genie

I’ve been working on Rye since 2018. It’s a project of joy — but also because I believe there is a potential to create something of value to others, eventually.

Even people living under a rock know we’ve entered the age of LLMs. I don’t jump to ships too soon, but eventually, even I had to admit: code can get generated from prompts. And in many situations — with a smart prompter — the results are quite OK.

Even if you disagree, genie can’t be put back in the bottle. Technical progress generally only moves forward.

buzz

Trying to persuade programmers to switch to a language you made up is very ambitious (or just mad) on its own. When all the buzz is about not needing to use any programming language at all, it makes even less sense.

The falling tide lowers all boats — and my is not even in the water yet.

killing

LLMs currently need programming languages, they can’t just generate executables directly from your prompt. Absurdly they also need examples, tutorials, blogposts, Stack Overflow answers, which they are now well, killing.

LLMs are on the level of transpiler languages (Coffeescript?). They need the host languages — but if really succsessful, they would end up killing the host.

If no one uses Python anymore, will anyone still work on Python? (Just an example.)

Probably, in the long term, LLMs* will have/generate their own runtimes, libraries, and everything else. (* Or what comes next.)

specific

So the real question — does it make sense to keep developing Rye? — boils down to:

Is natural language the best substrate to declare what you want from a computer?

Traditionally, programmers believed it isn’t. But maybe it’s a matter of specificity. Yes, natural language can be used to declare any solution — but it’s not ideal for declaring a specific one.

“Make me a Flutter app where I can jot down recipes and rate them” vs.

Specific storage. Specific structures. Specific behavior. Specific interface…

Specialized languages—with consistent, exact, and repeatable design—will beat natural language at that. Like any specialized tool beats a general one at the task it’s built for.

thoughts

Languages are not just tools for communication. They are also tools for thinking.

Medical jargon helps doctors communicate, store information — and also think about medical issues. SQL helps us think about data and its relationships. Functional languages help us solve problems that compose better and cleaner (with less state).

These aren’t just syntax choices — they’re cognitive frameworks.

If we lose tools for precise thought, we may lose the ability to have precise thoughts altogether.

yesterday’s

LLMs are trained on yesterday’s solutions. They are good at recombining existing patterns, even in novel ways, but they are fundamentally backward-looking.

If we stop developing better ways of expressing computational ideas — is this it? Does progress stop here?

monkeys

This also leads to a question (inspired by certain monkeys on typewriters) I asked LLMs a few times:

If you leave 100 LLMs chatting for 100 years, will they come up with any original thought?

Or would they simply recombine existing knowledge in increasingly sophisticated ways — trapped in an echo chamber of human-generated training data?

Ask your favorite LLM.

And yes — I still think it makes sense to build a language. Maybe more than ever.