{R}R 開発ノート

合計 13 件の記事が見つかりました。

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

The Engineering of Intent, Chapter 1: The Triadic Relationship Model

Chapter 1 of The Engineering of Intent blog series. Software used to be a dyad between engineer and machine. Now a third actor — the AI agent — has joined permanently. A teaser covering the Triadic Relationship Model, the CMDP view of software, and the six failure modes every AI-native team needs to name.
2026-04-17

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

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 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 14: Hardening Your Identity Stack — Setting the Defaults That Keep You Safe

Chapter 14 of the OpenID: Modern Identity series — hardening defaults that neutralize common attacks: strict redirect URI matching, audience validation to solve the confused deputy problem, token lifetime tuning, and refresh token binding, rotation, and revocation.
2026-03-20

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 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

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

3.3 Conditioning of Problems vs Stability of Algorithms

Learn the critical difference between problem conditioning and algorithmic stability in numerical computing. Understand why some systems fail even with correct code, and how sensitivity, condition numbers, and numerical stability determine the reliability of AI, ML, and scientific algorithms.
2025-09-15

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