At the end of the last post I said I owed you something: what runtime authority over coordinates actually costs, and how you would train a model to use it. I've spent the time since building instead of writing - small language models, from under a million parameters to about eight million, trained on a desktop GPU or less, with each claim I could build at that size turned into an experiment whose pass mark I wrote down before the first run. This is the report. It is mostly a list of things that didn't work, and I've come to think the list is worth more than a win would have been, because each failure moved somewhere specific. I'll keep the scale caveat in front, where it belongs: everything below is small models, short training, synthetic data. It can falsify a mechanism. It can't vindicate an architecture. A note on units, because I'll use one: loss here is measured in nats , the model's average surprise per character. On text these models sit around 1.3 -...

Two posts ago I argued the coordinating layer of an agent system should stay deliberately dumb , and last time I laid out the contract at the seam that makes "dumb" mean something you can hold yourself to. Both made the same promise and neither paid it. Post one said the submodels "should go deep in their own native regime." Post two said "all the intelligence lives below the seam." Fine. What does deep mean? The lazy answer is: bigger. More parameters pointed at a narrower problem. That answer quietly gives the whole argument back to the monolith — if depth is just scale, the monolith wins by having more of it, and my two posts collapse into an efficiency argument about compute budgets. Depth isn't scale. Depth is owning the representation. The habit nobody questions Here is how audio has been fed to models for most of the last decade, and how a great deal of it still is. Take the waveform. Cut it into windows. Run a Short-Time Fourier Transform over...

Last time I argued that the coordinating layer of an agent system should stay deliberately dumb — slow, discrete, symbolic — while the perceptual and temporal work gets pushed down into specialized submodels. The obvious follow-up question, and the one I got asked most, was: fine, so what does that actually look like? Isn't a dumb orchestrator just a control loop with tool calls, which everyone already has? That's the right challenge, and answering "draw the box diagram" doesn't meet it — because the diagram does look like an agent loop. The difference isn't the shape. It's the contract at the seam. Dumb-by-design is a set of rules you hold to about what crosses the boundary, and every one of them is a rule most real systems quietly break. Here are the five that matter. 1. The orchestrator holds no perceptual state. It never touches the waveform, the pixels, the raw stream — only summaries handed up by submodels. This sounds obvious and is violated co...

There is a quiet assumption running underneath most agent architectures right now: that the path to capable systems is one model that does everything. Make it bigger, make it multimodal, bolt on tools, and eventually the single monolith reasons, perceives, plans, and acts. Mixture-of-experts is the industry's one concession to specialization, and even that is a routing trick, not a design. I think this is backwards. The better structure is a slow, discrete, almost boring orchestrator sitting on top of fast, specialized submodels that each work in their own native regime — and you should resist every temptation to collapse the two into one. The nervous system already made this bet. Your prefrontal cortex is a slow serial planner. It does not process the raw waveform hitting your ear or the photon stream landing on your retina. Sensory cortices do that, in parallel, in a temporal regime the prefrontal cortex could never operate in, and they hand up a compressed summary — "a voi...