Great, especially that they still have an open-weight variant of this new model too.
But what happened to their work on their unreleased SOTA video model? did it stop being SOTA, others got ahead, and they folded the project, or what?
YT video about it: https://youtu.be/svIHNnM1Pa0?t=208
They even removed the page of that: https://bfl.ai/up-next/
Readit Newsspyder commented on FLUX.2: Frontier Visual Intelligence bfl.ai/blog/flux-2... · Posted by u/meetpateltech
> For me, it's a mystery.
For me, it's one of the last true mysteries! We've figured out damned near everything else, nothing has this level of "unknown" to it.
It's simply mind-blowing to me how such a tiny block of data can encode such high-level behaviours so indirectly!
Genes code for proteins, not synapse weights!
Those proteins influence cell division, specialisation, and growth through a complex interplay of thousands of distinct signal chemicals.
Then those cells assemble into a brain, apparently "randomly" with only crude, coarse patterns that are at best statistical in nature. Some cells are longer, some shorter, some with more interconnects, some with less, but no two perfectly alike.
Then, then, somehow... waves hands... magically this encodes that "wide hips are sexually attractive" in a way that turns up fully a decade later, well into the "pre-training" phase!!!
What... the... %#%@!
How does that work!? How does any of that work?
Y'all work in AI, ML, or adjacent to it. You know how hard it is to train a model to learn to detect anything even with thousands of examples!
PS: Human DNA contains only 750 MB (62 billion bits) of information, of which maybe 0.1% to 1% directly code for brain structure and the like. Let's be generous and say 10%. That is just 75 MB that somehow makes us scared of snakes and spiders, afraid of heights, attracted to the opposite sex, capable of speech, enjoy dancing, understand on instinct what is a "bad" or "good" smell, etc, etc...
For us it's hard to train a model because our compute and resources is nothing compared to nature's "compute" the whole universe: "it" has absurdly more resources to run different variations and massively parallel compute to run the evolutionary "algorithm", if you think about all the chemical building blocks, proteins, cells, that was "tried" and didn't survive.
From that angle our artificial models seem very sample efficient, but it's all hard to quantify it without know what was "tried" by the universe to reach the current state. But it's all weird to think about because there is no intent in natures optimizations it's just happens because it can and there is enough energy and parallel randomness to eventually happen.
And the real mystery is not how evolution achieved this but that the laws of chemistry/universe allow self-replicating structures to appear at all. In an universe with different rules it couldn't happen even with infinite trial and error compute.
spyder commented on Amazon’s Ring to partner with Flock techcrunch.com/2025/10/16... · Posted by u/gman83
Seems like a fairly impractical thing to map unless you're getting volunteers to walk up to and inspect people's front doors. I know there is an app for a sort of gamified version of this where people take tasks to verify street signs or even how many stories a building has, I used that app for a while, but doorbell mapping seems a lot leas casual.
You don't need to walk up:
1. You can do wardriving and identify them by MAC address.
2. You can use object recognition on google street view photos or your own photos while you're wardriving.
spyder commented on NanoChat – The best ChatGPT that $100 can buy github.com/karpathy/nanoc... · Posted by u/huseyinkeles
I haven't heard of this before. Has Muon dethroned Adam and AdamW as the standard general purpose optimizer for deep learning?
It's for hidden layers and not for every parameter:
From Keller's Muon github page:
"Muon is an optimizer for the hidden weights of a neural network. Other parameters, such as embeddings, classifier heads, and hidden gains/biases should be optimized using standard AdamW."
And I just looked into this nanochat repo and it's also how it's used here.
https://github.com/karpathy/nanochat/blob/dd6ff9a1cc23b38ce6...
Not here to comment on the _content_ of the blog post...
Just wanted to say the blog post design looks super nice. Beautifully laid out, very readable typography, clear graphics, approachable design with a welcoming UX, footnotes in the side, etc.
Anybody know how this is designed / styled? (I can see three.js being used, along with katex.js - but don't know more details)
Thanks
For me it's horrible, some scripts makes the scroll very choppy, unusable... had to disable scripts just to be able to normally scroll :-(
spyder commented on Context is the bottleneck for coding agents now runnercode.com/blog/conte... · Posted by u/zmccormick7
that's because a next token predictor can't "forget" context. That's just not how it works.
You load the thing up with relevant context and pray that it guides the generation path to the part of the model that represents the information you want and pray that the path of tokens through the model outputs what you want
That's why they have a tendency to go ahead and do things you tell them not to do..
also IDK about you but I hate how much praying has become part of the state of the art here. I didn't get into this career to be a fucking tech priest for the machine god. I will never like these models until they are predictable, which means I will never like them.
This is false:
"that's because a next token predictor can't "forget" context. That's just not how it works."
An LSTM is also a next token predictor and literally have a forget gate, and there are many other context compressing models too which remember only the what it thinks is important and forgets the less important, like for example: state-space models or RWKV that work well as LLMs too. But even just a the basic GPT model forgets old context since it's gets truncated if it cannot fit, but that's not really the learned smart forgetting the other models do.
spyder commented on Claude Code is all you need dwyer.co.za/static/claude... · Posted by u/sixhobbits
"Now I can just tell Claude to write an article (like the one you're currently reading) and give it some pointers regarding how I want it to look, and it can generate any custom HTML and CSS and JavaScript I want on the fly."
Yea, I know that was the case when I clicked on the thumbnails and couldn't close the image and had to reload the whole page. Good thing that you could just ask AI to fix this, but the bad thing is that you assumed it would produce fully working code in one shot and didn't test it properly.
spyder commented on The most otherworldly, mysterious forms of lightning on Earth nationalgeographic.com/sc... · Posted by u/Anon84
Came up on my feed a few days ago. Looks like convincing ball lightning to me: https://youtu.be/mmOfwFHBu_o
It's likely an arcing powerline (see the reddit comments):
https://www.reddit.com/r/videos/comments/1lrk1rz/incredible_...
spyder commented on Show HN: Real-Time Gaussian Splatting github.com/axbycc/LiveSpl... · Posted by u/markisus
Correct me if I'm wrong but looking at the video this just looks like a 3D point cloud using equal-sized "gaussians" (soft spheres) for each pixel, that's why it looks still pixelated especially at the edges. Even when it's low resolution the real gaussian splatting artifacts look different with spikes an soft blobs at the lower resolution parts.
So this is not really doing the same as a real gaussian splatting of combining different sized view-dependent elliptic gaussians splats to reconstruct the scene and also this doesn't seem to reproduce the radiance field as the real gaussian splatting does.
spyder commented on ACE-Step: A step towards music generation foundation model github.com/ace-step/ACE-S... · Posted by u/wertyk
I want to play something on my keyboard (the only instrument I am slightly ok at) and then be able to tell it to play it with a saxophone and describe exactly how I want it played. I don’t need an AI to create a song for me, I need 100 session musicians at my disposal to create the song I want. I am very excited about having that type of ai.
Here is a cool demonstration to do voice-to-instrument or instrument-to-another instrument
(The inconvenient thing is that for a new kind of output sound you have to train a model for around 1 hour for good quality, but after that you can use it with different inputs quickly):
https://youtu.be/lI1LCfTx2lI?t=525
There is also Kits.ai https://www.kits.ai/tools/ai-instruments