Alex

Thoughtfollower, Dysfunctional Programmer

[Alex @clowbrow]

The Muse SOUL.md, presented without comment.

[Alex @clowbrow]

@GarrettPetersen How do you get people to watch the trailer?

[Alex @clowbrow]

Messing with shaders in Raylib. Cool lava lamp effect from crushing gradients to pre-defined colors.

[Alex @clowbrow]

@floydophone Checks out. I think the go team did this with googles file download service to flex/prove the language was good internally https://t.co/r9SE7JEC9v

[Alex @clowbrow]

@_rygo6 @raysan5 I don’t think I disagree the last paragraph of the above is what I was trying to say: someone who knows what they are doing has to drive.

[Alex @clowbrow]

@_rygo6 @raysan5 I’m not sure we’re at the “AI can independently create optimized renderers” stage yet.

See: https://t.co/UHACX5Je7a

[Mitchell Hashimoto @mitchellh]

I've got an agent in a loop optimizing a renderer with the goal to minimize frame times (and tests to measure). It got times down from 88ms to 2ms and allocations down from ~150K to 500. Sounds good, right? Wrong. This is exactly why agent psychosis is a big fucking problem.

As an experiment, I rewrote the Ghostty core render state in Go, with access to identically laid out data structures as Ghostty and the exact same validation tests. I made a purposely naive renderer (simple, correct, but slow). 88ms per frame with 150,000 allocations (horrendous, lol)!

I then kickstarted a Ralph loop to bring the frame times down. I told it it can't modify input data structures or the public API or tests (they're correct), but it can do anything else it wants. It got to work.

It has worked for about 4 hours. I've spent around $350 on this experiment so far. The results?

88ms => 1.5ms
150K allocs => ~500 allocs

Incredible right? Nope.

My hand-written renderer I ported has frame times (same benchmark) of ~20us (0.020ms) and 0 allocations in the update path.

This is the problem with psychosis and lacking systems understanding. If you don't understand the system, you're going to accept that this is an incredible result. If you understand the system, you'll see better solutions immediately and can do roughly 75x better on throughput.

The people who blindly trust agent output are in the former camp. They're sheeple, overdrinking from a fountain of mediocrity.

Standard disclaimer: I use AI all the time. I like AI. The point I'm making is to not blindly accept results. Think. Analyze. Learn.

[thebes @voooooogel]

<rant>
extremely frustrating to see people laughing at this sentiment, repeating the exact failure of image generator boosters with artists in 2022/2023 based on petty ressentiment of people who've achieved technical skill in a difficult domain and alienating the exact people who could centaur the models into a more humane basin, driving the ai-curious contingent down to the stupidest common slop-peddler denominator with beacons of high-openness skillful creativity few and far between.

artists weren't blameless in that particular conflict, but they were partially right: the rhetoric around "these models are going to replace you" was very much driven at the start by ressentimentful glee over artists no longer getting to 'gatekeep art.' (by building skill back when that was the only option?) the discourse turned bidirectionally obnoxious and death-threat-laden later, but this was the poison at the start. in the early days, many artists were experimenting with image generators! (there's even still an artifact of this pre-lapsarian centaur era on display in SFO, greatly confusing modern normies... https://t.co/EjZ5QTjY4d)

compare software engineering. @random_walker had a good essay on here a couple months ago, proposing a thought experiment where generative models could only produce binaries and not readable source code:

> In this universe, software-generating LLMs compete directly with software engineers. Binaries are not human readable. They are equally inscrutable to software engineers and non-technical people. Over time, an increasing fraction of software would be vibe-rolled (not vibe-coded — there is no code!) It is not as good as human-authored software, but it’s free to generate! Since there is no source code, having a software engineer involved in the process adds nothing (except cost). Even to the extent that AI-generated software creates new demand for labor because of its limitations, it doesn't require highly paid software engineers and can instead be handled by lower-skill workers (perhaps just repeatedly yelling at the LLM to make no mistakes).

what a terrible world compared to ours. yet this is exactly the situation for current image- and video-generator models!

why is that? source code availability made it easier to build agentic pair-coder models for software... but it was still a ton of work to build out the interactive coding harnesses we have today, the models are expensively RL'd to be good at these harnesses, and in service of providing the software engineer lots of points to edit and steer and inspect the model's thinking process, they also leak lots of valuable information for distillation. it wasn't some inevitable law of the universe that we'd get agentic coders instead of something closer to "prompt to program" - markets are inefficient all the time - or that image and video models /had/ to converge on the text-to-artifact path optimized for generic, low-expertise edits like "move that over to the right."

rather, i think it's better to look at who works at the labs. the labs are filled with software engineers, and they steered the development of models towards shapes that are useful as /collaborators/ instead of replacers. claude code was literally whipped up as an engineer's side project to help him work faster, before being promoted to an official project.

meanwhile, to take a random image model company - Black Forest Labs says they are a "small team of 70" who are:

> scientists, engineers and builders - battle-tested at the world's leading companies and research labs

forgive me for noticing, but a certain type of person, and a certain type of experience, is strangely missing here... how many people at BFL do you think know e.g. what a photoshop layer blend mode is, much less has trained a model to help generate images for different ones?

---

we could have had a world where image and video generators produce references, files with layers, take lists of shot directions, etc. - a world where diffusion models could act as real collaborators to experienced artists. in this world there still probably would've been artist backlash - artists are just a conservative bunch, see the reaction to digital art - but enough open-minded artists might've started working with the models that a virtuous cycle of increasingly better generative artist tools could've ignited.

instead, there was a massive early backlash because of deliberately antagonistic comms, the models have stagnated in that early t2i UX with little improvement (image edits are just t+i2i, videos are still largely keyframed, node editors never reached much adoption, etc) despite huge jumps in raw capability. we have models that can produce whole worlds with a primary consumer base of... short form video attention farming slop? sora 2 even had the idea to do something different, yet "cameo yourself and your friends in a video" was the best they could think of. an embarrassing waste of talent.

meanwhile as someone who draws, i still find it almost impossible to turn the image i see in my head into something on the screen using a model. there are a few brave artistic souls like gossip goblin building skill, swimming upstream against the model shape current, but for the most part years later these models are still languishing at a tiny fraction of their potential.

i wish i could browse the top tweets about video generators and see beautiful thing after beautiful thing, real collaborations between humans and latent space. i think we will see that. but for now, it's dominated by grifters selling prompt packs they steal from each other and bluecheck monetizers recreating tacky versions of movie scenes to ragebait.

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why does this matter? because we're now at a similar point with math. despite the hype, models are not yet replacing human mathematicians - they've just reached the point predicted a couple years ago when people asked "why can't something that's read every publication ever make progress just by connecting ideas from disparate fields or noticing holes in the literature humans overlooked?"

turns out that took a bit more capability than we thought in 2023, but we're there now. the models can gap fill and connect ideas. that's already impressive, to be clear!

unlike with art, production of model math is not going to stagnate - there's too much economic value riding on it. it seems likely enough that at some point, maybe soon, the models will be capable of doing truly new math by inventing new constructions. we have the power to choose the shape of that - whether it's something human mathematicians can collaborate with and work into the existing cathedral of human math knowledge, or whether it's megabytes of self-contained 'verified' leanslop dumps that no human can interpret and "proof pdfs" in inscrutable model speclish produced by someone pulling the token lever without any need of understanding.

in 2023, the labs went two ways. software engineers were worried about being replaced - and the labs instead built collaborators that made them, at least for now, 10x more effective. artists were also worried about being replaced - and the labs did their best to make it happen.

the same thing is happening with math now. we don't have to end up in the world of "catching crumbs from the table," at least not yet. if we include experts in model development instead of laughing them off, we can Build the Math Harness properly. (most mathematicians can't code, let alone formalize things in Lean! models have the chance to be hugely helpful to the wider math community, not just a small contingent in the labs!)

we can develop systems that at least have /some/ chance of empowering humans instead of slowly (and then all at once) disempowering us out of the knowledge production process. we have advantages this time. the labs employ mathematicians, and even the non-mathematicians at the labs have more respect for math than they do for art. but still, let's avoid poisoning the discourse. let's not force ourselves into the stupidest of all possible timelines a second time. be nice to the mathematicians.

[Alex @clowbrow]

@ID_AA_Carmack Feels good to see this

[Alex @clowbrow]

Made a little multi-level sokoban experiment. Give it a try at: https://t.co/lxOSTHwyJ5 you can make your own levels.

[Alex @clowbrow]

@ThePrimeagen @signulll Yeah he reuses the 3d assets from model test to test so there is some kind of harness underneath that the model is working with to make these and it looks fairly rigid based on how close all the demos he’s generated are

[Alex @clowbrow]

The older I get, the more I understand Henry Darger.

[Alex @clowbrow]

Messing around with Unreal MCP a bit. It's... meh so far. https://t.co/BGeJl0plXi

[Alex @clowbrow]

Codex lately

[Alex @clowbrow]

Take this shit back to LinkedIn where it belongs

[Han Wang @handotdev]

https://t.co/uFkkNhc2Dx

[Alex @clowbrow]

@floydophone Congrats!

[Alex @clowbrow]

@TheStalwart Over a small sample size, when i tried it the LLMs actually do better if you turn reasoning off.

[Alex @clowbrow]

@vboykis Saying something is a trace implies it’s a grouping of spans that represent some kind of task being done, but yeah, basically.

[Alex @clowbrow]

Someone’s gotta get @souljaboy to record himself playing this new John Blow game.

[Jonathan Blow @Jonathan_Blow]

@FakePsyho Sounds like you are trying to play with the run button held down the entire time. This is not recommended (and that should be obvious).

[Alex @clowbrow]

@cwarzel Preordering the ear deformer 2000

[Alex @clowbrow]

@pleometric Not enough tungs