{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 Forward Deployed Engineer, Chapter 2: The Last-Mile Problem in Enterprise AI

Chapter 2 of The Forward Deployed Engineer blog series. Where SaaS stopped at the enterprise threshold, AI has to walk the last mile. A teaser on the four frictions at the last mile, the integration tax no demo shows, and why workflow redesign — not the model — is the product.
2026-05-28

Chapter 22: Identity in AI Systems — When the "User" Is an Agent

Chapter 22 of the OpenID: Modern Identity series — identity for AI systems: LLM authentication, the Model Context Protocol (MCP), Dynamic Client Registration for ephemeral agents, and the emerging patterns for trusting autonomous non-human actors.
2026-03-28

Chapter 21: Decentralized Identity — DIDs, Verifiable Credentials, and OID4VC

Chapter 21 of the OpenID: Modern Identity series — decentralized identity: DIDs (Decentralized Identifiers) without a central authority, Verifiable Credentials with selective disclosure, and OpenID for Verifiable Credentials (OID4VC) as the bridge from centralized to decentralized identity.
2026-03-27

OpenClaw Engineering, Chapter 2: Anatomy of the Agent Brain

How OpenClaw agents think through their identity files, two-layer memory system, and proactive task scheduling. A deep dive into SOUL.md, AGENTS.md, USER.md, MEMORY.md, HEARTBEAT.md, and semantic memory via Supermemory.
2026-03-17

OpenClaw Engineering, Chapter 1: The OpenClaw Paradigm

The first chapter teaser in a new series on OpenClaw Engineering. Why autonomous agents need a different foundation, the four-layer architecture (Gateway, Nodes, Channels, Skills), and the three principles that hold it all together.
2026-03-16

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 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 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 11: Architectural Thinking

Architectural thinking is the discipline of designing systems that survive real-world growth. It means asking how your code will feel to live in years from now.
2026-01-06

8.1 Power Method and Inverse Iteration

A clear, practical, and intuitive explanation of the power method and inverse iteration for computing eigenvalues. Covers dominance, repeated multiplication, shifted inverse iteration, and real applications in ML, PCA, and large-scale systems. Smoothly introduces the Rayleigh quotient.
2025-10-07

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

Chapter 6 — Cholesky Decomposition

A deep, narrative-driven introduction to Cholesky decomposition explaining why symmetric positive definite matrices dominate real computation. Covers structure, stability, performance, and the role of Cholesky in ML, statistics, and optimization.
2025-09-27

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

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

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