{R}R 開発ノート

合計 62 件の記事が見つかりました。

Frictionless SaaS, Chapter 22: AI, Automation, and the Future of Frictionless Design

In the AI era, features are commoditized overnight. So what actually becomes defensible? A teaser for Chapter 22 of Frictionless SaaS, covering the AI-Era SaaS Framework and the Experience Moat — the only lasting competitive advantage left.
2026-04-12

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 13: SaaS Metrics, Cohort Analysis, and the North Star

Chapter 13 preview of Frictionless SaaS: the SaaS Metrics Pyramid, Net Revenue Retention, cohort-based optimization, and how to choose a North Star that actually drives retention and revenue.
2026-04-03

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

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

Chapter 20: Passwordless Authentication — Passkeys, WebAuthn, and the End of the Password

Chapter 20 of the OpenID: Modern Identity series — passwordless authentication: passkeys as friendly public-key credentials, WebAuthn as the underlying browser API, and the FIDO2 ecosystem including hardware security keys.
2026-03-26

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

Chapter 19: Observability and Operations — Identity You Can Actually Run

Chapter 19 of the OpenID: Modern Identity series — observability and operations for identity systems: structured authentication logging with correlation IDs, distributed tracing of login flows, and immutable audit trails aligned to regulatory requirements.
2026-03-25

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 18: Claims Design and Privacy — Identity Data Without Overshare

Chapter 18 of the OpenID: Modern Identity series — designing custom claims with namespacing and size discipline, attribute mapping across providers into a single internal schema, and privacy by design through minimization, selective disclosure, pairwise identifiers, and GDPR-ready retention.
2026-03-24

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 16: Zero Trust Identity — When Every Request Is an Authentication Decision

Chapter 16 of the OpenID: Modern Identity series — Zero Trust identity: identity as the perimeter instead of the network, continuous verification via CAEP, and device trust and conditional access feeding into authorization decisions.
2026-03-22

OpenClaw Engineering, Chapter 7: The Skill Ecosystem

Bundled skills vs workspace skills, skill discovery and context, publishing to ClawHub, managing 13,000+ community skills without collision, semantic search, and the meta-skills that let agents improve themselves.
2026-03-22

OpenClaw Engineering, Chapter 6: Extending Capabilities with SKILL.md

The anatomy of SKILL.md files in OpenClaw: how to author reusable, versioned instruction sets with YAML frontmatter, dependencies, and explicit procedural guidance for agents.
2026-03-21

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

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 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 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 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 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 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

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 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

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

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

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

3.2 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

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

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