Thoughts, experiments, and critical analysis on artificial intelligence, cognitive architectures, and the technologies shaping our future. Where deep thinking meets deeper code.
Simplex v0.5.0 is now open source under MIT license. Introducing the Anima—a cognitive soul for AI agents with memory, beliefs, and intentions—and revolutionary per-hive SLM provisioning where 10 specialists share a single 4GB model instead of loading 10 copies.
Read AnnouncementSimplex 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.
