I notice that language models are much better at programming than at writing. This is largely because, in programming, they have a closed feedback loop that allows them to find errors. Writing is much more complicated because the feedback is much more diffuse and delayed over time. In fact, it is often not well-defined.
Any conversation
there's no fundamental physical limit that prevents silicon from developing taste and judgement, but language is a lossy compression of reality.
taste comes from experience, from the shadowy mass of episodic memory, which swirls and convects at the boundary of consciousness. the ideas recombine into new ones in the nuclear reactor of the hippocampus.
but no matter how imbued with meaning and expertise is a handmade wardrobe we still get our shit from ikea
signals. thinking about shifting my mental framework to identify signals instead of waiting for concepts to be assembled. exploring the amount of energy and opportunities for distractions through the operations of processing what just happened and what is happening to short circuit latent and unnecessary systems. cut through the assembly processes and identify the raw signals. recognizing that naming these "raw signals" is misleading. maybe, shifting observability lower in the stack.
๐ค I built agent-top - an open-source htop for AI agents.
github.com/kannandreams/agent-top
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