MUSE is the first platform for applied emergent simulation at massive scale. Entities with real internal systems interact in persistent environments and produce outcomes no one scripted.
Most simulations are scripted. Set up a scenario, define the rules, run the clock. MUSE is different. Every entity in a MUSE simulation has its own internal model — biological systems, psychological states, social relationships — that drive its behavior from the inside out. Outcomes emerge from those interactions, not from a script.
This distinction matters more than it might seem. Every other simulation tool is a question-answering model — you define the question first, then build the model to answer it. MUSE is a world model — you build the world, run it, and watch what happens. The answers come out the other side, including answers to questions you never thought to ask.
A city that commissions a MUSE simulation to study a new transit line may discover that the same run reveals unexpected shifts in business district activity, public safety patterns, and neighborhood density — none of which were in the original scope. MUSE runs the world. You observe.
Gothic Grandma builds and maintains every layer of the stack.
The emergent simulation engine and kernel orchestrator. Manages entity systems, heuristics, and state at runtime.
Visual node graph Constructor for defining Universal Entity schemas — the blueprint for every entity in a simulation.
Code generator that turns TESSERA schemas into FONT kernel C++ source. No hand-writing engine code.
The unified internal workbench for kernel engineering, R&D, content authoring, and research.
The LLM and natural language interface used across MUSE tools — bridging simulation state and human language.
The consumer-facing e-reader and marketplace. How the public experiences MUSE Living Worlds.
Open-source scientific pipeline framework for reproducible data workflows. Built for science, not just neuroscience.
Financial modeling and scenario analysis tooling built on MUSE entity simulation.
MUSE is GPU-native from the ground up. Entities are stored in compact Structure-of-Arrays memory layouts designed for throughput, not convenience. Behavioral heuristics use bitmask decomposition and are dispatched via FontDispatch — a custom kernel scheduling system that routes work across CPU and GPU based on biological processing constraints.
The team behind MUSE comes from neuroscience, data science, and cognitive science. The systems aren't metaphors — they're grounded in how brains, bodies, and societies actually work, then optimized for real-time performance.
Large language models are good at generating plausible text. They are not good at modeling the causal dynamics of complex systems. An LLM cannot tell you where the outbreak goes because it has no internal model of a pathogen, a social network, or a healthcare system. It can describe what those things sound like — it cannot simulate what they do.
Agent-based modeling (ABM) tools have existed for decades. MUSE is not one of them — or rather, it is something they were trying to become. The differences are architectural, not cosmetic.
GG.Flow is Gothic Grandma's open-source node-based scientific pipeline framework — and the connective tissue of the entire company. It provides reproducible, composable data workflows for scientific computing, built initially for neuroscience but designed for any domain where you need to move data through processing steps reliably and repeatably.
Inside Gothic Grandma, GG.Flow does the work that holds everything else together:
The open-source core is available for any research lab to use independently. The MUSE-specific node library — the GG.Flow nodes that interface directly with FONT shared memory, run simulation ensembles, and produce MUSE diagnostics — is proprietary.