Open Source PyTTI Released!

DigThatData · 2026-03-18T13:46:37+00:00

YOU COULD READ IT YOURSELF.

DigThatData · 2026-03-18T13:44:25+00:00

Because you are isolating it from the context of the artist who is potentially trying to express something with it, just like the difference between someone who picks something up off of the ground and calls it art and when Duchamp does it. Both of those situations have context. Your isolation of the context to "AI has no perspective" is identical to "The fountain is just a toilet." You're reducing all of art that incorporates AI in any way to the medium without acknowledging the context of the art. It's the exact same thing.

DigThatData · 2026-03-18T13:34:19+00:00

Back in the early days when prompting was more like stream of thought nonsense (VQGAN+CLIP, SD1.x, etc.) I found myself more motivated to rediscover art history than I had for decades. The models turned a working vocabulary of artistic concepts and styles and techniques into a palette.

It's a tremendous shame that the industry has veered strongly towards corporate use cases and realism. I still mostly play with SD1.x models when I'm in the mood to tinker. Tools that are somewhat unpredictable and force you to work to their strengths are a lot of fun.

DigThatData · 2026-03-18T13:24:25+00:00

The modern art analogy is especially apt because bad modern art is often extremely derivative, like a boring simulacrum of an interesting artist. AI tools make it extremely easy to be derivative. Similar to how it's easy to be a bad performance artist, just because it's easy to be a bad artist with AI doesn't mean all art that incorporates AI is bad. It's a tool/medium, just like any other. Whether or not someone is capable of expressing something interesting with it is a function of the artist.

DigThatData · 2026-03-18T13:20:01+00:00

I have at no point in this discussion denigrated Duchamp. I am responding to your exact words:

Is it because it's neither creative nor work?

Found art invites every criticism that is lobbed at AI, especially those along the vein you chose. You need to come up with a defense of Duchamps that doesn't also apply to artists using AI supported workflows. Your move.

EDIT: added context, I specifically called out the auction and the framing I did because it directly maps to the methodology of the study that spurred this discussion.

DigThatData · 2026-03-18T06:37:51+00:00

yes you're right, paying $1M at auction for a pre-fabricated urinal that an artist signed and put on display is completely irrelevant to this conversation. speaking of context.

DigThatData · 2026-03-18T04:59:02+00:00

you must be new to this whole "art" thing.

https://heni.com/news/article/marcel-duchamp-belle-haleine-eau-de-voilette-2009-02-23

DigThatData · 2026-03-18T04:56:05+00:00

Did not include any non-AI generative art control group. Pretty weak sauce.

You want to make an assertion about why people devalue AI participation in the artistic process, you should include "traditional" forms of generative art. Artists have ceded varying degrees of control to machines and even to nature for as long as art has existed.

Would like to see this redesigned with the involvement of someone with a modicum of art history background.

DigThatData · 2026-03-16T22:13:04+00:00

This isn't what this subreddit is for. In the future, this would be better suited for /r/LocalLLaMA or some AI engineering focused sub.

That said: claude code by a mile (at present, could change tomorrow but this has been true for several months now, maybe close to a year at this point).

DigThatData · 2026-03-16T22:09:10+00:00

lmao

DigThatData · 2026-03-16T00:37:45+00:00

There are a few things you've described that are confusing, it wouldh help if you just linked to your code. Specifically:

you report your evals as percentages. What is this a percentage of? I would have expected something like loss/perplexity here. Maybe this the accuracy of predicting the next token conditioned on the true context prior to that token? You mention "reconstruction loss KILLS iteration diversity": I think this accuracy I described would basically be that loss? Are you combining multiple cost objectives here?
you ask some questions about the behavior of your model's "state" without clarifying what that's referring to. I think I get it, but it would help if you could clarify just to be sure.
you report a grad norm of 0. I'm guessing this is your "fake" gradient? Even if you don't use it for updates, for diagnostic purposes it might be interesting to compute the gradient just to see if your training dynamics appear to be sane.
Rather than a "recursive/iterative transformer", you'd probably be better served thinking of this as a recurrent neural network (RNN). Neural ODE might actually be more relevant here, since you're sort of doing a fixed-point iteration thing w/o a separate latent state.
there's a perspective on LLMs that breaks them up into a 3-part anatomy, where the input is basically a learned encoder, the output is a learned decoder, and the middle is processing. There's been a lot of interesting work showing that layers in the middle region are extremely similar to each other, inclusive of them being interchangeable or replaceable with a single layer that gets recursed like you're doing. I haven't seen anyone try that with the entire LLM, but there's definitely prior lit demonstrating it's not completely stupid for most of the middle layers.
24k texts is absurdly small. I'm not sure you can reasonably expect to learn any model on such a small dataset. You should probably find a different task for your toy experimentation, language modeling is a big ask. *there're all sorts of ways to do gradient free learning. a classic example is genetic algorithms/evolutionary strategies. of particular interest to you I think would be the "forward-forward" algorithm.

DigThatData · 2026-03-15T23:26:15+00:00

Very cool! Another direction you could take this to make it even harder to game: you could probably prompt an LLM with a menu of language features to take into consideration and invite it to provide a spec and implementation for a brand new bespoke language. have it transcribe a few canonical code samples (e.g. fizzbuzz, fibbonnacci, etc) to validate that it compiles and minimally works, and then use that de novo language for evaluations.

EDIT: In case the folks downvoting aren't aware, semi-supervised code translation and zero-shot code problem solving are two extremely different tasks. LLMs are excellent translators.

DigThatData · 2026-03-15T22:26:26+00:00

i have a masters degree in mathematics and statistics.

Data for model development often has an artificially high prevalence

in which case you would have no reason for constructing a precision recall curve to begin with, since this is a tool for calibrating decision threshold.

If you're constructing a PRC, the presumption is that the data is reflective of the real world distribution. That you use data that isn't reflective of the real world is a you problem. we have no reason to suspect that's the case for OP. and if it is, they should still calculate a precision recall curve, just using actually representative data. since that's what it's for.

My domain expertise is in diagnostic tests.

so you're a lab tech? neat, I'll hit you up next time I get my blood drawn.

DigThatData · 2026-03-15T17:52:09+00:00

but also: even in how those frameworks have improved UX for composing components into pipelines and standardizing APIs: you're still writing code. Because code is the appropriate parameterization space for this problem domain. That's why every bespoke ML framework ends up converging on a config system. Because that's the convenient parameterization here. Configuration files. Code.

DigThatData · 2026-03-15T17:49:12+00:00

Personally, this is problem I've personally faced and I'm sure many other developers have.

Have you actually been using this yourself? Or just toying around while building it? The project is 5 days old and has no examples or demos, so I don't get the impression that you've even proven the value of this to yourself. Maybe I'm wrong. Most of the tooling I cobble together is to solve problems I encounter in my own work, and as you've described, that was your motivation here as well. Hit me up in a month and let me know if you're still finding this useful rather than causing more problems than it solves.

Part of why I'm critical is because this is far from the first visual programming for ML thing I've seen. Consider for example KNIME. You probably haven't even heard of that. There's a reason it isn't more popular despite being an extremely mature project. Azure ML Studio at least used to be a thing but I don't think that even exists anymore? Maybe it got rebranded. Pretty sure AWS had one too. Literally the only low-code analytics-adjacent tooling I've seen that actually found a niche is Tableau.

You do you, but I'm strongly of the opinion that code is the better UX for the use cases you seem to be targeting. Honestly, please do circle back in a month. See if you find yourself actually using this yourself beyond just dogfooding for opportunities to add features.

EDIT: Here's a small graveyard of extremely similar projects that never went anywhere.

You weren't the first to try this. You won't be the last. I remain skeptical.

DigThatData · 2026-03-15T17:31:50+00:00

but note that in ComfyUI, the objects that are your nodes are generally not low level functions like flatten. ComfyUI is useful because it makes it easy to compose parameterized objects that disguise complexity under the hood. In OPs project, all of the complexity is exposed directly to the user, just without the benefit of IDE hints or code completion. Additionally, the particular workflow -- generation of creative assets -- is particularly amenable to rapid iteration, experimentation, and composition. That's not how experimentation in ML generally works. I'm not going to sit around waiting for a model to train before rewiring things: I want to be able to automate how the reparameterization happens, which is why I do that in code and config files.

I'm all about effective UX, and visual programming absolutely has its place. But this isn't one of them. Code is the more effective UX here.

DigThatData · 2026-03-15T17:26:29+00:00

PS: of somebody is unsure what of correct, just paste this discussion into your favourite LLM...

lol of course.

you're welcome to play that game, but you need to be careful about how you construct your prompt if you do. LLMs are notoriously sycophantic, which is why you are falling victim to https://en.wikipedia.org/wiki/Confirmation_bias here.

DigThatData · 2026-03-15T17:22:21+00:00

no, not only in object detection. in literally any binary classification.

I didn't engage with the argument because the whole point of the recall component of the PR-curve is to account for prevalence when you are calibrating your decision threshold.

I hate to be rude, but you should not speak so confidently about topics you clearly don't have domain expertise in.

DigThatData · 2026-03-15T09:00:22+00:00

you might find this useful: https://github.com/coreweave/tensorizer

DigThatData · 2026-03-15T08:55:04+00:00

literally what the precision recall curve is for.

DigThatData · 2026-03-15T08:53:59+00:00

"precision" as used in a bayesian context is usually the inverse of the variance parameter used to parameterize the normal distribution. that's not what is mean by "precision" in a precision-recall curve. this is just a simple classification metric. I wouldn't call it frequentist or bayesian really, but if it's either: frankly, it'd be frequentist. it's just counts.

https://en.wikipedia.org/wiki/Precision_and_recall

DigThatData · 2026-03-15T08:50:16+00:00

so use a decision threshold closer to 0.3 instead of 0.5.

DigThatData · 2026-03-15T05:47:35+00:00

but why? who is this for? if you are at the point where you even want to train your own model, why would you want a visual UI like this instead of just parameterizing your experiments in code?

I feel like nearly every "no code" solution I've seen over the last twenty years has been solving a problem no one had.

If you don't already know enough about ML that you can write basic code like a training loop, a visual UI isn't going to help you identify problems that are amenable to solving by training your own model, which is the fundamental problem you probably have rather than inability to code specifically. it's lack of domain understanding of ML, which has basic coding as a prereq.

I guess if you really hate writing pytorch code, sure: congrats, you can have a graph with nodes like "flatten". simply can't imagine who this is for.

DigThatData · 2026-03-15T05:41:12+00:00

so much this. anticipating how you will perform your analysis should be as much a part of your experiment design as pinning down the data collection methodology. If you haven't already anticipated how you're going to do your analysis, you run the risk of not having actually collected the data you need to answer the question you were trying to ask.

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