it begs the question: what's the next programming primitive to be replaced with matrices? for loops? functions? while statements? what does the "language for intelligence" look like?
the distinctive modern pathology is unopposed rhetoric: values statements and PR that pre-empt criticism as they are designed to never face an advocate on the other side.
the jev release is giving react major version drop vibes. just a massive crashing wave, everyone rethinking fundamental stuff and thinking through the implications of a novel new primitive.
Skills are now a valuable form of software, like Emil's world-class design eng work. Projects like 𝚙𝚐𝚋𝚘𝚝, 𝚔𝚗𝚒𝚙, 𝚞𝚗𝚕𝚒𝚐𝚑𝚝𝚑𝚘𝚞𝚜𝚎 create amazing AI engineering loops.
The US military is being run by a group that apes the Russians and not the Ukrainians—when we have in front of us one of the best statements of command principles I have ever seen from the latter.
But, simply just having your loops build everything without having some guardrails around the blast radius without having guardrails around how you think about quality, I think is a recipe for disaster.
The world is bad, but better. The vast majority of westerners believe the world is poorer, more violent, deadly, primitive, and awful than it actually is. This book beautifully illustrates our ignorance, updates your knowledge, and gives you tools for a fact-based worldview.
So if you design call it loop, call it graph, call it workflow, it's kind of all the same. If you design something that gets a trigger or gets an input and does something for you and maybe there's a decision in the way, boom, there's your graph.
And I think there's a lot of room in a lot of domains for much faster validation models, possibly learned valu validation models that can uh you know get you a a approximation to the true answer much much more rapidly. And that changes how those experimental loops can be thought of and how quickly you can go around those loops.
if you want to do loops engineering, you should build one loop at a time and you should keep them small and contained. Basically, I think everything except stop reading the code is really good advice.
You'll notice what I said was not use loops to ship the features that users want. We use loops to actually improve the codebase quality and we read all the code because we care about how it's architected and we care not just about the system architecture but what I would call the program design
So I think the the thing I'm most excited is actually like what we call like iterated loops or like slow loops where we basically have a cron job. We have the loop the the the structure of the loop is really easy. It's like run this llinter fix one thing commit and push and then we run that every night in our GitHub actions and we wake up every morning to one PR that makes the codebase a little bit better.
I think a lot of the things that people might say that like oh, it was a waste of time to learn this subject cuz I didn't actually use like those details on the job. I think that's a very wrong way to think about it. And I think that's what a lot of people are doing now with AI. Like hey, what what if I'm not going to be writing for a loops a couple years from now. Um I don't think those things are a waste of time.
although students' personal statements seem more creative because they use more varied words, they actually feature less original ideas. AI writing produces an illusion of creativity.
we went from infrastructure is code to infrastructure is data and infrastructure is code is like if this do that um bring in this module for loops all this stuff and Kubernetes is like no no no you have to specify exactly the containers you want how much memory that they need and then we have the status field to tell you if they were running or not
as leaders we get so focused on value statement mission statements we forget the mission statement is not the mission. The map is not the territory. Mission is an emergent property of the living superorganism of the thing we're birthing.
My model is that research requires a mix of skills. The day-to-day coding and execution is crucial. But there's also a set of harder-to-learn conceptual skills, collectively called research taste. These skills take a long time to gain because they have poor feedback loops, but they take very little time to use.
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