{R}R Dev Notes

Found total of 16 articles.

The Engineering of Intent, Chapter 39: Impressionistic Scanning — A Visual Heuristic Guide

Chapter 39 of The Engineering of Intent blog series. Shape matters before content — especially for AI-generated code, where the agent is often blind to global shape. A teaser cheat sheet of six visual code shapes (wide-flat, deep-nesting, high-import-churn, long-thin, jagged, suspicious-uniformity) and what each one means.

2026-05-25

The Engineering of Intent, Chapter 24: The Failure Mode Catalog

Chapter 24 of The Engineering of Intent blog series. Fifteen named failure modes I keep seeing in Vibe Coding practice, with remedies. A teaser on phantom confidence, silent scope creep, context amnesia, loop obsession, the yes-person agent, and the deprecation blind spot.

2026-05-10

The Engineering of Intent, Chapter 15: The Future of the Human Engineer

Chapter 15 of The Engineering of Intent blog series. Am I going to be replaced? The honest answer, after five years of watching the discipline evolve. A teaser on intent architecture, staying relevant, the economic reshaping of the senior-to-junior ratio, and craft in a craftless era.

2026-05-01

The Engineering of Intent, Chapter 13: VibeOps and CI/CD Evolution

Chapter 13 of The Engineering of Intent blog series. Static CI/CD was built for human-paced commits. AI-native velocity needs dynamic, context-aware, agent-literate pipelines. A teaser on VibeOps, context preservation across deployments, merge queues at velocity, and the ten-minute pipeline contract.

2026-04-29

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

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, Chapter 14: Code Reviews and Pair Programming

Code reviews as mentorship and collaboration. How to write for reviewers, offer critique with respect, and build a team culture grounded in feedback.

2026-01-10

Art of Coding, Chapter 9: Design Patterns as a Language of Developers

Design patterns compress complex architectural ideas into shared language. But they're tools for solving problems, not decorations for code.

2026-01-04

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 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, Chapter 6: Abstraction and Modularity

Drawing boundaries that make systems stronger. How to abstract without over-engineering, and design interfaces that last.

2025-12-31

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

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

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

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

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