Before we build it, let’s understand the work. Every Monday, we’ll take a recurring task and break it down: what starts it, what information it needs, what decisions happen, and what should come out the other side. Last Wednesday, we looked at a workflow we’ve been building at GLR and asked: What happens when the workflow runs again? That question matters before you build anything, too. Mapping the work helps you spot the decisions that are easy to skip when you’re focused on getting something running. For this first one, let’s use a simple example: 📝 A meeting ends. You have messy notes. Now those notes need to become something you can act on. “Have AI summarize them” sounds like a complete task. But there are a few decisions hiding inside it. Here’s the map: ⚡ TRIGGER The meeting ends and the notes are ready to review. 📥 INPUT The notes you actually captured. Some things may be incomplete or unclear. 🔀 DECISIONS / TRANSFORMATION Separate confirmed decisions, action items, reference information, and open questions. Was that an actual commitment, or just an idea someone brought up? Was an owner assigned? Was a deadline agreed on? 🧑‍💻 HUMAN CHECK Compare the result with the notes before treating it as the record of what happened. Confirm commitments, owners, and dates. Keep anything unresolved marked as unknown. 📤 OUTPUT A reviewed recap and task list, with unanswered questions still visible. ⚠️ WHAT COULD GO WRONG? “I could probably handle that” becomes an assigned task. “Maybe next week” becomes a deadline. An idea becomes a decision nobody made. The result can look organized and still be wrong. 🧪 FIRST TEST Try this with one set of notes. Check every action item against the original. Can you trace each owner, deadline, and commitment back to something actually recorded? If you can’t, that’s something to resolve—not a blank for AI to fill. Your turn: Pick one task that keeps coming back and map these five parts: Trigger → Input → Decisions / transformation →

Welcome to the workbench. Every Wednesday, we’ll open up something real — a build, problem, decision, failure, experiment, or lesson from inside the Lab. For the first one, here’s something from a system we’ve actually been building at GLR. We had a workflow that could find leads due for follow-up, send the right alert, and update the record. That worked. But working once isn’t enough. ⚠️What happens when the workflow runs again?⚠️ If a lead has already been handled, the system needs to recognize that and do nothing. So we tested it again. The workflow still woke up. It still checked the data. But there were no eligible leads. The alert paths stayed inactive. No duplicate message. No unnecessary update. That small test represents a much bigger systems principle: A workflow isn’t finished when the happy path works. You also need to know: → What happens the second time? → What happens when there’s nothing to do? → What happens when something already happened? → What stops the system from repeating itself? Sometimes the most important thing a system can do is know when not to act. My workflow is dropped below! Your turn: Think about a workflow you use, want to build, or deal with regularly. What could go wrong if it accidentally ran twice? 👇Drop it below.👇 🧪 Unfinished answers are welcome — this is the Lab.🧪

GLR Systems Lab is officially open! The Lab is built for people who want to do more with AI than collect prompts or chase tools. Start with real work. Map how the work actually happens. Decide where AI belongs. Test what it produces. Keep human judgment visible where it matters. Improve from evidence. Then reuse what works. Inside the Lab now: 🧠 Start Here 📚 Lab Basics 🛠️ Notes to Action 🔧 Builder Kit 💬 Build in Progress 🆘 Workflow Help 🧪 Ideas & Experiments ✅ Show Your System If you're new, start in START HERE and choose one recurring task from your real work. You do not need a polished system before you participate. Bring the real version. Build it. Test it. Improve what actually breaks. Welcome to GLR Systems Lab!

GLR Systems Lab works best when we treat this like a working lab, not a content feed. The goal is to learn by understanding real work, breaking it into systems, testing ideas, and improving what actually works. WHEN YOU POST OR ASK FOR HELP Give enough context for people to understand the system. Whenever possible, include: - What you’re trying to accomplish - What the current process looks like - Where the friction or failure is - What you’ve already tried - What result you’re trying to improve You do not need to have the technical answer before posting. A clear description of the work is enough to start. WHEN YOU SHARE SOMETHING YOU BUILT Don’t just show the finished result. Tell us: - What problem it addresses - How the workflow works - What you tested - What failed or changed - What you learned Failures are useful here. A system that broke and taught you something can be more valuable than one that worked once. HOW WE APPROACH AI We don’t start with: “What can AI automate?” We start with: “What is actually happening in this process?” Then we decide whether AI, automation, software, or a simpler process change makes sense. Our core process stays the same: Understand → Map → Build → Test → Improve → Reuse KEEP THE LAB USEFUL Be constructive. Ask real questions. Share real work. Avoid spam, hype, fake results, and copying things you don’t understand just because they look impressive. The goal is simple: Build systems you understand well enough to test, improve, and trust.

Before we talk tools, automations, or AI, start with the work itself. Drop a comment below and tell us: 1. What do you do? 2. Your job, business, role, or the kind of work you spend most of your time on. 3. What’s one thing you do repeatedly? 4. A task, process, handoff, follow-up, report, decision, or piece of work that comes up again and again. 5. What about it feels inefficient, repetitive, confusing, inconsistent, or harder than it should be? Don’t worry about whether it should be automated yet. The point is to understand the work first. That’s how we’ll approach systems inside GLR Systems Lab: Understand → Map → Build → Test → Improve → Reuse Your answer may also help shape future examples, breakdowns, and workflow builds inside the community. Start with the real work. We’ll figure out the system from there.