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An agent finishes a task and forgets almost everything. Dreaming is an offline pass that replays the scattered notes an agent saved, distils them into durable lessons, and folds those lessons back into its prompt — so it wakes up knowing what it learned yesterday. Self-improvement with no retraining.
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This is a collection of my thoughts, ideas, and learnings.
I have tried blog formats many times before, but it varies a lot whether I have 2 lines to share or 100
and the burden of long form always kills any initiative for me.
So I am trying to invent a new format for myself.
Why does next-token prediction learn language so well, yet stall on dynamical systems? An explanation rooted in the smoothness of the map from syntax to meaning — and what it implies about where each domain earns its competence: pre-training, reinforcement learning, or search.
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A programming language for dynamical systems — a LEAN-style parser, a rewrite engine, a circuit compiler, and a JAX runtime. The mathematical structure stays explicit from equation to execution, enabling formal composition, algebraic rewriting, and interchangeable semantics.
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Most agent frameworks treat LLMs as agents. Hugin treats them as oracles — one component in a larger reasoning system. Built around an immutable stack architecture that makes branching, debugging, and multi-agent coordination natural.
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This is part four in a series on how to use a state machine framework to model agentic flows and how this approach enables some interesting features. In this part we explore advanced techniques for improving multi-agent reasoning.
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In the last two parts we described the state machine framework, how it works and how we can steer agents within it. In this third part, we will describe several other desirable extensions of any agent framework and how we can implement them with our state machine setup.
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In the first part we described the state machine framework and how it works. In this part we will dig into some of the techniques for steering agents and how we implement them in the state machine framework.
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Since I last wrote a few thoughts down on agents the buzz has only increased. With our release of Bigwig I thought it would also be interesting to dig into some of the details of how we have gone about implementing our system of multi-agents.
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Recently Yoav Goldberg asked a series of interesting question on Bluesky(1, 2, 3).
I think this is one of the more interesting and peculiar operators in matrix algebra, so I wanted to write a little bit about it.
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I have recently been thinking about modelling of dynamical systems again - so wanted to revisit some of my old ideas.
At the end of October, I gave a quick demo at AI Tinkerers Paris about one of our early, early version of BigWig. This is a short writeup based on the slides, called "Agents for building ML models".
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