simmisBetter AI agents are only the beginning. We write about organizations that people and agents can operate, program, and improve from within, turning useful work into lasting organizational capability without losing memory or control.
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Lean on the JVM: proved functions for Java and Clojure
Prove properties of a Lean function, call it from a Java or Clojure service, let Lean call back into the application, and deploy a prepared artifact with the Ansatz Lean/JVM SDK.
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Probabilistic programming for Clojure programmers: write the model once, choose the inference later
Probabilistic programming explained as constraint programming with soft constraints, then one forecasting model run under several inference methods with Foerster, an MIT-licensed Clojure library, each result checked against an exact answer.
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Before deploying an AI agent, benchmark the whole workflow
Benchmark AI agent workflows before rollout. See how Dvergr compares prompts, tools, models and checkers, and how Simmis reviews proposed changes.
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Superficie: Clojure you can read without learning Lisp
Why every Clojure example on this site has a second, block-and-infix view, how that view stays exactly the same program, and how libraries teach it to show numerical kernels and proofs.
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Stuttgart in a posterior: a city simulation you can question
A research demonstrator: 936,597 simulated people, shops chosen by size and distance, nine parameters inferred from the city's published retail turnover, and a policy question answered with its uncertainty attached.
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A KV cache you can fork
How pretrained-rstr gives a language model's KV cache an identity, a durable home, and a copy-on-write fork, so exact context can be reused across restarts, workers, and parallel attempts.
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A REPL you can fork
Fork a live SCI interpreter in the browser, compare independent runtime worlds, and see where branching stops at continuations, host objects, and external effects.
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Agents can act. Who keeps the company coherent?
Where agent harnesses, worktrees, and human-in-the-loop controls stop, and why multi-agent work needs shared organizational state.
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Shared context for AI agents: five architectures compared
How Wato, PromptQL, Dust, Buzz, and Simmis use memory, connected data, workspaces, event logs, or versioned organizational state to coordinate AI agents.
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What Bayesian inference does, in pictures
A research foundations guide to how probabilistic models turn evidence into distributions and how governed organizational history could support future learning.
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Copy-on-write branching: why isolated attempts can stay cheap
How structural sharing records only what changed and why that matters when people and agents work in parallel.
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Reading a causal graph: what your model knows
A research foundations guide to how causal graphs represent interventions and how governed organizational history could support future prediction.
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