{R}R 開発ノート

合計 65 件の記事が見つかりました。

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 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

Frictionless SaaS Chapter 9: Eliminating Friction and Building Consistency

Chapter 9 preview of Frictionless SaaS: the Friction Audit Matrix, the Consistency Principle, perceived speed, and information ergonomics - the retention levers most teams ignore.
2026-03-30

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 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 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 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: The Complete Series Index — Your Guide to All 24 Chapters

The complete reader's guide to the Frictionless SaaS blog series. An introduction to the thesis — that in the AI era, features are commoditized and experience is the only lasting competitive advantage — plus direct links to all 25 posts across the 24 chapters of the book.
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

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 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 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 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 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

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 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 5: Tokens in Depth — What's Actually in That JWT

Chapter 5 of the OpenID: Modern Identity series — what's really inside an ID Token, Access Token, and Refresh Token, how JWTs are structured, how to validate signatures correctly, and how DPoP and mTLS bind tokens to their legitimate holders.
2026-03-11

Chapter 3: Core Concepts — The Vocabulary of OpenID Connect

Chapter 3 of the OpenID: Modern Identity series — the IdP/RP/user triangle, claims and JWTs, the three OIDC token types, consent and scopes, sessions vs tokens, and the boundary between authentication and authorization.
2026-03-09

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

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 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

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

8.2 Rayleigh Quotient

An intuitive and comprehensive explanation of the Rayleigh quotient, why it estimates eigenvalues so accurately, how it connects to the power method and inverse iteration, and why it forms the foundation of modern eigenvalue algorithms. Ends with a natural transition to the QR algorithm.
2025-10-08

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

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

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

4.4 When Elimination Fails

An in-depth, practical explanation of why Gaussian elimination fails in real numerical systems—covering zero pivots, instability, ill-conditioning, catastrophic cancellation, and singular matrices—and how these failures motivate the move to LU decomposition.
2025-09-21

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

4.2 Row Operations and Elementary Matrices

A deep but intuitive explanation of row operations and elementary matrices, showing how Gaussian elimination is built from structured matrix transformations and how these transformations form the foundation of LU decomposition and numerical stability.
2025-09-19

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

4.0 Solving Ax = b

A deep, accessible introduction to solving linear systems in numerical computing. Learn why Ax = b sits at the center of AI, ML, optimization, and simulation, and explore Gaussian elimination, pivoting, row operations, and failure modes through intuitive explanations.
2025-09-17

3.4 Exact Algorithms vs Implemented Algorithms

Learn why textbook algorithms differ from the versions that actually run on computers. This chapter explains rounding, floating-point errors, instability, algorithmic reformulation, and why mathematically equivalent methods behave differently in AI, ML, and scientific computing.
2025-09-16

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

3.2 Measuring Errors

A clear and intuitive guide to absolute error, relative error, backward error, and how numerical errors propagate in real systems. Essential for understanding stability, trustworthiness, and reliability in scientific computing, AI, and machine learning.
2025-09-14

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

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