deborah

👁️𖦹

[Ambrose Pike @ambrose_pike]

No. Flame propagation has a minimum quenching diameter around 1-2mm, so your campfire is already smaller than the smallest self-sustaining flame, and you'd stand there in the cold like an idiot, radiating heat off your now catastrophic surface-area-to-volume ratio, dying of hypothermia next to a pile of kindling that physics has declined to ignite.

[Andrew Curran @AndrewCurran_]

I'm posting this prediction now so I can quote it later. There has been a significant breakthrough in architecture - specifically around memory efficiency - not by one of the big labs, but by a team that was spun out of OpenAI (not SSI). They will probably announce it soon.

[Fermat's Library @fermatslibrary]

In 1970, George Akerlof showed how a market can collapse because buyers know too little.

When buyers can’t distinguish good used cars from bad ones, they offer an average price. Good sellers exit. Quality falls. Prices fall again.

The market unravels. Annotated paper here: https://t.co/yjeXHMhITq

[signüll @signulll]

the craziest part now is that the modern computer probably has to be entirely reinvented, from scratch. pretty much like how jobs & co brought apple ii to market.

like not improved. not given a chatbot sidebar or something but really from the ground up like the iphone redefined what it meant to be a pocket computer.

the current paradigm for computers was built around a human staring at a screen, moving a cursor, opening apps, managing windows, naming files, remembering where things live, & manually translating intent into interface actions.

that made sense when the human was the runtime. but in an ai native world, it starts to look kinda ridiculous.

you can see this ridiculousness when you use computer use agents… they are useful sure, but they’re also obviously transitional. they’re teaching ai to operate machines designed for humans, which is clever, but also kind of absurd. it’s like making a robot hand so it can use a doorknob instead of asking why the door needs a knob at all. yes i know humans also need to use a door knob, but maybe in the future humans don’t need to use a computer, or at least what we think of a computer today at all.

this all leads to some interesting questions:

- what is a file when the system understands context?
- what is an app when intent can route itself?
- what is a desktop when work can be decomposed, executed, monitored, & summarized by agents?
- what is a browser when the agent can retrieve, compare, transact, & remember?
- what is an operating system when the primary user is no longer just a person, but a person plus a swarm of delegated intelligences? or no person at all.

the old computer assumed navigation.
the new computer has to assume a new kind of intention. the old computer organized information. the new computer has to try to organize agency.

we’re still in the hacky middle stage at the moment with sidebars, copilots, agents clicking through legacy ui, & automation layers sitting on top of 40 year old metaphors.

the new computer is likely one where memory, context, identity, permissions, tools, agents, & interfaces are native primitives. this means desktop, mobile, browser, apps, files, folders deserves another first principles look.

[Dwarkesh Patel @dwarkesh_sp]

Here's a question I find confusing and interesting and which actually tells us a lot about the nature of current AI progress:

Why has progress on computer use been so slow? Computer use is so clearly verifiable.

I think the answer is that it is not enough for a domain to be verifiable.

It also has to be very grindable—in the sense that you can run lots of parallel rollouts against a deterministic and replayable simulator.

If you’re trying to make a model better at coding, you can create an environment that has a software repo with some missing feature that you’ve tasked the AIs with creating, and then you have a thousand parallel agents just go at the problem, each with their identical copy of the container.

But this doesn’t work with computer use—at least not trivially. You can’t have a thousand agents go try the same checkout flow on Amazon. Because Andy Jassy will find and detect your bots and shut your ass down.

How would we train an AI to build a business? How would you make an AI that’s really good at winning court cases? Or having a profitable day trading in the markets? Or helping a candidate win an election?

What is the RL environment to make an AI as good at politics as Lyndon Johnson, or as good at building a space launch business as Elon Musk?

The rollout requires interacting with the world and cannot be recreated simply within the datacenter. And the outer loop verification may take months or years of real world actions to elicit, and cannot be re-observed by perturbing the model’s actions thousands of times in parallel so that you can isolate what exactly the model did that actually worked.

[David Bessis @davidbessis]

"When you dare say such things, you see on everyone's face, from the dullest who are sure they're dull, to the smartest who are certain they're smart... the same smiles, part embarrassed, part knowing, as if someone had made a joke in bad taste."
— Alexander Grothendieck

[Martin Bauer @martinmbauer]

The Planck length is not the Universe’s pixel size

[Casey Handmer @CJHandmer]

I'm old enough to remember reading Born and Wolf and the principle of holography stating that with enough computing power you can solve the complete Green function within some volume down to half a wavelength (or even better...) but that that level of computation was beyond imagination. So we made do with beam ultrasound, like peasants.

[Osita Nwanevu @OsitaNwanevu]

This is true. People were very chill. You could get chicken nuggets shaped like dinosaurs. Folks would feed you peas on a little spoon while making airplane noises. You could even dump a whole bowl of spaghetti on your head if you wanted. Totally fine. Not sure what happened.

[Andrew Curran @AndrewCurran_]

https://t.co/yUVogvdruU

[Taelin @VictorTaelin]

Just saving this here to document a story and as a self reflection on whether AI is really making me more productive

Yesterday morning I found a way to complete the new HVM approach, that is much faster than before. I spent a few hours writing a spec, and then used Opus to implement. About 3k lines of C code later, everything worked and performance was incredible: 5x faster than HVM4 (stable at ~10x now). So, in one day I had outclassed HVM4. Incredible. I'd never have implemented that so fast manually.

Now, enter today. I want to turn this into a real thing, but I haven't fully read the 3k lines yet. So, how do I trust it? I spent the whole day auditing the code. With AI. Several bugs found, most minor like forgetting to collect() some argument. But then I stumble upon this:

λ{ inl: 1 ; inr: 1 }

This was a test. But wait. This is matching on inl/inr. So the branches should receive the value of the Either. But they were numbers instead. Numbers aren't functions. This makes no sense. So why this is a test?

It then stuck me. The AI completely misunderstood how function arities work. It literally assumed for no good reason that HVM5 was supposed to handle under/over-applied functions. For no good reason. I never wrote that. It never asked either. It just kinda thought "HVM is weird in some aspects, this might be one of them..." - and then it went on to implement a massive system to handle cases that should never happen to begin with. And all of that code is obviously wrong because it should not even exist. It is wrong. It is damage. And it is there.

But it isn't too bad either. I just told Opus that it was wrong. Perhaps not so politely. And it solved it just fine.

But then this begs the question. I spent ~20 hours in this file, and it is STILL not done. I went from 0 to 95% in the first 5 hours. Yet, 15 hours later, it is still not 100%. I suppose that is the real effect of using AI. If I had just written the C file manually in the last two days, would I not be further than where I am *right now*?

Surely, the first version would have taken much longer to drop. But when I'd finish writing all that code, there would be zero, literally zero retarded shit. And, just today, I caught 5 or 6 retarded shit. And the worst part is: I don't know what the number of retarded shit left is, but I'm afraid it is >0.

So if I have to read it all, review it all to ensure there is no retarded shit... what did I achieve by using AI, other than that dopamine anticipation?

[roon @tszzl]

the frontier labs don’t have “comms problems”. reality right now has a comms problem. what is happening is a little scary and there’s no nice words anyone could say, especially not those profiting from it, that’ll make it feel that much better

[Minh Nhat Nguyen @menhguin]

fyi, nowadays im busy so i just have openclaw automations+deep research tracking @zephyr_z9 and @aleabitoreddit for new positions.
up about ~60% YTD mostly from existing positions:
memory stocks, intel calls, palantir puts, zai and minimax shares all of which are up ~100-200%.

[Daniel Litt @littmath]

Assuming models have complete advantage over human mathematicians in all areas of math (IMO not guaranteed in 15 years, but likely enough), this seems to be a question about the shape of society. Arguably the answer is not at all special to math--presumably the answer is basically the same for all knowledge work (and maybe all professions?).

I think in this world the most likely background situation is that humans are not really useful for proving theorems, but nonetheless lots of basic and understandable questions remain open, including many that are open today (since I think these have basically unbounded difficulty). And lots of other questions of basic interest have been resolved. So human activities might include (1) trying to understand solutions, (2) trying to understand progress and obstructions to resolving open questions, (3) (non-rigorously) understanding mathematical phenomena.

In all these activities, the purpose of the human is to serve as a locus of understanding. There's an obvious question as to why we would pay someone to do this. One plausible answer is that maybe we want to avoid complete disempowerment--at a minimum we might want people to understand what they can about what the AIs are doing--which requires development of human capital.

[Tony Feng @tonylfeng]

I think I can follow the proof at a technical level, but I don’t think I can give a good assessment without having worked on the problem in order to develop a feeling for why it is challenging or even interesting. The construction looks extremely clever to me, but being distant from the field I cannot ascertain how much of the cleverness is novel to GPT’s argument and how much is layered upon prior cleverness of others. For example, I was very impressed with the idea to use Golod-Shafarevich towers, but the 9-author paper suggests this is not a novel idea. The level of technical intricacy is striking. On the other hand, it seems to me that the proof does not contain any conceptual breakthrough, which is what I personally would value most.

I was also trying to digest this from a machine learning perspective. I have a mental model of the mathematical capabilities of current AI, and I’m not sure how much this event shifts that distribution; it depends a lot on whether it really came from a base model. I know the solution was also elicited from GPT 5.5 Pro with minimal prompting, but I suspect GPT Pro is already a harness in the sense of calling a base model more than once. I am not sure if those people who claim that the OpenAI solution did not involve a harness were working with the same definition.

More and more I feel that mathematics should not be thought of as a single activity, but should instead be viewed as a collection of loosely related activities, like the Olympics is to individual sports. I think AI has mastered some of the sports but is still poor at others, a nuance which doesn't seem to be appreciated on X.

[lostbutlucky @lostbutlucky]

This is genuinely hilarious.

Some anonymous person on 4chan, responding to an anime watch order question, posted a proof that later turned out to be mathematically correct and significant.

- It was posted in under an hour after the question.

- The poster basically said, “please check for loopholes.”

- It sat mostly unnoticed for seven years.

- Later, actual mathematicians checked it and were like: yeah, this is legit.

- The formal paper literally lists the author as “Anonymous 4chan Poster.”

[Noam Brown @polynoamial]

Today, we’re sharing that a general-purpose internal @openai model achieved a breakthrough on one of the best-known combinatorial geometry problems. Less than 1 year ago frontier AI models were at IMO gold-level performance. I expect this pace of progress to continue.

[Ash Jogalekar @curiouswavefn]

Cosmo Shalizi: "I like to think I am not a stupid man, and I have been reading about, and coding up, neural networks since the early 1990s. But I read Vaswani et al. (2017) ["Attention Is All You Need"] multiple times, carefully, and was quite unable to grasp what "attention" was supposed to be doing. (I could follow the math.) I also read multiple tutorials, for multiple intended audiences, and got nothing from them...the sheer opacity of this literature is I think a real problem."

https://t.co/volQDk4VDL

[Gregor Stocks @GregorStocks]

Magic: The Gathering writing has a short half-life. Historically-important articles are taken offline every year. This sucks.

That's why I'm launching Library of Leng, an index of 32 years of Magic writing. 150k+ articles, 20+ sites, fully searchable.

https://t.co/n6LTexyeed