{R}R Dev Notes

Found total of 17 articles.

The Forward Deployed Engineer, Chapter 5: The AI and Agentic Frontier

Chapter 5 of The Forward Deployed Engineer blog series. The technical bar that the FDE shares with platform engineers — plus the AI-specific skills that separate the role in 2026. A teaser on agents beyond chatbots, RAG, multi-agent orchestration, evals as a discipline, and model-agnostic deployment.
2026-05-31

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

Azure Front Door: a practical introduction

What Azure Front Door is, who it's for, what it costs, how it compares to Cloudflare and CloudFront, and a walkthrough of the settings that matter when putting it in front of an Azure App Service.
2026-05-11

The Engineering of Intent, Chapter 21: Working With Teammates (Human and Agentic)

Chapter 21 of The Engineering of Intent blog series. Vibe Coding is only solo in the narrowest sense — every piece of code has reviewers and maintainers, some human, some agent. A teaser on the review contract, pair Vibe Coding, handing off to agents without walking off a cliff, and the explicit mentorship that still works in the AI-native era.
2026-05-07

The Engineering of Intent, Chapter 10: The Five-Layer Quality Gate Stack

Chapter 10 of The Engineering of Intent blog series. Every AI-generated change must pass five layers of automated gates before a human sees it. A teaser on linting, strict types, SAST, test synthesis, and agentic E2E — plus the anti-patterns that quietly invalidate the stack.
2026-04-26

The Engineering of Intent, Chapter 9: Advanced Context Engineering

Chapter 9 of The Engineering of Intent blog series. Context engineering is the highest-leverage activity in AI-native development. A teaser on the Context Pack, the Layered Prompt, the A/B test that proved more context isn't better context, and the three anti-patterns that quietly kill agent quality.
2026-04-25

Frictionless SaaS Chapter 14: Experience Observability and Friction Detection

Chapter 14 preview of Frictionless SaaS: experience observability, synthetic and real-user monitoring, and the friction detection engine that surfaces retention issues before they become churn.
2026-04-04

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 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

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 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

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

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

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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