{R}R 開発ノート

合計 45 件の記事が見つかりました。

The Engineering of Intent, Chapter 16: The Morning Routine

Chapter 16 of The Engineering of Intent blog series. The first thirty minutes of your workday set the upper bound on how much you will accomplish. A teaser on the five-step morning routine — reload, sync Specs, review memory bank, warm up, launch — and the anti-routine that destroys more productive days than any other pattern.
2026-05-02

The Engineering of Intent, Chapter 7: The GenDD Execution Loop

Chapter 7 of The Engineering of Intent blog series. Generative-Driven Development replaces your ceremony set with a fractal five-step loop: Context, Plan, Confirm, Execute, Validate. A teaser on each step, what goes wrong when it's skipped, and the payments team that cut one-hour cycles down to ten minutes.
2026-04-23

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

The Engineering of Intent, Chapter 5: Agentic Editors and Flow States

Chapter 5 of The Engineering of Intent blog series. The editor is where the wiring meets your hands. A teaser on the three generations of editor, how semantic search amplifies your codebase's virtues and vices, the flow killers that destroy productivity, and the shortcut rebind that doubled a team lead's output.
2026-04-21

The Engineering of Intent, Chapter 3: Context Momentum and Path Dependence

Chapter 3 of The Engineering of Intent blog series. Agents amplify project momentum — good patterns propagate, bad ones propagate just as fast. A teaser on the First Prompt Trap, context rot, the physics of convention drift, and the ten-thousand-dollar rule for decision rigor.
2026-04-19

The Engineering of Intent, Chapter 2: Cognitive Load and Material Disengagement

Chapter 2 of The Engineering of Intent blog series. When the agent does most of the typing, the real failure mode is the engineer who has stopped engaging. A teaser on material disengagement, impressionistic scanning, the autocomplete trap, decision fatigue, and the seven habits of engaged engineers.
2026-04-18

Frictionless SaaS, Chapter 19: Self-Serve Monetization and Growth

The Self-Serve Growth Engine, the Expansion Revenue Framework, and the Seamless Handoff Principle — how to turn upgrades into a natural moment instead of a sales call.
2026-04-09

Frictionless SaaS Chapter 10: Data Lock-In and Network Lock-In

Chapter 10 preview of Frictionless SaaS: the Data Gravity Effect, the Network Lock-In Model, and how to build structural moats that make churn expensive without being manipulative.
2026-03-31

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

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 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 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

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 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 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 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 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

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

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

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

4.0 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

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

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

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

1.3 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

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

Use Case: Sales Assistant Bot｜Mastering Microsoft Teams Bots 6.3

Learn how to build a Sales Assistant Bot for Microsoft Teams. From surfacing leads to logging calls and syncing with CRMs, this section shows how bots can empower sales teams to move faster, close deals, and automate follow-ups — all within Teams.
2025-04-20

Conversation Flow and Dialogs｜Mastering Microsoft Teams Bots 3.3

Learn how to build intelligent conversation flows in Microsoft Teams bots using dialogs. This section explains how to guide users through multi-turn interactions, manage state, use prompts and waterfalls, and decide when to use dialogs versus Task Modules.
2025-04-10

Rich Responses with Adaptive Cards｜Mastering Microsoft Teams Bots 3.2

Learn how to create rich, interactive messages in Microsoft Teams using Adaptive Cards. This section explains how to design, send, and handle cards in your bot — making your bot feel less like a chat and more like a true app experience inside Teams.
2025-04-09

Bot Authentication and Identity｜Mastering Microsoft Teams Bots 2.3

Learn how Microsoft Teams bots authenticate users and access secure data. This section covers SSO, OAuth 2.0, and the Microsoft Graph API, giving your bot the ability to understand identity and act on behalf of users—safely and seamlessly.
2025-04-07

Overview of Microsoft Teams Bot Capabilities｜Mastering Microsoft Teams Bots 1.3

Explore the full range of capabilities bots can offer in Microsoft Teams. This section breaks down interactive contexts, features like Adaptive Cards, proactive messaging, user authentication, Graph API integration, and what limitations still exist. Get a developer’s guide to what’s possible.
2025-04-04

Overview of Microsoft Teams Architecture｜Mastering Microsoft Teams Bots 1.2

Get a developer-friendly introduction to how Microsoft Teams is built. This section explains Teams architecture—channels, tabs, bots, messaging extensions, and Graph API—and shows how each component fits into the broader platform. A must-read before building your first bot.
2025-04-03

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