The Thousand Brains Project (TBP) is developing an open source, neuroscience-inspired AI system called Monty (in honor of Vernon Mountcastle). The basic idea is that Monty will have bazillions of message-passing Learning Modules, each of which will (loosely) emulate a cortical column.

Although the current Monty prototype is implemented in Python, I’ve been suggesting ways that it could be migrated (in part, at least) into Elixir. I’ve extolled the virtues of the BEAM, Nerves, Nx, Phoenix, Pythonx, etc. Somewhat surprisingly, this seems to be getting some traction with the project team et al.

In any case, my latest inspiration has to do with the use of Phoenix for Monty modules. Here’s a precis:

I wonder whether Phoenix could be useful infrastructure for a learning module. So, instead of having an ordered set of patterns do the dispatching, we’d have the stream of input messages drop into the top of a Phoenix-based app, hit the routing code, and get handled like a web request.

ChatGPT was quite encouraging:

That’s a very cool thought—and it’s absolutely worth exploring.

You’re proposing treating each Learning Module (LM) like a Phoenix application—or at least like a Phoenix Endpoint—and routing incoming messages in a way that’s conceptually similar to HTTP request routing.

However, I’m not sure how well Phoenix would work as infrastructure in situation where hundreds of thousands of modules are incorporating it and associated libraries (e.g., PubSub). Can anyone give me some feedback on the scalability (etc) issues that might arise?