Short list. It exists so this stays a place where people actually help each other. ONE - BE USEFUL, NOT PROMOTIONAL Points and status here come from helping people, not from marketing at them. If a post is mostly about you, it is probably a promotion. TWO - SHOW YOUR WORK Post the screenshot. Post the error message. Post the workflow that half works. "Here is what I built" and "here is where I am stuck" are the two best kinds of post on this platform, and both are equally welcome. THREE - NO POACHING, NO DROPPING YOUR OWN PAID OFFERS Do not DM members to sell to them. Do not post affiliate links or your own paid products without asking me first. Ask, and the answer is often yes. FOUR - SEARCH FIRST, BUT NEVER FEEL BAD FOR ASKING A quick search saves everyone time. But nobody here gets mocked for a beginner question. Everyone started at Tinkerer, including me. FIVE - KEEP IT REAL This is a build community, not a hype channel. If something did not work, say so. If a tool is overrated, say that too. An honest post about a failure is worth more here than another thread about how AI changes everything. IF YOU BREAK THEM First time, the post gets removed and I will tell you why. Repeat, and you are removed from the community. Spam and DM selling skip straight to removal. Unsure whether something is allowed? Ask me before you post it.

Welcome. This community exists to get you from watching automation tutorials to running things that actually ship. Here is the fastest path in. STEP 1 - INTRODUCE YOURSELF Post a comment below telling us your role, what you already self-host or want to, and the one thing you would automate first if it were easy. That last answer is the most useful thing you can give us, because it tells everyone here what you actually need. STEP 2 - PICK ONE COURSE AND START IT There are four in the Classroom and they do different jobs. AI & Automation: Build Intelligent Workflows - start here if you are not sure what to automate. It covers the decision: which processes are worth it, how to design one properly, and how to prove it paid. Six modules across marketing, sales, support, finance, HR and operations. n8n for Enterprise - start here if you already know what you want to build and need the tool. 28 lessons, hands-on. Hands-On Virtualization with Proxmox VE - the infrastructure to run all of it yourself. 38 lessons. Claude Code Mastery - agentic AI, building real apps, and delivering client projects. 14 modules. STEP 3 - POST WHAT YOU BUILD, INCLUDING THE BROKEN BITS A screenshot of a workflow that half works, with a question attached, is the single most valuable post here. It helps you, and it helps the next person who hits the same wall. HOW LEVELS WORK You earn points when people like your posts and comments, and levels unlock more of the Classroom. The ladder runs Tinkerer, Automator, Builder, Self-Host Operator, Agent Engineer, Automation Architect, AI Systems Pro, Inner Circle, Legend. The fastest way up is not posting more. It is being useful: answering questions, sharing what you built, saying what did not work. ONE ASK When you read something helpful, like it. That is what moves people up the ladder and it costs you nothing. Good to have you here.

Most AI projects that stall do not stall because someone wrote a bad prompt. They stall because the prompt was the only thing anyone engineered. In 2026, “the prompt is wrong” is rarely the real diagnosis. Missing context, stale retrieval, an agent with far too many tools, no approval boundary, no trace to debug from: that is what actually breaks in production, and none of it lives inside the prompt. The cleanest way I have found to reason about this, after shipping 55+ agents into production, is to treat AI system design as three nested layers. Prompt engineering is the wording and structure of a single instruction. Context engineering is deciding what the model sees at each turn. Harness engineering is the runtime system around the model: orchestration, validation, approvals, tracing, evals, and governance. Anthropic frames context engineering as the natural progression of prompt engineering. OpenAI’s harness engineering framing extends the same line one layer out, to the full agentic system. This piece walks each layer, shows where it breaks, and gives a build order. If you want the architecture patterns that sit underneath, read AI Agent Architecture: Reference Patterns alongside this. The one-line version Prompting solved single-call behaviour. Context solved multi-turn cognition. Harnessing solves production reliability. They are layers of responsibility, not rival techniques. You do not pick one. You build all three, in that order, and most of the reliability of a real system comes from the outermost layer, not the innermost. A short history, so the vocabulary makes sense The terminology here is still settling, and different teams use different labels for overlapping work. Reading it as nested layers avoids the confusion. Prompt engineering became strategic with large-scale in-context learning around 2020, when GPT-3 style few-shot prompting showed that the instruction itself was a design surface. Chain-of-thought prompting, self-consistency, and automated prompt search followed.

Dear All Long wait is over, I will be publishign new content soon

I just uploaded a demo showing how you can turn Microsoft Teams into a fully automated, 24×7 AI Assistant using n8n. This system understands who is asking, what they need, and interacts with tools like ERP, CRM, BI, HR, and Finance to deliver instant, personalized responses. Whether it’s leadership insights, sales updates, IT support, or employee policy questions—everything is automated inside Teams. This is a powerful example of enterprise-grade automation that can be extended to any department or workflow. 🎥 Watch the video now and see what’s possible.Let me know what use cases you want to explore next!