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.

VigilOre and NLP Logix aren't isolated wins. They're instances of a repeatable model. This lesson reveals the training funnel that sits behind all of it. 600+ employees trained across Fortune 500 companies. 95% adoption rate. Industry average is less than 10%. That gap exists because most AI training teaches tools. Tools decay. The 60/30/10 teaches patterns. Patterns survive tool changes. The course companion has five exercises you run with Claude using your own work. Your real processes. Your real bottlenecks. Same approach I use when I walk into a Fortune 500. If you've gone through it, which exercise are you running? 👇