Software is mostly all you need

In other words, a higher-level JIT compiler, meaning it still dynamically generates code based on runtime observations, but the code is in a higher-level language than assembly, and the observations are of a higher-level context than just runtime data types.

> Code is the policy, deployment is the episode, and the bug report is the reward signal

This is a great quote. I think it makes a ton of sense to view a sufficiently-cheap-and-automated agentic SWE system as a machine learning system rather than traditional coding.

* Perhaps the key to transparent/interpretable ML is to just replace the ML model with AI-coded traditional software and decision trees. This way it's still fully autonomously trained but you can easily look at the code to see what is going on.

* I also wonder whether you can use fully-automated agentic SWE/data science in adversarial use-cases where you traditionally have to use ML, such as online moderation. You could set a clear goal to cut down on any undesired content while minimizing false-positives, and the agent would be able to create a self-updating implementation that dynamically responds to adversarial changes. I'm most familiar with video game anti-cheat where I think something like this is very likely possible.

* Perhaps you can use a fully-automated SWE loop, constrained in some way, to develop game enemies and AI opponents which currently requires gruesome amounts of manual work to implement. Those are typically too complex to tackle using traditional ML and you can't naively use RL because the enemies are supposed to be immersive rather than being the best at playing the game by gaming the mechanics. Maybe with a player controller SDK and enough instructions (and live player feedback?), you can get an agent to make a programmatic game AI for you and automatically refine it to be better.

I think it makes better decisions than me provided I give it enough high-level direction and context.

Sometimes I give it __too much__ direction and it finds the solution I had in mind but not the best.

I'm not into it enough that I'm formally running different personas against each other in a co-operative system but I kind of informally do that.

I think that's also true of people but we are kinder to each other and ourselves when judgement is bad.

How many times have you been in a conversation where you asked the wrong question or stated the wrong thing because you either weren't 100% listening (no one is), or you forgot, or you didn't connect the same dots that others did?

Neural nets have been better at classifying handwriting (MNIST) than the best humans for a long time. This is what the author means by judgement.

They are super-human in their ability to classify.