Field Guides Tagged as "AI"

AI engineering, models, and building AI-powered apps.

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Mastering n8n

Mastering n8n

If Directus is the first thing I stand up on a new project, n8n is the second. It's the workflow automation tool I reach for whenever something needs to talk to something else — webhooks, scheduled syncs, API orchestration, and increasingly, AI agents that actually do work instead of just chatting. The pitch is simple: a visual canvas where each node is a step, the data flows down the chain, and you can drop into real JavaScript any time the visual builder runs out of road. Self-host it on a single box, point it at Postgres and Redis, and you've got a durable automation engine you fully own — no per-task pricing, no vendor watching your data go by. This is the guide I'd hand a developer who's tired of gluing services together by hand: how to self-host n8n properly with Docker and queue mode, how the data model and expressions actually work, how to handle errors like you mean it, how to build real AI agents with the LangChain nodes, and how to wire it up to Directus so the two cover each other's weak spots. _This is a living document and will be updated as n8n updates._

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Running the Fleet: A Field Guide to Multi-Agent Orchestration

Running the Fleet: A Field Guide to Multi-Agent Orchestration

You built one agent that knows you cold — its own personality, a memory that survives, skills it runs your way. It's a great employee. It's also still one employee, doing one thing at a time, waiting on you to hand it the next task. This guide is about the next move: turning that single agent into a coordinated fleet that plans, executes, and monitors real goals while you supervise instead of operate. It's the deep-dive sequel to [Building Your Agentic OS](/guides/building-your-agentic-os) — where that guide ended by pointing at the horizon, this one walks the whole distance. We build it on Hermes, because Hermes already ships the hard parts: profiles (every agent a full citizen with its own identity and memory), a durable kanban board that coordinates them, a decomposer that routes a dropped-in goal to the right specialists, and a way to package a whole agent as a git repo and hand it to your team. Concrete throughout, honest about the sharp edges, and built so the foundation you already laid is exactly what scales up.

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Building Your Agentic OS: A Field Guide

Building Your Agentic OS: A Field Guide

Two people can use the exact same AI agent and get wildly different results. It's almost never the prompting. One of them built a system underneath the tool — a layer that gives the agent a persistent identity, a real memory, and a set of skills it runs the same way every time — and the other is still re-explaining themselves at the start of every session. This is a field guide to building that system, in three moves. First, the OS itself: a plain-files architecture you can stand up this afternoon on whatever agent you already use, built around three pillars — personality, memory, and skills. Second, the pivot — graduating that static setup onto Hermes, Nous Research's open-source, self-hosted agent, so the files stop being a brief you read aloud and become a teammate that runs on its own. And third, where it's all heading: coordinating many agents to plan, execute, and monitor real goals, with the orchestration, shared memory, and governance that make a true agentic OS. Everything here is portable by design, and you build it one working piece at a time — starting with a single agent that actually knows you.

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The 70/30 Engineer

The 70/30 Engineer

The machine does about 70% of the typing now. That part is real, and it's not going back in the box. But the work that's left — guiding the agent, verifying what it produces, and owning the result — is the other 30%, and it turns out that 30% is the entire job. This is a field guide to working that way on purpose: a real, adoptable workflow for coding with agents, built on the AC/DC loop (Guide, Generate, Verify, Solve), grounded in what a year of agentic development actually taught the industry — the wins, the data, and the failure modes nobody likes to talk about. It's for the engineer who wants to be faster without shipping 1.7x more bugs to prove it.

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The AI-Era Fractional CTO's Field Guide

The AI-Era Fractional CTO's Field Guide

"Fractional CTO" has become one of those titles that means everything and nothing. If you're a founder, it's often unclear what you're actually buying — a part-time CTO? a fancy consultant? a cheaper hire? And if you're an experienced engineer, it looks like an appealing way out of full-time employment, but with no real map for how to do it well. This guide is for both of you — a straight, advisory look at what the role actually is, when a company needs one, what it costs in 2026, and the AI and agentic-team leadership that increasingly defines the job.

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The Product Engineer's Guide to AI-Powered Development Workflows

The Product Engineer's Guide to AI-Powered Development Workflows

It's 3 AM, and you're staring at a production issue that's affecting 15% of your users. The error logs are cryptic, the stack trace points to three different microservices, and your team lead is asking for an ETA on the fix. Six months ago, this would have meant hours of debugging, context switching between monitoring tools, and probably a very late night. But tonight is different. You paste the error details into Claude, describe the user impact, and within minutes you have a clear diagnosis: a race condition in your payment processing service triggered by a recent deployment. More importantly, you have a tested fix and a deployment strategy that minimizes risk. What used to be a 4-hour debugging session just became a 20-minute resolution. This isn't science fiction—it's the new reality for product engineers who have learned to work alongside AI. After two decades in this field, building everything from travel platforms to AI automation tools, I've witnessed many technological shifts. But nothing has transformed my daily workflow quite like the AI revolution we're experiencing right now. The difference between traditional developers and product engineers has never been more important. While developers optimize for technical elegance, product engineers think backwards from user needs. We're the bridge between "what's technically possible" and "what actually solves problems." And AI doesn't just make us faster coders—it amplifies our ability to maintain that crucial product context while handling increasing technical complexity. In this guide, you'll discover how to build an AI-enhanced development workflow that spans from initial product discovery to deployment and monitoring. I'll share the specific tools and integrations that have transformed not just my productivity, but my ability to deliver user value faster and more reliably. You'll see real examples from recent projects, learn a framework for evaluating AI tools, and understand how to implement these changes without disrupting your team's existing rhythm. This matters more than ever because we're at an inflection point. Recent data shows that 76% of product leaders expect their AI investment to grow in 2025, and developers using GitHub Copilot report up to 55% productivity improvements without sacrificing code quality. But here's what the statistics don't capture: AI isn't just making us faster—it's making us better product engineers by freeing us to focus on the strategic, creative, and deeply human aspects of building products that people love. The developers who thrive in the next decade won't be those who resist AI or those who blindly adopt every new tool. They'll be the ones who thoughtfully integrate AI as a force multiplier for product thinking, user empathy, and creative problem-solving. If you're ready to become one of them, let's dive in.

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