One of my colleagues quipped recently that instruction tuning might not be about teaching large language models (LLMs) what to do, but rather about reminding LLMs of the wealth of wisdom they already have at their foundation. Here’s a rather unkempt vignette on what wisdom I would like an LLM to recall. I would like … Continue reading “Models and structured reasoning”
The whole large language model space is brand new, and there are lots of folks trying to make sense of it. If you’re one of those folks, here’s an analogy that might come handy. Any gasoline-powered car has an engine. This engine is typically something we refer to as a “V8” or “an inline 4” … Continue reading “The engine and the car”
To make the asymptote and value niches framing a bit more concrete, let’s apply it to the most fun (at least for me) emergent new area of developer experience: the various developer tools and services that are cropping up around large language models (LLMs). As the first step, let’s orient. The layer above us is … Continue reading “AI Developer Experience Asymptotes”
I’ve been thinking about the boundaries of what’s possible when applying large language models (LLMs) to various domains. Here’s a framing that might not be fully cooked, but probably worth sharing. Help me make it better. To set things up, I will use the limits lens from the problem understanding framework. Roughly, I identify three … Continue reading “Limits of applying AI”
This story begins with the introduction of metacognition to large language models (LLMs). In the LLM days of yore (like a few months ago), we just saw them as things we could ask questions and get answers back. It was exciting. People wrote think pieces about the future of AI and all that jazz. But … Continue reading “Reasoning boxes”
I’ve been spending most of my hobby time playing with reasoning boxes and it’s been crazy fun. I need to write properly about my explorations later, but so far, I’ve convinced an LLM to brainstorm ideas and then critique itself in a diverge-converge exercise, evaluate Cynefin space of a situation, and even get some Humean … Continue reading “Schemish”
In my last post, I kept talking about AI patterns, but kept it a bit vague. I thought it might be useful to share a couple of examples to describe what I mean by “AI patterns” a bit more clearly. Once again, put your technical hats on. 🗺️ How to read AI pattern diagrams As … Continue reading “AI Patterns and Breadboard”
Here’s a concrete example of a reasoning box that I’ve been talking about last week. It’s not super-flashy or cool – certainly not one of those viral demos, but it’s a useful example of recursively ground-truthing a reasoning box onto itself. The source code is here if you want to run it yourself. The initial … Continue reading “Concept miner”
This one is also a bit on the more technical side. It’s also reflective of where most of my thinking is these days. If you enjoy geeking out on syntaxes and grammars of opinionated Javascript APIs, this will be a fun adventure – and an invitation. In this essay, I’ll describe the general approach I … Continue reading “Placing and wiring nodes in Breadboard”
