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Show HN: Jacquard, a programming language for AI-written, human-reviewed code

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Re: Show HN: Jacquard, a programming language for AI-written, human-reviewed code

#41
post #4

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 las…

> How is the "world" model different from plain dependency injection? In addition to what the other comment said, this "world" model is great for hermetic testing of complex code, LLM written or not. We've seen existing projects that intercept the OS level syscall for testing, replayability, etc. Building it into the language runtime, hopefully with better ergonomics from the start than a syscall, would be a welcome…

Dependency injection provides the same hermetic-testing capability, so that's not really an answer to OP's question. Effect-as-world-model is, in this specific way, just a special argument slot that only accepts dependency-injected functions.

What effects provide beyond DI is entirely in their ability to abort (resume zero times) or (in the case of multi-shot effects) resume multiple times. An effect that resumes exactly once is structurally the same as a dependency injected lambda.

Re: Show HN: Jacquard, a programming language for AI-written, human-reviewed code

#42
I don't understand the code in the examples after simply reading it. That's a problem. You need to have comments to clarify or pick a better example of code.

You are in competition with Python and Javascript which has lots of code for training data. I wouldn't touch esoteric features unless it improves readability or is under the hood. The bigger problem with esoteric features is that its hard for humans in general to understand unless you cover it with enough syntactic sugar for people to write it down.

Re: Show HN: Jacquard, a programming language for AI-written, human-reviewed code

#43

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.

Not just money. A lot of CO2 was dumped into our atmosphere and tons of clean water were evaporated as well.

Doesn't stop people from mass-producing and consuming beef lol, people just don't care aside from performative lip-service.

Re: Show HN: Jacquard, a programming language for AI-written, human-reviewed code

#44
Very cool, this is the first language I've seen that has effects/permissions annotated.

When reasoning about a bit of code, types and effects are two things that help me grok it. However, there are a lot more. Like: how much memory can this use, does it process any PII, how quickly should it respond, which users are allowed to run it. Our "type" system could then alert us when we call a function that takes 20 seconds straight from a button without a loading spinner.

I'm very curious to see whether a language with many annotations this could work. Probably a language stored in a database approach like Unison is needed.

Other than that, I'm also curious to know if Jacquard works well with LLMs in practice. How well does providing the language docs into the prompt actually work? The models are trained on mainstream languages so I've been doubtful whether new languages will be able to compete.

Re: Show HN: Jacquard, a programming language for AI-written, human-reviewed code

#45

Earlier quoted context omitted.

I don't really have a dog in this race, but "3 hours of streaming" is several orders of magnitude off using a frontier model to write a project like this. Like, 3000 hours of streaming would be closer. Then you're talking about one person consuming the resources for a useless project they'll abandon after a week vs. the resources consumed providing years of entertainment.

How did you get 3000? That's a wildly inaccurate and out-of-this-world figure. I did some preliminary research recently that I'm not fully recalling (would have to look for it), but I had a baseline of something like 9 minutes of streaming video using the equivalent resource (electricity, etc) of just over 5 average prompts. So 3 hours of streaming video is a bit over 1000 prompts. I can get an OK tool that still goe…

Streaming is not computationally expensive at all. The computing and networking cost is so trivial it's difficult to measure, meaning most of the cost is in the cost of electricity your TV, computer monitor, or phone consumes with the display on. Some of this cost may even be rendered redundant if you were leaving the display on anyways; I certainly don't turn my PC monitor off when I'm done watching a video, even if I'm letting it idle for a while. At any rate, the typical device will be in the range of 0.1kwh per hour of streaming. Perhaps substantially less if most people watch on their phones these days.

"Average prompts" is a meaningless measurement. An 8x B200 GPU node, which is capable of running Deepseek V4-Pro, consumes in the ballpark of 300kwh per day while producing over 300 million tokens per day when operating at production loads, so we can get a roughly clean estimate of 1kwh per million tokens from a near-frontier open model. The project in the OP, which we'll take to be "respectably-sized", is 100k loc. How many tokens it takes to arrive at 100k loc is the most hand-wavy, difficult part to quantify here, but looking at my own usage, I've spent about 800 million tokens in the past two weeks on a 25k loc side project[1], and that was with full human-in-the-loop management, single agent usage. If we extrapolate that to 100k loc, we're looking at ~3.2 billion tokens, around 320kwh. Note this is for Deepseek, which is much cheaper to run than the frontier models. Inference on Fable or GPT 5.6 is likely another order of magnitude higher power cost, but those numbers are not disclosed, and we can only make rough inferences from their API token pricing (which also includes undisclosed amount of profit margin).

Anyways, 320kwh for a 100k loc project vs. 0.1kwh per hour of streaming comes out to my ballpark estimate of 3000 hours, but that was at Deepseek rates. It might actually be closer to 30,000 hours of streaming if this was done on Fable.

Note that the Bun project you cited burned $165,000 in tokens at their API rates. Perhaps you can look up the price of electricity in your region and do the math for yourself on how many hours having your TV on that would buy you. I'm estimating around 10 million hours, which sounds in line with my previous math: it's 1m loc written with Fable, so 10x the 30k number for 100k loc, with another magnitude for wasting tokens on swarms of agents checking each other and consuming many more tokens per loc than you would with human management and review. Even if you assume Fable API rate comes baked in with a 50% profit margin on inference, that would still leave you with 5 million hours of streaming for the electricity usage of the Bun rewrite.

[1] To be honest, I'm kind of shocked at how much electricity I've blown on this project, after having done the math! It actually resulted in something I'm shipping and maintaining with hundreds of real-world users, at least. The project would have been easier without LLMs, which have bungled so many things and taken so much effort to correct that it would have been significantly easier to do 90% of the work myself and leave only some data-munging to the bots, but I try to stay in tune with the absolute limits of what frontier models are capable of in real-world scenarios.

Re: Show HN: Jacquard, a programming language for AI-written, human-reviewed code

#46
post #4

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 las…

Why do you think LLMs write prompts for LLMs badly? I use LLMs to write and refine prompts all the time. The prompts seem to come out very good. What are you basing this on?

Re: Show HN: Jacquard, a programming language for AI-written, human-reviewed code

#47
post #15

Earlier quoted context omitted.

Yea, LLM remove the burden of typing - so if token cost don’t explode the new high-level languages will be above the current - and the new low-level language will maybe just weights…

Like to believe we will one day use a low level language that is non-deterministic to build everything on top off seems to me in violation of such fundamental laws of information theory it's on the level of a belief in telekinesis.

All of human technology resulted from human brains, which are non-deterministic at the level you're talking about, right?

Re: Show HN: Jacquard, a programming language for AI-written, human-reviewed code

#50
> a C-emitting native AOT backend that currently compiles the kernel .jqd carrier

This human reviewer gave up at line 4 of the Readme. What is the kernel? What does the carrier carry? Why does it need to be carried? Where does the .jqd come from?

Also, for whatever reason, from the submission here:

> I had AI analyze the ASTs of several mainstream languages plus a few of the conceptually groundbreaking but esoteric ones (listed in the README)

No, they are not listed in the Readme. Why lie to us right off the bat?

I like the general idea, but the human responsible for this seems to demonstrate no interest in actually reviewing their system's output. Which does not bode well.

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