I'm fascinated by the generative AI wave rolling over us, and wondered if AI could create a language that it might prefer using over the ones created by and for humans.
To create the design, I had AI analyze the ASTs of several mainstream languages plus a few of the conceptually groundbreaking but esoteric ones (listed in the README) and then create a new structure and new syntax. It was named after the Jacquard machine (https://en.wikipedia.org/wiki/Jacquard_machine), a precursor to Babbage's Analytical Engine (and punch cards).
The result reused a lot of existing ideas but combined them in what I found to be an interesting way. External/world effects are visible in function signatures, and the runtime requires explicit permission to touch the filesystem, network, etc. Effect interactions can be recorded and replayed to see what happens under different conditions or code. And since code is given a content-addressed semantic identity internally, renames and formatting changes don't require recompile or retesting.
Another piece that fell out of this was a testing framework called Warp, which combines replay, results caching, handler substitution, and a few other tools that I frankly wish I had when writing Python. There are a few examples available in the demos directory.
There's more to do, but it's installable and usable. I'm hoping people will have their agents digest the docs/SKILL.md file and maybe write a few programs or see where it might fit in their projects. It should be particularly useful in agent systems. If an agent says something is painful or you as a human find the code tough to understand, I'd like to hear about it so I can address it.
Given how poorly LLMs do with writing prompts for LLMs, I'm not sure I'd trust their judgement in designing a language for LLMs.
> and the runtime requires explicit permission to touch the filesystem, network, etc
This feels like more of an OS problem (or library problem) than a language problem.
> Run one program against many worlds. The same code can run against the real network, a scripted fake, a recording of last week's traffic, or a probability model of how servers usually behave
How is the "world" model different from plain dependency injection?
But basically world is a bit more narrow, that moment where your code touches the outside world (logging, http, etc), you can swap that. It's sorta like DI but deliberately narrower, only the moments where code touches the outside world are swappable.. With DI in theory you can replace anything, which has its benefits, but at least personally I am not a big fan of mocks, except when they touch the outside world. So that's what's replaceable.
I love how people create so many new things with AI, but to think how much tokens, and in turn money we all have collectively burned for these side projects is crazy.
it pales in comparison to all the brain cells burned chasing dumb ideas. Don't sweat it, and keep burning tokens in an effort to discover something great.
8 comments
[ 0.21 ms ] story [ 27.6 ms ] threadTo create the design, I had AI analyze the ASTs of several mainstream languages plus a few of the conceptually groundbreaking but esoteric ones (listed in the README) and then create a new structure and new syntax. It was named after the Jacquard machine (https://en.wikipedia.org/wiki/Jacquard_machine), a precursor to Babbage's Analytical Engine (and punch cards).
The result reused a lot of existing ideas but combined them in what I found to be an interesting way. External/world effects are visible in function signatures, and the runtime requires explicit permission to touch the filesystem, network, etc. Effect interactions can be recorded and replayed to see what happens under different conditions or code. And since code is given a content-addressed semantic identity internally, renames and formatting changes don't require recompile or retesting.
Another piece that fell out of this was a testing framework called Warp, which combines replay, results caching, handler substitution, and a few other tools that I frankly wish I had when writing Python. There are a few examples available in the demos directory.
There's more to do, but it's installable and usable. I'm hoping people will have their agents digest the docs/SKILL.md file and maybe write a few programs or see where it might fit in their projects. It should be particularly useful in agent systems. If an agent says something is painful or you as a human find the code tough to understand, I'd like to hear about it so I can address it.
More detail here:
Repository: https://github.com/jbwinters/jacquard-lang
Further intro/human-oriented write-up here: https://research.friendmachine.co/jacquard/
> and the runtime requires explicit permission to touch the filesystem, network, etc
This feels like more of an OS problem (or library problem) than a language problem.
> Run one program against many worlds. The same code can run against the real network, a scripted fake, a recording of last week's traffic, or a probability model of how servers usually behave
How is the "world" model different from plain dependency injection?
But basically world is a bit more narrow, that moment where your code touches the outside world (logging, http, etc), you can swap that. It's sorta like DI but deliberately narrower, only the moments where code touches the outside world are swappable.. With DI in theory you can replace anything, which has its benefits, but at least personally I am not a big fan of mocks, except when they touch the outside world. So that's what's replaceable.