowork and Code explores how plugins transform Claude from a generalist into a domain expert — with pre-built plugins for Sales, Finance, Marketing, and Legal, slash commands, and organization-managed customization.
2026-03-08

Chapter 2: From OpenID to OpenID Connect — How the Industry Got This One Right

Chapter 2 of the OpenID: Modern Identity series — tracing how the industry moved from the original OpenID and SAML through OAuth 2.0 to OpenID Connect, and when to reach for each standard.
2026-03-08

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

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, Chapter 17: AI, Automation, and the Role of the Engineer

How AI changes engineering roles. Why automation removes drudgery but makes human judgment more valuable, and what "curation" means for the future programmer.
2026-01-15

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 10: Anti-Patterns to Avoid

Anti-patterns are the structural traps that silently erode codebases. Learning to recognize them early is one of the most valuable skills a developer can have.
2026-01-05

Art of Coding, Chapter 9: Design Patterns as a Language of Developers

Design patterns compress complex architectural ideas into shared language. But they're tools for solving problems, not decorations for code.
2026-01-04

Art of Coding, Part IV: Patterns, Anti-Patterns, and Architecture

Part IV explores design patterns as language, anti-patterns as warning signs, and architecture as the invisible skeleton enabling system growth.
2026-01-03

Art of Coding, Chapter 7: Error Handling and Resilience

Designing for failure, not avoiding it. How graceful error handling, clear logging, and balanced defense build systems that endure.
2026-01-01

8.4 PCA and Spectral Methods

An intuitive, in-depth explanation of PCA, spectral clustering, and eigenvector-based data analysis. Covers covariance matrices, graph Laplacians, and why eigenvalues reveal hidden structure in data. Concludes Chapter 8 and leads naturally into SVD in Chapter 9.
2025-10-10

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

8.1 Power Method and Inverse Iteration

A clear, practical, and intuitive explanation of the power method and inverse iteration for computing eigenvalues. Covers dominance, repeated multiplication, shifted inverse iteration, and real applications in ML, PCA, and large-scale systems. Smoothly introduces the Rayleigh quotient.
2025-10-07

Chapter 8 — Eigenvalues and Eigenvectors

A deep, intuitive introduction to eigenvalues and eigenvectors for engineers and practitioners. Explains why spectral methods matter, where they appear in real systems, and how modern numerical algorithms compute eigenvalues efficiently. Leads naturally into the power method and inverse iteration.
2025-10-06

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

7.3 Least Squares Problems

A clear, intuitive, book-length explanation of least squares problems, including the geometry, normal equations, QR decomposition, and SVD. Learn why least-squares solutions are central to ML and data science, and why QR provides a stable foundation for practical algorithms.
2025-10-04

7.2 Householder Reflections

A clear, intuitive, book-length explanation of Householder reflections and why they form the foundation of modern QR decomposition. Learn how reflections overcome the numerical instability of Gram–Schmidt and enable stable least-squares solutions across ML, statistics, and scientific computing.
2025-10-03

7.1 Gram–Schmidt and Modified GS

A clear, practical, book-length explanation of Gram–Schmidt and Modified Gram–Schmidt, why classical GS fails in floating-point arithmetic, how MGS improves stability, and why real numerical systems eventually rely on Householder reflections. Ideal for ML engineers, data scientists, and numerical computing practitioners.
2025-10-02

Chapter 7 — QR Decomposition

A deep, intuitive introduction to QR decomposition, explaining why orthogonality and numerical stability make QR essential for least squares, regression, kernel methods, and large-scale computation. Covers Gram–Schmidt, Modified GS, Householder reflections, and why QR is often preferred over LU and normal equations.
2025-10-01

6.3 Applications in ML, Statistics, and Kernel Methods

A deep, intuitive explanation of how Cholesky decomposition powers real machine learning and statistical systems—from Gaussian processes and Bayesian inference to kernel methods, Kalman filters, covariance modeling, and quadratic optimization. Understand why Cholesky is essential for stability, speed, and large-scale computation.
2025-09-30

6.2 Memory Advantages

A detailed, intuitive explanation of why Cholesky decomposition uses half the memory of LU decomposition, how memory locality accelerates computation, and why this efficiency makes Cholesky essential for large-scale machine learning, kernel methods, and statistical modeling.
2025-09-29

6.1 SPD Matrices and Why They Matter

A deep, intuitive explanation of symmetric positive definite (SPD) matrices and why they are essential in machine learning, statistics, optimization, and numerical computation. Covers geometry, stability, covariance, kernels, Hessians, and how SPD structure enables efficient Cholesky decomposition.
2025-09-28

5.4 Practical Examples

Hands-on LU decomposition examples using NumPy and LAPACK. Learn how pivoting, numerical stability, singular matrices, and performance optimization work in real systems, with clear Python code and practical insights.
2025-09-26

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