{R}R Dev Notes

Found total of 24 articles.

The Forward Deployed Engineer, Chapter 9: The Outer Loop — Scaling Field Intelligence

Chapter 9 of The Forward Deployed Engineer blog series. The outer loop begins the moment a deployment goes into production. A teaser on gravel roads and paved superhighways, the productization decision, the platform commit, measuring leverage, and the two-team handshake.
2026-06-04

The Engineering of Intent, Chapter 40: The De-Vibing Protocol — Stabilization Sprints for Production

Chapter 40 of The Engineering of Intent blog series. The final chapter and the remedy for the autocomplete trap — a two-week, agent-heavy stabilization sprint that moves a fast vibes-only build from 90/10 to 50/50 without halting feature development. A teaser on recognizing when you need it, the four tracks, and the three post-sprint disciplines.
2026-05-26

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 38: Multi-Agent Conflict Resolution — Protocols for Agentic Tie-Breaking

Chapter 38 of The Engineering of Intent blog series. When multiple specialized agents block the same PR with incompatible demands, the answer is a protocol for Agentic Tie-Breaking. A teaser on triaging stacked concerns vs. real conflicts, three resolution protocols, Architect Agent design, and the four governance failure modes.
2026-05-24

The Engineering of Intent, Chapter 37: Context Scaling — Just-In-Time Retrieval for Million-Line Codebases

Chapter 37 of The Engineering of Intent blog series. Hand-authored Context Packs don't scale past a million lines. A teaser on Just-In-Time Context, retrieval via MCP, the three governors that prevent runaway retrieval, and a concrete pipeline from a 3.8M-line codebase.
2026-05-23

The Engineering of Intent, Chapter 36: The Long View

Chapter 36 of The Engineering of Intent blog series. The long view. What happens to our craft, our profession, and our lives over the next ten years? A teaser on cycles and waves, three things that will endure, three that will change, and a final word on identity.
2026-05-22

The Engineering of Intent, Chapter 14: The 30-Day Pilot Framework

Chapter 14 of The Engineering of Intent blog series. Every successful AI-native transformation starts as a thirty-day pilot on a single well-scoped project. A teaser on how to scope the first project, the week-by-week playbook, the five-question graduation rubric, and the three pilots that show what works and what doesn't.
2026-04-30

Frictionless SaaS Chapter 13: SaaS Metrics, Cohort Analysis, and the North Star

Chapter 13 preview of Frictionless SaaS: the SaaS Metrics Pyramid, Net Revenue Retention, cohort-based optimization, and how to choose a North Star that actually drives retention and revenue.
2026-04-03

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 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

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 12: Version Control as a Storytelling Tool

Git is not just a backup system—it's a narrative tool. How clean commits and thoughtful branching strategies turn version control into a form of storytelling.
2026-01-08

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, 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

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

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

4.2 Row Operations and Elementary Matrices

A deep but intuitive explanation of row operations and elementary matrices, showing how Gaussian elimination is built from structured matrix transformations and how these transformations form the foundation of LU decomposition and numerical stability.
2025-09-19

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

3.4 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

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

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

Deploying to Azure｜Mastering Microsoft Teams Bots 5.1

Learn how to deploy your Microsoft Teams bot to Azure for production use. This section walks through setting up an Azure App Service, configuring environment variables, connecting to Bot Channels Registration, and testing your bot in the cloud.
2025-04-15

Message Handling｜Mastering Microsoft Teams Bots 3.1

Learn how to build responsive and intelligent Microsoft Teams bots by handling messages effectively. This section covers activity types, keyword detection, mentions, markdown formatting, conversation context, and tips for scaling from simple replies to powerful, workflow-driven bots.
2025-04-08

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