{R}R 開発ノート

合計 36 件の記事が見つかりました。

Frictionless SaaS, Chapter 24: Anti-Patterns and Failure Modes

The last chapter of Frictionless SaaS is about the mistakes teams keep making, even when they know better. A teaser covering the Anti-Pattern Registry, the Feature Trap, and the additional failure modes that quietly erode good products.
2026-04-14

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

Frictionless SaaS, Chapter 8: Designing for Habit - Why Retention Is Your Real Growth Engine

Chapter 8 of the Frictionless SaaS blog series. Retention is the multiplier on every dollar of acquisition you'll ever spend. The Habit Loop Engine, the Return Reason Architecture, and the DAU/WAU signals that tell you whether you're building a habit or a one-night stand.
2026-03-29

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 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

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

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 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 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 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 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, 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

Art of Coding, Chapter 19: Why I Still Code

The final chapter. A personal reflection on why the act of writing code remains meaningful—and why craftsmanship endures even as everything else changes.
2026-01-17

Art of Coding, Part IV: Patterns, Anti-Patterns, and Architecture

Part IV explores design patterns as language, anti-patterns as warning signs, and architecture as the invisible skeleton enabling system growth.
2026-01-03

Art of Coding, Chapter 8: Performance without Sacrificing Clarity

Chasing speed too early blinds you to real bottlenecks. Clarity first, measurement second, optimization third—that's the order.
2026-01-02

Art of Coding, Chapter 7: Error Handling and Resilience

Designing for failure, not avoiding it. How graceful error handling, clear logging, and balanced defense build systems that endure.
2026-01-01

Art of Coding, Part III: Practices That Shape Good Code

From principles to practice. How daily habits, small decisions, and repeated choices shape code that actually endures.
2025-12-30

Art of Coding, Chapter 1: Code That Speaks

Chapter 1 of the Art of Coding series. Why beauty in code is not decoration but survival — clarity, empathy, efficiency, and what separates code that works from code that lasts. Plus: what AI-generated code means for craftsmanship.
2025-12-24

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

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

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

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

1.3 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

Monitoring, Logging, and Telemetry｜Mastering Microsoft Teams Bots 5.3

Learn how to monitor and support your Microsoft Teams bot in production using logging, Azure Application Insights, and alerts. This section shows how to track user events, diagnose failures, and create telemetry that makes your bot reliable and supportable.
2025-04-17

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