Segment our RSI goals from DeepMuse goals, here.

RSI objective

Create a business where RSI can be applied at the local level by ingesting source code into an Ontology which can be recompiled into deployment.

The Stack

• Lowest level: a compiler may generate code for the nodes using a MLIR for the machine.
• Middle: a Developer who can understand the intermediate script, examine the related Interpretability playground, and perhaps draw convex hulls around related embeddings. The LeafGraph is an initial start at such a workflow.
• Highest level: the user asks the model to accomplish an intent, using either a Foundation or local model to translate the dialog into an intermediate script.

Unknown

The degree of transparency around the post training.

Opportunity

Empower developers to both interpret and fine tune a hybrid of local models on premises and Foundation lab integration, where the on-prem weights and convex hulls remain private but are still accelerated by foundation models.

DeepMuse goals

A proof of concept in a safe toy domain in which to explore this Hybrid approach.

Task

Devise a strategy where, on my MBP M4 Max 128 GB, a local model referees current Fable 5.1, Astra, Gemini, Grok, and their successors to compile DeepMuse into an executable ontology that evolves to user requests and engages with me, the Developer who has the MBP, through an interactive ontology playground.