I insist on the text generator nature of the thing. It’s just that we built harnesses to activate on certain sequences of text.

Think of it as three people in a room. One (the director), says: you, with the red shirt, you are now a plane copilot. You, with the blue shirt, you are now the captain. You are about to take off from New York to Honolulu. Action.

Red: Fuel checked, captain. Want me to start the engines?

Blue: yes please, let’s follow the procedure. Engines at 80%.

Red: I’m executing: raise the levers to 80%

Director: levers raised.

Red: I’m executing: read engine stats meters.

Director: Stats read engine ok, thrust ok, accelerating to V0.

Now pretend the director, when heard “I’m executing: raise the levers to 80%”, instead of roleplaying, she actually issue a command to raise the engine levers of a plane to 80%. When she hears “I’m executing: read engine stats”, she actually get data from the plane and provide to the actor.

See how text generation for a role play can actually be used to act on the world?

In this mind experiment, the human is the blue shirt, Opus 4-6 is the red and Claude code is the director.

The whole “chat with an AI” paradigm is the culprit here. Priming people to think they are actually having a conversation with something that has a mind model.

It’s just a text generator that generates plausible text for this role play. But the chat paradigm is pretty useful in helping the human. It’s like chat is a natural I/O interface for us.

Working on TRIPA, a internal pack format to save huge amount of small images to cloud storage without dying from the write costs.

Im planning a change that will save 20k a month of storage.

I absolutely could come up with the details and implementation by myself, but that would certainly take a lot of back and forth, probably a month or two.

I’m an api user of Claude code, burning through 2k a month. I just this evening planned the whole thing with its help and actually had to stop it from implementing it already. Will do that tomorrow. Probably in one hour or two, with better code than I could ever write alone myself.

Having that level of intelligence at that price is just bollocks. I’m running out of problems to solve. It’s been six months.

In 2020 I became a full time Java developer, coming from a infrastructure role where I kind of dealt with Java code, but always as artifacts I managed in application servers and whatnot.

So when I first started dealing with the actual code, it scared me that the standard json library was basically in maintenance mode for some years back then. The standard unit test framework and lot of other key pieces too.

I interpreted that as “Java is dying”. But 6 years later I understand: they were are feature complete. And fast as hell, and god knows how many corner cases covered. They were in problem-solved, 1-in-a-billion-edge-cases-covered feature complete state.

Not abandoned or neglected, patches are incorpored in days or hours. Just… stable.

All is quiet now, they are used by millions, but remain stable. Not perfect, but their defects dependable by many. Their known bugs now features.

But it seems that no one truly want that. We want the shiny things. We wrote the same frameworks in Java, then python the go then node the JavaScript the typescript.

There must be something inherently human about changing and rewriting things.

There is indeed change in the Java ecosystem, but people just choose another name and move on. JUnit, the battle tested unit testing framework, had a lot to learn from new ways of doing, like pytest. Instead of perturbing the stableness, they just choose another name, JUnit5 and moved on.