Exploring system design, product engineering, technical leadership, and the changing relationship between people and AI—alongside the trails, rituals, and ideas that help us stay curious, healthy, and grounded.
LangChain is a framework for connecting language models to prompts, tools, data, and application logic. Here are the core concepts — prompt templates, chains, retrieval, memory, and agents — and when you actually need them.
Generative AI creates content in response to a prompt. Agentic AI pursues a goal through a series of actions. Here is how the two differ, how they share a common foundation, and why the future likely combines both.
LangChain and LangGraph both help you build LLM-powered applications, but they solve different problems. Here is how they differ, when to reach for each, and why they work well together.
Rushing produces mistakes, rework, and delays. A better approach: begin deliberately, build a reliable process, and let speed emerge from consistency.
LangChain, LangGraph, CrewAI, LlamaIndex, and OpenAI Agents SDK can all build agentic systems. Here is how to pick the simplest architecture that solves your problem, and the framework that fits it.
Learn five essential AI agent terms—AGENTS.md, Agent Skills, MCP, A2A and subagents—and understand how modern AI agents complete real work.
Understand AI agents, reasoning models, vector databases, RAG, MCP, mixture of experts, AGI, and ASI—and how they fit together in real AI products.
A model alone is not an AI application. A tour of the five layers — infrastructure, models, data, orchestration, and application — that turn a model into something useful.
Why background jobs are easy to start and hard to run reliably — delivery guarantees, idempotency, the transactional outbox, and a practical BullMQ implementation.
A practical guide to the layers of today's AI stack — from foundation models and infrastructure to protocols, agent frameworks, developer tools, retrieval, security, and vertical applications.
How fast-growing startups accidentally turn workflows into systems of record — and how to fix it without a rewrite.
Delivery guarantees, idempotency, dead-letter queues, and backpressure — what separates a demo from production pub/sub.
Add topic-based routing so subscribers only receive the events they care about.
Fix blocking publish and failure isolation with per-subscriber queues and async workers.
Build pub/sub from scratch — publishers, subscribers, and the decoupling that makes event-driven architecture work.
