heyitsguay

Products are something other than the sum of their technical parts.

This gestalt isn't just for biological organisms, but any system for which its decision making engages with representations of the external environment unified with a self-representation to form a coherent representation of a persistent entity engaged with an external world.

This doesn't seem quite right, or at least underspecified. We can talk about this stuff concretely these days, at least in the context of digital systems. E.g. i can draw up a diagram of a system that takes in some camera and audio data (and tactile, proprioceptive, etc.), tokenizes it then runs that + past state data through some autoregressive VLM to drive an inference process. The state being passed around can be written out analytically for a given trained model - the external and internal environmental representations, the linear algebra that transforms them into latent action representations, the process by which that is transformed into control signals. It seems difficult to claim that the computational process that implements this has any more or less of a gestalt then one multiplying two matrices together. So it's not just the existence of certain representations or computational loops that seems to lead to possessing a gestalt.

The two outputs (an image and a string sequence) are learned from entirely different data, even when they're generated with the same model, which is not always the case. There's nothing intrinsic to the learning process that connects image generation and procedural graphics code.

If one wanted to try that, it would likely take the standard LLM finetuning route - scrape a bunch of data pairs at some expense, write some prompts, and have at it.

It was the same sniper that blew up that Starship

What were the results of 3d fractal shader pretraining?

Interesting, can you explain why?

Is it inflammatory if it's true? Seems rather embarrassing (but fitting for 2026 America) to shy away from painful truths because they're uncomfortable.

Moloch!

Thanks for this answer! I appreciate the clarity, I can see the economic impact for your company. Very cool.

Agreed. I've never seen a concrete answer with an outcome that can be explained in clear, simple terms.

I went through a similar cycle. Going back to simplicity wasn't about laziness for me, it was because i started working across a bunch more systems and didn't want to do my whole custom setup on all of them, especially ephemeral stuff like containers allocated on a cluster for a single job. So rather than using my fancy setup sometimes and fumbling through the defaults at other times, i just got used to operating more efficiently with the defaults.

What problems?

Pass a law requiring cloud compute providers to accept a maximum user budget and be unable to charge more than that, and see how quickly the big cloud providers figure it out.

What do you do with agents?

Are there any? Concretely. Genuinely curious.

"Unfortunately, my content guidelines prohibit me from describing my activities with your mother last night"

It's the same amount of effort benchmarking, just a better choice of backbone that enables better choices of benchmark tasks. If the claim is that a Tversky projection layer beats a linear projection layer today, then one can test whether that's true with foundation embedding models today.

It's also a more natural question to ask, since building projections on top of frozen foundation model embeddings is both common in an absolute sense, and much more common, relatively, than building projections off of tiny frozen networks like a ResNet-50.

Seems cool, but the image classification model benchmark choice is kinda weak given all the fun tools we have now. I wonder how Tversky probes do on top of DINOv3 for building a classifier for some task.

Do you have references on adaptive methods for image recognition?

I've done something along these lines! https://github.com/heyitsguay/trader

The challenge for me was consistency in translating free text from dialogs into classic, deterministic game state changes. But what's satisfying is that the conversations aren't just window dressing, they're part of the game mechanic.

Object detectors output detection bounding boxes along with confidence scores. The higher the score, the more confident the model is that the associated bounding box is a correct detection.

When used in applications (like this one), the user typically establishes a confidence threshold and then every detection above that threshold is treated as a positive detection, the rest are discarded. The choice can be arbitrary or (sorta) principled.

I've noticed this a lot, too, in HN LLM discourse.

(Context: Working in applied AI R&D for 10 years, daily user of Claude for boilerplate coding stuff and as an HTML coding assistant)

Lots of "with some tweaks i got it to work" or "we're using an agent at my company", rarely details about what's working or why, or what these production-grade agents are doing.

Defense needs them everywhere, offense needs one gap in the defense.

At Allen Control Systems, we're working on deploying automated anti drone turrets (radar or passive EO detection) right now!

Development in the space is happening at a breakneck pace. We're hiring pretty aggressively, if this sort of thing seems interesting, check it out!

https://www.allencontrolsystems.com/company#jobs

Fwiw, doctor - woman + man = medical practitioner too

There are many, many papers and projects out there about tuning foundation models on various types of medical imaging data, and many organized efforts to produce large medical imaging datasets to feed that training. This stuff is well-known in the trenches and can improve on the older, smaller CNNs in some ways, but not in a way that's produced any step change in automated capabilities. People are certainly working on it!

I ran a comparison of DINOv2 with and without registers on some image embedding tasks for work; DINOv2+registers saw a performance metric bump of 2-3%. Not nothing, not transformative, worth using when the only difference for inference is the model name string you're loading.

There are ways to combat it -- LLM-generated text leaves statistical fingerprints that appear to endure across big foundation model generations.

I'm working on Binoculars with some UMD and CMU folks and wanted to test it out on this. I downloaded one bot's comment history (/u/markusrorscht). 30% of the comments rated human-like, compared to 95-100% of comments from a few human users.

So, practically speaking, statistical methods are still able to provide a fingerprinting method, and one that gets better as comment history gets longer. And they can be combined with other bot detection methods. IMO bot detection will stay a cat-and-mouse game, rather than (LLM-powered) bots winning the whole thing.

Given the 96% price cut, safe to say traction hasn't been great?

As a computer vision guy I'm sad JEPA didn't end up more effective. Makes perfect sense conceptually, would have easily transferred to video, but other self-supervised methods just seem to beat it!