{R}R 開発ノート

合計 51 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 13: VibeOps and CI/CD Evolution

Chapter 13 of The Engineering of Intent blog series. Static CI/CD was built for human-paced commits. AI-native velocity needs dynamic, context-aware, agent-literate pipelines. A teaser on VibeOps, context preservation across deployments, merge queues at velocity, and the ten-minute pipeline contract.
2026-04-29

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

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

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 14: Experience Observability and Friction Detection

Chapter 14 preview of Frictionless SaaS: experience observability, synthetic and real-user monitoring, and the friction detection engine that surfaces retention issues before they become churn.
2026-04-04

Chapter 21: Decentralized Identity — DIDs, Verifiable Credentials, and OID4VC

Chapter 21 of the OpenID: Modern Identity series — decentralized identity: DIDs (Decentralized Identifiers) without a central authority, Verifiable Credentials with selective disclosure, and OpenID for Verifiable Credentials (OID4VC) as the bridge from centralized to decentralized identity.
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 15: FAPI and High-Stakes Security — When the Defaults Aren't Enough

Chapter 15 of the OpenID: Modern Identity series — FAPI for high-stakes scenarios: what FAPI 1.0 Advanced and 2.0 require, Pushed Authorization Requests (PAR), JWT-Secured Authorization Requests (JAR), and migrating from FAPI 1.0 Advanced to FAPI 2.0.
2026-03-21

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 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 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

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 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 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 10: Single Sign-On at Scale — Identity as Infrastructure

Chapter 10 of the OpenID: Modern Identity series — running identity at organizational scale: corporate IdPs (AD, Entra ID), the CIAM vendor ecosystem (Okta, Auth0, Entra ID), multi-tenant isolation, account linking, and external user access.
2026-03-16

Chapter 9: SPA and Mobile Patterns — Auth in Hostile Environments

Chapter 9 of the OpenID: Modern Identity series — SPAs and mobile apps in hostile environments: XSS and CSRF defense, PKCE in the browser, the Backend-for-Frontend pattern, native app patterns, and refresh token rotation with reuse detection.
2026-03-15

Chapter 8: Securing Backend APIs — Bearer Tokens, Scopes, and Service-to-Service

Chapter 8 of the OpenID: Modern Identity series — securing backend APIs with bearer tokens, scope design for least privilege, token introspection versus local JWT validation, and the three mechanisms for service-to-service authentication.
2026-03-14

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 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 1: Why Identity Is Hard — The Trust Problem Behind Every Login

Chapter 1 of the OpenID: Modern Identity book series — why identity is a trust problem first and a technology problem second, and why authentication and authorization must never be conflated.
2026-03-07

OpenID: Modern Identity for Developers and Architects — A 22-Part Blog Series

Introduction and index for the 22-part blog series based on OpenID: Modern Identity for Developers and Architects by Sho Shimoda — with links to every chapter from Why Identity Is Hard through Identity in AI Systems.
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 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 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

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 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

7.2 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

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

6.2 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

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

5.4 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

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

5.2 Numerical Pitfalls

A deep, accessible explanation of the numerical pitfalls in LU decomposition. Learn about growth factors, tiny pivots, rounding errors, catastrophic cancellation, ill-conditioning, and why LU may silently produce incorrect results without proper pivoting and numerical care.
2025-09-24

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

Chapter 5 — LU Decomposition

An in-depth, accessible introduction to LU decomposition—why it matters, how it improves on Gaussian elimination, where pivoting fits in, and what modern numerical libraries like NumPy and LAPACK do under the hood. Includes a guide to stability, practical applications, and a smooth transition into LU with and without pivoting.
2025-09-22

4.3 Pivoting Strategies

A practical and intuitive guide to pivoting strategies in numerical linear algebra, explaining partial, complete, and scaled pivoting and why pivoting is essential for stable Gaussian elimination and reliable LU decomposition.
2025-09-20

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

3.1 Norms and Why They Matter

A deep yet accessible exploration of vector and matrix norms, why they matter in numerical computation, and how they influence stability, conditioning, error growth, and algorithm design. Essential reading for AI, ML, and scientific computing engineers.
2025-09-13

Chapter 3 — Computation & Mathematical Systems

A clear, insightful introduction to numerical computation—covering norms, error measurement, conditioning vs stability, and the gap between mathematical algorithms and real implementations. Essential reading for anyone building AI, optimization, or scientific computing systems.
2025-09-12

2.4 Vector and Matrix Storage in Memory

A clear, practical guide to how vectors and matrices are stored in computer memory. Learn row-major vs column-major layout, strides, contiguity, tiling, cache behavior, and why memory layout affects both speed and numerical stability in real systems.
2025-09-11

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

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

1.2 Floating-Point Reality vs. Textbook Math

Floating-point numbers don’t behave like real numbers. This article explains how rounding, cancellation, and machine precision break AI systems—and why it matters.
2025-09-04

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

Numerical Linear Algebra: Understanding Matrices and Vectors Through Computation

Learn how linear algebra actually works inside real computers. A practical guide to LU, QR, SVD, stability, conditioning, and the numerical foundations behind modern AI and machine learning.
2025-09-01

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