Thoughts, experiments, and critical analysis on artificial intelligence, cognitive architectures, and the technologies shaping our future. Where deep thinking meets deeper code.
Text data is running out, so AI is turning to the physical world. The industry's answer: record everything, ship it to the cloud, train bigger models. We think that's the wrong bet. The real opportunity is understanding a machine while it runs—and that is what Senua AI builds.
Read the full storyRobots can act but cannot feel. The sensors are cheap and proven; the gap is the reader. Why touch is a change-stream problem, and why causal-state cognition on a sixty dollar computer is the right layer to read it.
The industry has concluded the physical world is the next dataset. The premise is right; the bet is wrong. Why capture-and-train repeats the text-era mistake at higher stakes—and what the mathematics of causal states says instead.
The energy budget 2ν∫Ω dt ≤ E(0) combined with cascade locality proves global regularity. A finite perturbation of a stable equilibrium must decay.
Angular relaxation scales as K². Vortex stretching scales as K. The integer lattice of Z³ provides enough mixing channels to overwhelm stretching at every shell. A proof of global regularity.
Apply the same amplitude method to six arithmetic functions using two million zeta zeros. A classification into three growth regimes emerges — and connects back to Navier-Stokes regularity through the same spectral convergence criterion.
Liquid neural networks, the sieve, the conduit, the scaffold, and how the boundary between code and model dissolves when everything is dual32. The system adapts continuously. The scaffold watches.
One data type — dual32. Native machine code. Register pairs. 1.6x faster than optimised C, not because the code is better, but because Simplex knows what a dual32 is. C doesn't.
The same scaffold array that diagnosed fluid dynamics solvers also diagnoses neural network learning failures. One tool, five domains, same structural finding: the cascade exponent is always negative.
How a multi-perspective diagnostic framework revealed a hidden energy conservation bug in a fluid dynamics solver — and what the corrected picture shows about one of the Clay Millennium Prize Problems.
Simplex v0.14.0 introduces a hybrid classical-quantum bridge. Cost-aware QPU dispatch to Amazon Braket, IBM Quantum, and Azure. Variational algorithms powered by dual numbers. Quantum uncertainty as epistemic beliefs.
Every outstanding task closed. 162/162 tests passing. Complex numbers, matrix algebra with quantum gate constructors, complete dual numbers, HTTP client, JSON parser, SLM native bindings, and five critical compiler fixes.
From module system to 154/154 tests passing. Cross-module imports, neural gate codegen, f64 type-aware arithmetic, forward-mode AD, contract verification, and a 19,589-line runtime.
Introducing Self-Learning Annealing and Edge Hive. Hyperparameters that tune themselves through meta-gradients using dual numbers, and local-first AI that runs entirely on-device.
Announcing Neural Gates and Real-Time Learning. A fundamental shift where code logic learns from data, models adapt during runtime, and systems get smarter the longer they run. No retraining required.
A complete guide to training domain-specific specialists for Simplex. Infrastructure selection, AWS provisioning, LoRA fine-tuning scripts, and the roadmap from Python to pure Simplex.
Google's RLM handles 10M+ token contexts through recursive decomposition. It's clever engineering—and a solution to a problem Simplex solved through cognitive architecture, not context workarounds.
An honest assessment of where Simplex stands today and the roadmap to Neural IR and Neural Gates. What we have, what's missing, and what comes next.
Simplex v0.5.0 is now open source under MIT. Introducing the Anima—a cognitive soul for AI agents—and revolutionary per-hive SLM provisioning where specialists share a single language model.
A comprehensive technical deep dive into the Simplex toolchain. Compiler internals, bytecode format, VM architecture, language specification, actors, CHAI specialists, and Mnemonic extensions with code examples.
An executive overview of the Simplex programming language toolchain. How a self-hosted compiler, package manager, documentation generator, virtual machine, and language server work together.
A comprehensive survey of the AI agent tooling landscape in 2026. Comparing Simplex against Dana, Mojo, Letta, Akka, and the Python framework ecosystem.
A critique of the agentic AI paradigm — backed by Stanford/Harvard research, Gartner predictions, and expert analysis from Gary Marcus and Yann LeCun.
Formal operational semantics for Simplex cognitive constructs — how memory, beliefs, actors, and agents execute with mathematical precision.
How emergence happens in Mnemonic Hives — stigmergic coordination, swarm intelligence, and the conditions under which agent collectives develop genuine collective intelligence.
How Mnemonic Hives learn from every interaction through knowledge distillation, LoRA fine-tuning, and intelligent compression — without catastrophic forgetting.
A deep dive into the epistemic architecture for Mnemonic Hives — how to design agents that track confidence, revise beliefs rationally, and distinguish facts from opinions.
A response to Dries Buytaert's investment thesis — challenging the notion that orchestration platforms are the future of AI coordination.
How we're extending Simplex with first-class support for beliefs, goals, intentions, and autonomous behavior — the technical foundation for true AI agents.
An introduction to cognitive architecture for AI systems — why LLMs need more than context windows, and how to build agents that actually remember.
The theory behind Cognitive Hive AI and the early iterations of Simplex — why swarms of specialists will outperform monolithic models.
