The Wisdom Wall

“Technical moats are not real moats. Like, I, I, there's no portion of basis's long-term terminal value that stems from some, you know, secret RL trick we found that nobody else found.”

“not thinking that an agent operating over 10 hours is a black box. It's not. It has a lot, a lot, a lot of data, and you're probably doing a disservice to your customers if you don't understand, like, how it's going about the work.”

“a lot of engineers, they, they, they treat the, the, the code as more precious than the English, when actually the English is more precious because the English affects the performance. The code does not affect the performance, right? If the logic, assuming the logic's the same, it does not affect the performance.”

“Speaking is just so much faster than writing things down, and in fact, when you try to write things down, you are Actually, essentially, uh, trying to summarize all the crazy thoughts in your head, and so that's why it takes a lot of time. And it's useful for your eye, because it's rude if somebody just blabbered and…”

“And then I think after a one, it was oh three, because I think oh three helped prove that not only could you scale the amount of reasoning at inference time, but with better training, with more compute, better data, et cetera, in the post-training phase, you could make the reasoning higher quality, more efficient, and,…”

“At the end of the day, coding is subjective. It's an art, uh, and you're not going to solve an art through verifiable rewards.”

“humans are already used to working with non-deterministic systems. It's just the systems are normally their coworkers, not their computers. And in many ways, like companies and processes, it's all about how do you design a system for non-deterministic entities, i.e. like humans, to coordinate together to solve a…”

“I think the framework I like is thinking about it as training data. Except you're just training the model at runtime. It is training data. And because you, the model's learning at inference time, the total amount of training data is far lower, right? Like the whole amount of context in your system that the agent would…”

“as models are getting cheaper and cheaper, more and more of that actually can just be done, um, using inference instead of using determined, like things like graphs or things like embeddings.”

“I, um, I think law actually is kind of like that. You know, in many ways I think about, I think maybe the founding fathers would have been really good, um, context engineers or agent managers, because you had to, you had to write, you know, a piece of English that was going to be, you know, interpreted at runtime.…”

“accounting is difficult. Um, it's not something that is just purely text in text out. And so it requires the ability for like AIs to be able to, you know, perform lots of actions over long periods of time and actually, um, you know, be coherent over that period of time to get to outcomes that are, that are good.”

“And accounting is actually the art of compressing all of that into something that is structured that now people can look at and understand and make decisions. So something about accounting you could argue in a meta way is kind of like an intelligence over the economy, um, uh, because it is really a compression activity…”

“And you can like optimize, not just for getting the work done, but for making it easy for your reviewer to understand the decisions that you made. Uh, and that's obviously very true in software engineering, and it's actually true in, I think, most professions, uh, and especially accounting, which we can kind of get…”

“once you start getting to longer horizons where you're beyond the context window or you're getting to context rot, you need to kind of use in sort of brute force your reasoning to, um, build out your environment. Um, whether that be with sub agents or compaction, we talk more about that, but That's, I think, where the…”

“as people who are good at building agents know, like it's much better at the margins to be able to give principles and the whys and more context and let them figure it out.”

“So there's some activation state that by definition is going to be biased to that current trajectory. And so maybe for review, you want an uncorrelated trajectory, right? Where it's like a new box, uh, and it's just a smart.”

“If you build these kind of LLM intuitions and you combine them with, you know, maybe basic principles of, like, organizational design and management, um, I think you start to get to maybe what is, like, the frontier of agent building.”

“a lot of the people with the best agent intuition, actually, yes, a lot of people come from ML backgrounds, but people who don't, a lot of them are ones who are just really good at automating their own work.”

“it is actually both a spec and a rubric. Uh, we call this specs and there were some people who asked, isn't this a rubric? And it is, it's both. Um, the reason it's both is because it is not just used to grade or potentially reward the agent. It's also used to align the humans. I think that is an underrated point, um,…”

“The ontology of your code base, it always mattered for engineers. It matters just as much, if not more, for, for really good agents over time.”

“And that's why I think, you know, for, for true agent native companies, um, especially in the future today, I think it's still quite early, but especially in the future, having a clear understanding of what your company canon is, um, and organizing that in an ontology that, uh, makes sense and ensuring that that is…”

“I think one thing, one skill that really matters is, uh, good systems thinking. And where does good systems thinking come from? Um, it comes from people who have had to, uh, think about some abstraction, some system, something, and design it in such a way that it performs in a plethora of situations.”