Domain experts consistently underestimate how quickly AI can master their field and surpass them, because they mistake human difficulties for universal difficulty.
Germany is in trouble. But the trouble goes deep and cannot be addressed by "more of the same" socio-economic measures in the domain of taxes or social spending or regulation.
At a fundamental level, they’re just hostile to the idea of expertise in any domain, in pretty much every agency where there’s something to know, where there’s technical stuff that you need to know to make good decisions. The people who actually know things, or the people who are willing to speak up about what they know, have been silenced, purged.
Second, I think ML is a very shallow domain relative to math. So I think in math there's much more of a you find some true deep abstraction um and then like that like if you really understand that thing which is hard to understand then you get somewhere
First, Refactoring, Domain-Driven Design, and the Pragmatic Programmer are all classics for a reason. Amazing books, and mandatory reads. Working Effectively with Legacy Code deserves all its praise, and of course everyone (yes, really) should check out the Mythical Man-Month.
First, Refactoring, Domain-Driven Design, and the Pragmatic Programmer are all classics for a reason. Amazing books, and mandatory reads. Working Effectively with Legacy Code deserves all its praise, and of course everyone (yes, really) should check out the Mythical Man-Month.
Multi-Paradigm Design for C++ by James O. Coplien. Twenty years after reading this, the core techniques are still with me. These are commonality analysis, where the purpose is to identify families of systems, and variability analysis, which focuses on capturing the domain parameters that vary. In essence: the foundation of great software design.
Coding isn't yet another application domain -- it's the meta-skill required for AI to automatically develop its own training material, via symbolic world models. That's how the RSI loop actually kicks off.
Um in our domain, to answer your last question, because we had to do so much stuff around security and partners and money movement and infrastructure and reliability and you know, all the things. We just we didn't feel like we could scale a really good self-serve experience without getting a lot of the kind of the preconditions um and the infrastructure in place.
I mean I think like if you look at uh my colleagues work on say alpha fold that was a very specific model for uh protein folding and it was highly successful um and was able to really handle that domain quite well so that all of a sudden you now have this amazing tool and model that can give you answers to questions about proteins and their structure um really effectively um but it's not a general model it's a very specific one and there are other I domains where that kind of approach can work really well. Uh maybe in material science or chip design or things like that that uh will enable you to leverage the capabilities of a very accurate but but niche model uh to do things that are hard today.
when you start talking about like most interesting domain problems, you have to pull in so much context that basically even writing what the function is supposed to do becomes a nightmare. The imperative program you write that will get correct 99% of the time is probably good enough to use in almost all cases.
So, we are hiring more people who can look across all the business domains and abstract that to here's the building blocks we're going to need in a world with AI.
we're accumulating code faster than we're accumulating trust and that sense of trust comes from me struggling to understand some domain concept I get it. I represent it in the code. I have I write tests that demonstrate that I really did understand it. And now I trust my program.
I think like in startups the term is agency. Like somebody who's high agency who's just going to get things done, who's never going to like say no to something. I think like that attitude is really important of like okay, if I don't know something, I'll just learn it.
the ability for general purpose computing to continue to scale has largely run its course and the only the the not the only way but the the way to do that is through domain specific acceleration
So I think for me I take a kind of uh OCD enjoyment in the in the craft. And I need to let go of that. Because that that satisfaction of getting this one function just right just doesn't make a difference anymore.
And so you're kind of like you're either on rails and you're part of the super intelligence circuits or you're not on rails and you're outside of the verifiable domains and suddenly everything kind of just like meanders.
The bitter lesson is don't try to be smarter than the AI, okay? You think that you've got special knowledge. The humans bring special domain knowledge to this problem and we're going to teach it something to AI and it will be smarter. What we found was bigger is smarter. Always.
If you get really good at coding that means you have to be really good at general purpose problem solving. So that's a skill, right? And that just maps into other domains.
My sense is that this hybrid is fairly common in practice; solvers aren't magical and if you can deduce additional structure using domain-specific analysis, it will often give the solver an important boost.
I saw some of the highest performers just being people that had very high agency, had that clock speed, had that energy. Um, yes, they they cared about the mission, but they didn't necessarily need to have deep experience on that matter. And in fact, sometimes that experience could be a crutch in certain ways. Especially in this world where the grounds are shifting so fast with AI, a lot of your like learn habits actually need to be intentionally discarded. You know, you need to have a beginner's mind on this type of stuff.
No matter how much I know about a domain, the other person will always know far more about their own situation, context, beliefs, skills, preferences, etc than I do.
But really the software engineering agents I think can be done faster sooner than any other agent because it is a verifiable domain. You can always unit test or compile, and there's many different regions of it can inspect the whole code base at once, which no engineer really can.
Criticism is what happens when someone with domain knowledge who is not trying to earn status by hurting you is able to help you see the world as it is. And those three pieces are the key, right? Because most of the criticism I've gotten in my life has come from people who felt like they would feel better if I felt worse.
in quote-unquote real life, right? Which is starting businesses or you know, writing books, right? Or composing music or playing basketball or playing poker, right? Or basically doing anything interesting we're in a probabilistic domain, right?
the reality is the kids that make new things work from scratch. It actually turns out that they actually have been deep in the domain for a long time. Almost every case, they've been thinking hard about the problem that they're trying to solve actually for in in a lot of cases for many years.
I register all of my domain names through Hover. I think it's a little more expensive than some of the other options, but the UI is simple and reliable and not loaded with dark patterns like some of the cheaper competitors.
Partly because of another book that nearly made it onto my list: Domain-Specific Languages by Martin Fowler. It talks about the idea of writing a small language inside another language, to express ideas in a specific domain
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