{R}R 開発ノート

合計 46 件の記事が見つかりました。

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

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 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 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

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

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

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

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

OpenClaw Engineering, Chapter 2: Anatomy of the Agent Brain

How OpenClaw agents think through their identity files, two-layer memory system, and proactive task scheduling. A deep dive into SOUL.md, AGENTS.md, USER.md, MEMORY.md, HEARTBEAT.md, and semantic memory via Supermemory.
2026-03-17

OpenClaw Engineering, Chapter 1: The OpenClaw Paradigm

The first chapter teaser in a new series on OpenClaw Engineering. Why autonomous agents need a different foundation, the four-layer architecture (Gateway, Nodes, Channels, Skills), and the three principles that hold it all together.
2026-03-16

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 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 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 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

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

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 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

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

7.3 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

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

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

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

2.1 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

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

1.3 Computation & Mathematical Systems

A clear explanation of how mathematical systems behave differently inside real computers. Learn why stability, conditioning, precision limits, and computational constraints matter for AI, ML, and numerical software.
2025-09-05

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

1.0 Why Numerical Linear Algebra Matters

A deep, practical introduction to why numerical linear algebra matters in real AI, ML, and optimization systems. Learn how stability, conditioning, and floating-point behavior impact models.
2025-09-02

Message Handling｜Mastering Microsoft Teams Bots 3.1

Learn how to build responsive and intelligent Microsoft Teams bots by handling messages effectively. This section covers activity types, keyword detection, mentions, markdown formatting, conversation context, and tips for scaling from simple replies to powerful, workflow-driven bots.
2025-04-08

Why Build Bots for Microsoft Teams?｜Mastering Microsoft Teams Bots 1.1

Discover why Microsoft Teams bots are transforming the workplace. This section explores the real-world impact of building bots in Teams, from automating tasks and integrating external services to enabling context-aware digital assistants. Learn how bots can save time, boost productivity, and bring automation into the flow of daily work.
2025-04-02

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