{R}R Dev Notes

Found total of 32 articles.

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 22: A Day in the Life — A Narrated Session

Chapter 22 of The Engineering of Intent blog series. A narrated recreation of a real, medium-complex working day — hour by hour — because average days are where practice gets tested. A teaser of the hourly shape, and the single rule behind all of it: agents are leverage on thinking you've done.
2026-05-08

The Engineering of Intent, Chapter 5: Agentic Editors and Flow States

Chapter 5 of The Engineering of Intent blog series. The editor is where the wiring meets your hands. A teaser on the three generations of editor, how semantic search amplifies your codebase's virtues and vices, the flow killers that destroy productivity, and the shortcut rebind that doubled a team lead's output.
2026-04-21

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

Frictionless SaaS, Chapter 23: Pattern Libraries and Proven Approaches

Frameworks are nice. Patterns are what you actually ship. A teaser for Chapter 23 of Frictionless SaaS, introducing the Fast Activation Pattern Library, the Frictionless Onboarding Catalog, and a set of high-performing product patterns borrowed from the SaaS companies that get activation right.
2026-04-13

Frictionless SaaS, Chapter 22: AI, Automation, and the Future of Frictionless Design

In the AI era, features are commoditized overnight. So what actually becomes defensible? A teaser for Chapter 22 of Frictionless SaaS, covering the AI-Era SaaS Framework and the Experience Moat — the only lasting competitive advantage left.
2026-04-12

Frictionless SaaS, Chapter 5: Just-In-Time Learning - Teach Users at the Exact Moment They Need It

Chapter 5 of the Frictionless SaaS blog series. Users don't want to learn your product - they want to solve their problem. Just-In-Time Learning teaches at the moment of need, and the Skippable Onboarding Principle respects the users who already know what they're doing.
2026-03-26

OpenClaw Engineering, Chapter 10: Multi-Agent Systems

Build teams of specialized agents that work in concert. Learn how to architect planners, coders, critics, and surveyors, coordinate them via channels, and use adversarial collaboration and taste gates for high-quality output.
2026-03-25

OpenClaw Engineering, Chapter 7: The Skill Ecosystem

Bundled skills vs workspace skills, skill discovery and context, publishing to ClawHub, managing 13,000+ community skills without collision, semantic search, and the meta-skills that let agents improve themselves.
2026-03-22

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

OpenClaw Engineering, Chapter 5: Connecting Multiple Channels

How to connect your OpenClaw agent to multiple messaging platforms (Telegram, WhatsApp, Discord, Slack) and manage multi-channel routing. Setup, configuration quirks, and troubleshooting for each platform.
2026-03-20

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

Chapter 14 – Connecting Systems with the Model Context Protocol (MCP)

Chapter 14 of Master Claude Chat, Cowork and Code explores the Model Context Protocol — the universal bridge that lets Claude connect to Slack, GitHub, Jira, Google Drive, and more, turning isolated AI into a deeply integrated workflow partner.
2026-03-15

Chapter 13: Encapsulating Knowledge with Agent Skills — From Conversations to Autonomous Procedures

Chapter 13 of Master Claude Chat, Cowork and Code introduces Skills — reusable, encapsulated procedures that Claude executes autonomously. Covers SKILL.md structure, YAML frontmatter, trigger descriptions, and the Skills Library pattern for team distribution.
2026-03-14

Chapter 6: What Is Claude Cowork? — The Desktop Agent That Touches Your Files

Chapter 6 of Master Claude Chat, Cowork and Code introduces Claude Cowork — a sandboxed desktop agent that automates file management, data extraction, and cross-application workflows on your local machine.
2026-03-07

Master Claude, Chapter 4: Context Persistence with Claude Projects — Solving the AI Amnesia Problem

Chapter 4 of Master Claude Chat, Cowork and Code explains how Claude Projects solve the AI amnesia problem with persistent context — custom instructions, knowledge bases, and shared team workspaces that remember your architecture, conventions, and patterns across every conversation.
2026-03-05

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

5.1 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

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

3.1 Norms and Why They Matter

A deep yet accessible exploration of vector and matrix norms, why they matter in numerical computation, and how they influence stability, conditioning, error growth, and algorithm design. Essential reading for AI, ML, and scientific computing engineers.
2025-09-13

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

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

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

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

Message Extensions｜Mastering Microsoft Teams Bots 4.3

Learn how to build search- and action-based Message Extensions in Microsoft Teams. This section shows how to let users interact with your bot directly from the message composer — to search records, fill forms, or insert rich cards — all without leaving the chat.
2025-04-13

Proactive Messaging｜Mastering Microsoft Teams Bots 4.2

Learn how to build bots that initiate conversations in Microsoft Teams. This section explains proactive messaging — including when and how to use it, how to store conversation references, and best practices to ensure your bot helps without interrupting.
2025-04-12

Overview of Microsoft Teams Bot Capabilities｜Mastering Microsoft Teams Bots 1.3

Explore the full range of capabilities bots can offer in Microsoft Teams. This section breaks down interactive contexts, features like Adaptive Cards, proactive messaging, user authentication, Graph API integration, and what limitations still exist. Get a developer’s guide to what’s possible.
2025-04-04

Overview of Microsoft Teams Architecture｜Mastering Microsoft Teams Bots 1.2

Get a developer-friendly introduction to how Microsoft Teams is built. This section explains Teams architecture—channels, tabs, bots, messaging extensions, and Graph API—and shows how each component fits into the broader platform. A must-read before building your first bot.
2025-04-03

Mastering Microsoft Teams Bots: A Complete Developer’s Guide

The definitive guide to building bots for Microsoft Teams—from fundamentals to deployment. Learn how to build intelligent and interactive bots using the Microsoft Bot Framework, integrate Adaptive Cards and Task Modules, send proactive messages, authenticate users with Teams SSO, and deploy securely on Azure. Packed with practical examples and real-world use cases, this book will help you automate workflows, enhance collaboration, and deliver smart experiences inside Teams.
2025-04-01

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