SL#50 - Build a Claude Sub-Agent That Reviews Your PRs Against a Team Style Guide
A 200-line TypeScript build using the Claude Agent SDK. The non-obvious bit: one sub-agent per file beats one big prompt, and it costs less. Plan for 90 minutes if you have used the Agent SDK before, 3 hours if this is your first time.
SL#49 - The Eight-Hour Outage After a Seven-Minute Fix
Railway's Google Cloud account was suspended at 22:20 UTC and reinstated at 22:29. The platform was still down at 06:14 the next morning. The lesson is bigger than Railway.
SL#46 - Your LLM Agent Is Drowning in Its Own Context Window
Context windows just hit two million tokens. So why are 5% of production AI requests still failing? Because the industry confused having more space with knowing what to put in it.
SL#44 - Building Agentic RAG Systems: Architecture, Reasoning Loops, and Production Considerations
The transition from simple LLM wrappers to AI Agents represents the next frontier in software engineering. While traditional Retrieval-Augmented Generation (RAG) improved LLM accuracy, Agentic RAG introduces a reasoning layer that allows the system to autonomously decide how to use data to solve a problem.
SL#43 - Beyond the Chatbox: How MCP Turns LLMs into Autonomous Operators
The Model Context Protocol (MCP) dismantles the "data silos" of modern AI, providing a standardized bridge for LLMs to move beyond conversation and into direct, real-world execution.
SL#42 - From Passive LLMs to Autonomous Agents: The Evolution of AI Workflows
The field of Artificial Intelligence is rapidly evolving from simple text generation to autonomous problem-solving. To understand where the industry is heading, technical professionals must distinguish between three distinct levels of AI implementation: Passive LLMs, AI Workflows, and Autonomous AI Agents.
