jbwinters

Ah, that’s a tough one. My hope is that Jacquard, or some similar language, can make intent explicit through readable, high-level constructs, giving both the human reviewer and the model a clearer target while the details are filled in. But it still can’t tell us whether the original intent was right.

Coincidentally, you reminded me of one of my favorite Charles Babbage quotes:

> “On two occasions I have been asked, ‘Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?’ I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question.”

I suppose catching that kind of mistake is how humans prove to our future AI overlords that we should be kept around.

This is definitely true. The language is intentionally small enough to fit into a single SKILL.md file, for what it's worth:

https://github.com/jbwinters/jacquard-lang/blob/main/docs/SK...

Agents I've tested with have had been able to pick up the language from that, at least to the extent that I've been able to test so far.

You're right, an LLM doesn't have preferences. As shorthand, I thought it a useful concept though, as they do have particular ways of writing that can be tuned for.

The effect system here is part of a type system (technically a type-and-effect system). Where a value type typically describes what value a computation returns, an effect describes which operations it can perform during evaluation.

The difference is that effects propagate through the call graph, so lower-level code cannot access disallowed resources without that authority appearing higher up. At runtime, world effects also require an explicit grant. In a typical value type system, lower-level code can introduce side effects without them appearing in the caller’s type.

It's a good name regardless! Don't stop on my account.

Thanks for the pointer to Jai! I will check it out.

Yes, Jacquard uses content-addressed definitions and it should be possible to set up a process for 'review again if this changes' on top of it. Warp, the testing framework, already uses this to avoid rerunning pure tests when neither the definition or dependencies have changed.

Jacquard does not currently implement proof or strictness levels, but binding those to a definition’s content identity is interesting and definitely worth exploring.

What are you building that people keep comparing to Ada?

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.

More detail here:

Repository: https://github.com/jbwinters/jacquard-lang

Further intro/human-oriented write-up here: https://research.friendmachine.co/jacquard/

I dug into the Stargate data center Michigan just approved. 1.4 gigawatts of load, roughly 11% of the state's entire retail electricity consumption. It's been controversial locally but most of the debate has been about farmland and water. I think the more interesting question is what this kind of infrastructure buildout means for the region's industrial capacity. The same grid upgrades, trades workers, and construction pipelines you need for data centers are what you need to build anything else heavy.