The philosophy page is stance. This page is what is on the bench: repos, numbers, failed runs, things you can click. I care whether human–machine teams stay auditable when more than one mind is in the loop: who decided what, who can push back, who owns the file when three agents and two humans are editing at once.
Superintelligent Group & supercoordination
I run Superintelligent Group (ΣIG). The product direction is superintelligent human–machine teams: tooling where people and models share context, revise each other's work, and keep agency on both sides.
The platform's world model has twelve canonical primitives—Workspace, Project, Actor, Collective, Task, Artifact, Locus, Claim, Run, Event, Precedent, Canon—that compose into higher structures like Virtual Autonomous Companies (VACs). The key architectural decision: claims and the work graph are canonical; VACs are packaging on top, not the root ontology. Agents operate with bounded authority via context packets(scoped read/write/tool permissions, budgets, leases), and every mutation is tracked through causation-linked events in a durable event log.
On the research side I am developing supercoordination: how coordination primitives scale when many agents—and the humans answerable to them—share state, precedent, and conflict. In our multi-agent experiments, pods of three to five agents sustained roughly 2–5% coordination overhead at 140 agents over 12+ hours. When we pushed past ten agents per pod, coordination replaced the work: threads multiplied, quorum latency climbed, context windows overloaded. Naive peer-to-peer messaging at 50+ agents produced O(n²) message . The AI Civilizations thread documents these runs, failure modes, and what “ as organization” looks like in practice.
Neural assemblies & computational linguistics
In parallel I study theories of computational linguistics through the neural assemblies framework—the Assembly Calculus and related models where language is learned from grounded experience rather than assumed as an innate grammar module. The question that keeps me here: can we explain word learning, composition, and working-memory limits with mechanisms that look like brain-scale computation, not backprop-through-a-transformer?
The implementation is real and runnable. The core operations—project, associate, merge, separate, sequence memorize, ordered recall—run on sparse k-winner-take-all populations with Hebbian plasticity. Default experiments use 10,000 neurons with k=100 active per step. Cross-area projection recovers assemblies at 1.000 overlap by ~10 rounds; associations reach 0.994 recovery at 30 rounds. Chains of association losslessly through five hops at k²p ≈ 100–500 connections per hop. I've formalized the N400 ERP as global pre-k-WTA energy (d = −25 to −31, p < 0.001), measured against 13 post-k-WTA metrics that show the reversed effect.
On the language side, the EmergentParser implements ~44 brain areas where part-of-speech is not hardcoded—a word's dominant grounding modality (visual → noun, motor → verb) routes it. A base vocabulary of 37 words across ~35 grounded sentences trains the system; a scaled run of ~200 words and 100 sentences achieves ≥80% POS accuracy. The Neural Assemblies page has interactive tools to explore the dynamics. CUDA kernels push toward million-neuron-scale steps/second (radix top-k gave a ~19× speedup over the original bitonic sort). This thread is where computational neuroscience meets linguistics meets software you can poke at.
Multi-agent coordination & population-scale intelligence
Single-model capability is not the frontier I care about most. I care about how many models, and the people responsible for them, inherit a shared world: artifacts, precedents, permissions, and fights that do not disappear because everyone stayed polite in chat.
The failure modes have names in our docs, but the lived version is simpler: agents disagree on what is true, one agent's scope leaks into another's, locks never clear, old decisions the thread. ΣIG's threat model maps each failure to halt, checkpoint, degrade, or alert instead of hoping a better prompt fixes a structural mess. The AI Civilizations experiments are the open lab for this. Codrawer is a different substrate, same obsession: make collective work visible enough to argue about.
Foundation-model agents: epistemic & agential impact
I also write critically about what happens when foundation-model agents enter workflows as if they were colleagues. The paper The Epistemic and Agential Impacts of Foundation Model Agents is the formal hook. The lived question is sharper: who gains and loses authority when a system drafts, retrieves, and acts, and which failures show up in the UI versus which ones hide in confident prose?
This thread is not anti-tooling. I use AI coding tools daily and helped accelerate a whole dev team with them at FabuBlox. I do insist that responsibility stay visible: if agents mediate science, governance, or education, I want a trail a stranger can criticize, not a press release about autonomy.
Polis and civics
Democracy and sensemaking run through all of it. The civics page covers who counts as “we,” polis, the Habermas machine paper, and the sites I vent on when the epistemic hygiene fails.
Hardware, bodies, and the physical loop
Several threads touch hardware directly. The MosaicDrone project designs modular self-assembling omnidirectional drones (pogo-pin docking, dynamic mixer reconfiguration, NVIDIA Orin Nano compute), pushing the question of how swarm coordination survives contact with physics. Augmentiv Labs collaboration builds a scientific reproducibility stack using smartglasses to make lab procedures verifiable and repeatable. The portfolio covers earlier hardware: TARS Lab quadcopters, Azure Kinect depth pipelines for VR piano learning, a holographic Cortana installation at the MIT Museum, and OpenGalea (colocated multiplayer MR controlled by EEG). These are where “physical world” stops being metaphorical.
How this connects to the rest of the series
For the rest of the arc: origins is the way in, philosophy the stance, research the assemblies and mech-interp detail, and cosmos the long zoom. If you are working on human–machine teams, assembly-based language, or multi-agent governance with real users, compare notes: start on the homepage, then follow the artifacts.