What do you wish you understood better? 👇 I want to hear from everyone. Every level. Day one or day one thousand. There's no question too small, too basic, or too "I feel like I should already know this." If you've ever wondered how something works, what something means, or how to do it, that's exactly what I want. Two quick things 👇 1️⃣ Vote in the poll for the big topics you want more of. 2️⃣ Drop your specific question in the comments. The more specific, the better. The comments can serve as building a list from your answers. The creators in this group are incredible, and once we know what you're asking for, we can make the content that answers it. (This is a post aimed at helping all of us make more engaging content) Even if you mostly read and rarely post, this is an opportunity to have someone write about something you may not have an answer for. One question from you might be the exact thing ten other people were too shy to ask. 😊 We learn together, we grow together, we win together! 🤓💪🏆Bas Question: What do you want us to make more content on?

Brian Eno is the north star for how I think about AI systems. The production philosophy: he didn't make music, he made conditions for music to happen. He'd set up constraints, point musicians at instructions, and leave. What came back wasn't his. That was the point. I've been building an AI operating system for my studio for about a year. Recently I renamed everything in it using music language, because the music metaphors turned out to be more precise than the business ones. The first stage of any transformation sprint is called Signal. In music, a signal is what carries meaning from source to listener. In this work, Signal is the diagnostic stage: find the one operational constraint worth solving and quantify what it costs. Most companies skip this. They start at Build: pick a tool, deploy it, wonder why nothing changed. The signal was never found, so nothing has anywhere to go. The second stage is Source: map where the knowledge actually lives before designing anything. In music, sourcing is knowing what's in the room before you hit record. What systems, what people, what undocumented institutional knowledge. You can't arrange what you haven't inventoried. The third stage is Arrangement. This is where you decide who plays what, human versus agent, before a line of code is written. The arrangement is the most underrated stage in both music and AI work. Everyone wants to talk about the performance. Nobody wants to do the arrangement. The agent that runs governance in my system is called The Monitor. Named for studio monitors: the speakers that give you accurate playback rather than flattering playback. They tell you what's actually there. The Monitor checks every output before it ships: voice, facts, completeness. Returns either CLEAR or HOLD. A monitor that flatters you isn't doing its job. The gap-detection agent is called The Oblique, named for Eno's Oblique Strategies, the card deck he made for breaking creative blocks by approaching the problem from an unexpected angle. The Oblique reads the system periodically and surfaces what still requires manual intervention. Everything it finds is a proposed next build.

It's easy to have 1 agent that works great solo. The wall comes when you try to make them work as a team. You run one agent, copy the output, open another window, paste it in, run the next one. You're the integration layer. You're the thing holding the handoffs together, by hand, every time. I want to walk through one session where the system did the stitching instead of me, and show you the mechanics — what each step actually loaded and did — because that's the part you can build, not the part you feel. The setup was ordinary. We're scoping a project for a nonprofit prospect. Just got off a call, I had the transcript, and they were owed a follow-up with a ballpark on cost. I typed one line into the chat: "ingest this transcript and update the folder on this prospect." Then I watched what the orchestrator did with it. First, what "Duke routed it" actually means. Duke is my orchestrator. He doesn't do the work. He reads the request, decides which specialist owns it, and loads that specialist's instruction set before handing off — the role file, the voice doc, the guardrails, and whatever skills the task calls for. The routing IS the framing. When Duke "passes it to Pike," that's really Duke loading Pike's instructions into the context so the next thing that runs is a framed researcher, not a blank model with a prompt taped to it. So the transcript went to Pike, my research specialist. Pike ran the ingest skill against the raw transcript — pulled what changed since the last call, which open questions got answered, the next step, a couple of risk flags — then loaded the existing prospect folder so it added to what was already there instead of rebuilding from zero. Output: an updated folder and a clean read on where the deal stands. That updated read made the actual next step obvious — the prospect was owed a pricing email. Duke loaded Cash, my copywriter: my voice doc, the guardrails, the prospect context Pike had just refreshed. Cash drafted the email. Notice the handoff structure — Pike's output became the framed input for Cash. The folder Pike updated is what Cash read to write.

Last week I gave my LLM a memory layer I call Cortex. This week I started feeding it something stranger: what I was listening to while I worked. A work session is not just the files you touched and the decisions you made. It has a texture. The track that was playing when something finally clicked is part of that memory, even if you would never think to write it down. So instead of throwing that signal away, Claude and I built a small observer to catch it. ———————————————————————— What it does, in three layers: 1. Passive. A tiny watcher checks the local music app once a minute and logs track, artist, and timestamp to a plain markdown file. No browser audio, no streaming history scraped, just what is actually playing on the machine. 2. Bookmarks. When a session opens or closes it drops a marker, so the log has boundaries instead of one flat stream of songs. 3. Flags. When a track lands on a moment that matters, I star it with one line of context. "This was playing when the gallery finally rendered." That markdown file is just another source the brain reads. Same rule as everything else: > Files own the truth. The brain owns the connections. ———————————————————————— Here is the part I do not know yet, and why it is interesting. The episodic layer now carries an ambient track. Does that change retrieval? When I come back to a problem, will the brain surface the session by its soundtrack the way a smell drags back one specific afternoon? Or is it just noise in the index? I genuinely cannot tell you. It has been running for two days. That is the honest bit. This is an experiment, not a feature. I built the observer in an afternoon because the cost of being wrong is a markdown file I can delete. The cost of being right is a brain that remembers the way humans actually do, by association and atmosphere, not just by fact. The try-me is attached if you want to point one at your own memory layer. It is about forty lines. Watch what your brain does with it before you decide whether it earned its place.

Creative reference libraries rot. You save things, you forget them. Your aesthetic lives between your eyes and your past work, and re-explaining it every brief doesn't scale. I run two streams. Trend cards distil external references: one card, one thesis sentence, operative moves. DNA profiles map my own work: every claim cites a real anchor example. Different questions. Don't mash them. Nothing publishes without passing a HALT gate. No card without a thesis I'd defend. No DNA claim without an anchor I can point to. Read the deep-dive: https://www.aris-space.com/documents/tools-and-plugins/lens Taste is a structured artefact, not a vibe. The gate is the discipline. //A<3