{R}R Dev Notes

Found total of 39 articles.

Loop Engineering, Chapter 2: The Six Primitives of Loop Engineering

Chapter 2 of the Loop Engineering blog series. If a loop is the engine, the six primitives are the parts. A teaser on automations, worktrees, skills, connectors (MCP), the maker/checker split, and external memory.
2026-06-16

The Forward Deployed Engineer, Chapter 19: The Future of Forward Deployment

Chapter 19 of The Forward Deployed Engineer blog series. The final chapter — the automation wave, what the AI FDE cannot do, the rise of FDE-as-a-Service, the talent market evolution, the customer wave, and the function's longer-term maturity. Plus a closing word, and the book.
2026-06-14

The Forward Deployed Engineer, Chapter 13: When FDE Goes Wrong — Failure Modes and Lessons

Chapter 13 of The Forward Deployed Engineer blog series. Six named failure modes that kill FDE functions before they scale. A teaser on the Consulting Trap, the Snowflake-per-Customer trap, the Hero Engineer, the Demo-Debt Spiral, the Sponsor Collapse, and the Burnout Death Spiral.
2026-06-08

The Forward Deployed Engineer, Chapter 8: The Inner Loop — Prototype to Production

Chapter 8 of The Forward Deployed Engineer blog series. From the end of discovery to the first production milestone, the engagement runs on the inner loop. A teaser on the DARE framework, Minimum Viable Architectures, demo-driven development, the hardening phase, and when the customer wants to help.
2026-06-03

The Forward Deployed Engineer, Chapter 7: Customer Discovery and the Messy Reality

Chapter 7 of The Forward Deployed Engineer blog series. The most important fourteen days of any engagement are the first fourteen. A teaser on the three outputs of discovery, the async interview, the Weird Tuesday problem, the workflow inventory, and the Eval-Customer Split.
2026-06-02

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 4: The Technical Bar

Chapter 4 of The Forward Deployed Engineer blog series. The FDE is, first and last, an engineer. A teaser on the four technical primitives, the non-obvious skills, what you don't need to be, and the four-round interview that actually tests for it.
2026-05-30

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

The Forward Deployed Engineer, Chapter 1: What Is a Forward Deployed Engineer?

Chapter 1 of The Forward Deployed Engineer blog series. The opening chapter of a new book — the operator's contradiction, the Palantir origin, the anatomy of the role, why the AI moment needs it now, and how the FDE differs from every sister role it gets confused with.
2026-05-27

The Engineering of Intent, Chapter 4: The Model Context Protocol (MCP)

Chapter 4 of The Engineering of Intent blog series. MCP is to agents what HTTP was to the early Web — a common protocol that turns bespoke integrations into reusable infrastructure. A teaser on host/client/server roles, the anatomy of a good tool, the six anti-patterns, and the security pitfalls every team trips over.
2026-04-20

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

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

Frictionless SaaS, Chapter 17: Self-Serve Onboarding and Setup

Why self-serve setup converts 2-3x better than assisted onboarding, and the Progressive Setup Pattern and Smart Defaults Strategy that make complex products feel simple.
2026-04-07

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

OpenClaw Engineering, Chapter 6: Extending Capabilities with SKILL.md

The anatomy of SKILL.md files in OpenClaw: how to author reusable, versioned instruction sets with YAML frontmatter, dependencies, and explicit procedural guidance for agents.
2026-03-21

Frictionless SaaS, Part 0: How Users Actually Find, Judge, and Try Your Product

Kicking off a blog series based on the book "Frictionless SaaS." This first post introduces Chapters 0.1 through 0.3 — Discovery, the Landing Page, and Freemium & Entry Points — the three friction points every user hits before they ever sign up.
2026-03-21

7.4 Why QR Is Often Preferred

An in-depth, accessible explanation of why QR decomposition is the preferred method for solving least squares problems and ensuring numerical stability. Covers orthogonality, rank deficiency, Householder reflections, and the broader role of QR in scientific computing, with a smooth transition into eigenvalues and eigenvectors.
2025-10-05

Chapter 7 — QR Decomposition

A deep, intuitive introduction to QR decomposition, explaining why orthogonality and numerical stability make QR essential for least squares, regression, kernel methods, and large-scale computation. Covers Gram–Schmidt, Modified GS, Householder reflections, and why QR is often preferred over LU and normal equations.
2025-10-01

6.1 SPD Matrices and Why They Matter

A deep, intuitive explanation of symmetric positive definite (SPD) matrices and why they are essential in machine learning, statistics, optimization, and numerical computation. Covers geometry, stability, covariance, kernels, Hessians, and how SPD structure enables efficient Cholesky decomposition.
2025-09-28

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

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

5.3 LU in NumPy and LAPACK

A practical, in-depth guide to how LU decomposition is implemented in NumPy and LAPACK. Learn about partial pivoting, blocked algorithms, BLAS optimization, error handling, and how modern numerical libraries achieve both speed and stability.
2025-09-25

5.2 Numerical Pitfalls

A deep, accessible explanation of the numerical pitfalls in LU decomposition. Learn about growth factors, tiny pivots, rounding errors, catastrophic cancellation, ill-conditioning, and why LU may silently produce incorrect results without proper pivoting and numerical care.
2025-09-24

5.1 LU with and without Pivoting

A clear and practical explanation of LU decomposition with and without pivoting. Learn why pivoting is essential, how partial and complete pivoting work, where no-pivot LU fails, and why modern numerical libraries rely on pivoted LU for stability.
2025-09-23

Chapter 5 — LU Decomposition

An in-depth, accessible introduction to LU decomposition—why it matters, how it improves on Gaussian elimination, where pivoting fits in, and what modern numerical libraries like NumPy and LAPACK do under the hood. Includes a guide to stability, practical applications, and a smooth transition into LU with and without pivoting.
2025-09-22

4.4 When Elimination Fails

An in-depth, practical explanation of why Gaussian elimination fails in real numerical systems—covering zero pivots, instability, ill-conditioning, catastrophic cancellation, and singular matrices—and how these failures motivate the move to LU decomposition.
2025-09-21

4.3 Pivoting Strategies

A practical and intuitive guide to pivoting strategies in numerical linear algebra, explaining partial, complete, and scaled pivoting and why pivoting is essential for stable Gaussian elimination and reliable LU decomposition.
2025-09-20

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

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

Chapter 3 — Computation & Mathematical Systems

A clear, insightful introduction to numerical computation—covering norms, error measurement, conditioning vs stability, and the gap between mathematical algorithms and real implementations. Essential reading for anyone building AI, optimization, or scientific computing systems.
2025-09-12

2.2 Machine Epsilon, Rounding, ULPs

A comprehensive, intuitive guide to machine epsilon, rounding behavior, and ULPs in floating-point arithmetic. Learn how precision limits shape numerical accuracy, how rounding errors arise, and why these concepts matter for AI, ML, and scientific computing.
2025-09-09

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

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

1.1 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

1.0 Why Numerical Linear Algebra Matters

A deep, practical introduction to why numerical linear algebra matters in real AI, ML, and optimization systems. Learn how stability, conditioning, and floating-point behavior impact models.
2025-09-02

Numerical Linear Algebra: Understanding Matrices and Vectors Through Computation

Learn how linear algebra actually works inside real computers. A practical guide to LU, QR, SVD, stability, conditioning, and the numerical foundations behind modern AI and machine learning.
2025-09-01

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