{R}R 開発ノート

合計 83 件の記事が見つかりました。

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 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 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

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 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 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

Art of Coding, Chapter 13: Testing as a Design Discipline

Testing is a design discipline. How well-written tests reveal awkward APIs, improve code clarity, and become the most reliable documentation of system behavior.
2026-01-09

Art of Coding, Part I: Why Code is an Art

Introducing the Art of Coding blog series: a 26-week exploration of what makes code beautiful, maintainable, and enduring in the age of AI.
2025-12-23

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

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

6.1 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

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

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

3.2 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

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

2.1 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

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

1.4 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

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

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

カテゴリー

タグ

検索ログ

Development & Technical Consulting

Working on a new product or exploring a technical idea? We help teams with system design, architecture reviews, requirements definition, proof-of-concept development, and full implementation. Whether you need a quick technical assessment or end-to-end support, feel free to reach out.

Contact Us