Rudybega

Yeah, their native API reliability is absolutely horrible. Constant request failures. I've always had to go through Vertex or OpenRouter for any sort of heavy load.

Glad to help (I only really understand the implications of ETH well enough to summarize myself without assistance haha). I figured if I wanted simplified context, others might too.

To give some context:

Kakeya / local smoothing (math): The result would resolve the Kakeya conjecture and Stein’s restriction conjecture across all dimensions together. Mathematicians had been making progress one dimension at a time, so this would settle a whole family of central harmonic analysis problems at once.

ETH is false (computer science): ETH has been used as a computational hardness premise in hundreds of papers, so a disproof would undercut a large body of existing complexity results. It would also change what researchers think is achievable for efficient computation.

QCD existence (physics): It would give a rigorous mathematical foundation to the theory of the strong force, which physicists have used for decades without proving it’s well-defined. It also bears on a Millennium Prize Problem.

I haven't had this issue since like last year with frontier models. What are you using?

I use it as a drop in replacement for LLMs I had previously doing filtering and classification in a pipeline. It's loads cheaper and faster with roughly equivalent performance on my own internal tests. The confidence scores (which are also reasonably well calibrated on my data) are a super nice bonus.

I mean, if you have sufficiently well developed CI, all of those commits will 100% function. That's a part of even allowing the commit to go through.

I would guess a huge chunk of it is for internal testing/experimentation/model development.

They're starting to do things like spinning up wet labs for testing purposes, which requires a lot of hardware interfacing (hence that protocol they announced) and code.

It wouldn't shock me if the bulk of this code was for things typical users don't see.

> If the lawyer reports them to the police, but then the client challenges that on grounds of attorney-client privilege being broken, is "the LLM said it qualified" a valid reason that'll prevent the lawyer from being found at fault?

How about, the LLM flagged it, I reviewed it, and it clearly meets the standard for mandatory reporting.

If the LLM flags it (which in a sanely designed system should be a high bar with very few false positives) and they fail to review it for bad reasons, they could be liable.

I feel like neither of these scenarios are particularly hard to address.

Tokens don't need to represent text.

They are sequence to sequence models.

I mean, that's entirely possible. It's definitely less powerful than the other two. The Mindspark RCT in particular seems pretty sound (6500 participants, statistically significant improved outcomes).

Imagine a drug that cures Alzheimer's. Does that improve the baseline cognitive level of the population? What about if we manage to find a cure for various intellectual disabilities (Down's, Fragile X, FAS, etc.). Does that improve the baseline cognitive level of the population? What if we mitigate some of the worst effects of aging on a population wide scale?

What if we achieve an understanding of how memories form in the brain and what factors distinguish individuals who have eidetic or near eiditic memory and can synthesize drugs that replicate that? What if we can find similar distinguishing factors for other cognitive domains (perception, visual spatial skills, etc.) and find drugs that improve those capabilities?

> but the opening sentence seems to suggest the cure to smartphones and AI having significant negative impacts on critical educational development is giving all kids pharmaceuticals.

You have to be operating extremely cynically for that to be your default interpretation of what I said, but go off.

If we make the kind of understanding gains that I'm positing (think actual accurate simulations of various biological systems), that cycle tightens dramatically.

Yeah man, that's a good faith interpretation of what I said! You've owned me and scored points, good job!

> But in just 20 years?

Ahh, I think this bit on my part may need some elaboration. Here's my main underlying belief:

We are systematizing biology and turning it into a computationally tractable domain. I think we're going to make more gains in our understanding of it in the next 20 years than we have in the entirety of human existence prior to this moment (that's mainly because our understanding until now has been remarkably poor).

I definitely don't disagree with screen or exposure or social media exposure being deleterious.

That said, I suspect this technology will allow us to study and mitigate those effects in ways we weren't previously capable of. That's in addition to the other broad swathe of things that should make us healthier and more intelligent (see some of my other posts here). I'm not particularly negative about our chances.

Peptide style drugs already have clear positive effects on executive function, it's not particularly hard to envision that we'll find other classes of drugs that have similar effects in other cognitive domains. Twenty years is a long time, and we're set up to make absurd gains in our understanding of biology over that time frame. If we additionally are able to mitigate or cure various intellectual disabilities, that's a massive baseline performance boost to the population as well.

If you want some examples in how AI could improve education:

* Alpha School (https://alpha.school/the-program/) reports 2.6× faster learning growth with just 2 hours of AI-driven instruction daily, with students scoring in the 99th percentile nationally.

* Mindspark (https://www.povertyactionlab.org/evaluation/scaling-personal...), deployed in Indian schools, demonstrated gains of +0.22 SD in math and +0.20 SD in language in a randomized trial involving 6,500 students.

* A Harvard study (https://pmc.ncbi.nlm.nih.gov/articles/PMC12179260/) found more than 2× the median learning gains using AI tutors.

The broader implications are clear: individualized, adaptive tutoring at scale is becoming accessible in ways that historically required expensive one-on-one instruction. This is also going to be possible in places that historically never had access to this quality of instruction, which again, is a massive boost to the human baseline.

I don't think the future looks like Wall-E. I think it looks like insanely healthy people able to pursue their passions with a level of rigour and capability that has never existed before.

Edit: fucked up link 2, fixed

I'm not entirely sure what your hostility is founded in or why you think it's appropriate for a forum that's supposed to promote the direct engagement of ideas.

What aspect of my comment, specifically, do you think should embarrass me? Address the content, not the person.

Do you think we're not going to make any advances in medicine that promote a higher baseline level of cognitive function? Do you think society will entirely lose interest in educating its citizenry and will completely forgo using these tools to improve education? What's your core contention here?

What high quality engagement on your part. Clearly you're the mature party here.

I'm 35 and have worked in ML generally for a decade and NLP for six years. Any other questions about my bonafides?

If the average human in twenty years isn't far more cognitively capable than the average human now, I'll eat my hat (assuming of course, we don't all die).

Between biology advances remediating disabilities, customized outcome-driven education, potential advances in drugs that regulate executive function/memory/etc., improvements in nutrition, improvements in access, reductions in stress, I think we'll see people flourish.

And that's completely ignoring the more out there biological/computer merger style Sci-Fi outcomes, which I believe have a non-zero chance of occurring.

It's depressing how poorly most internet spaces promote sincere dialogue. There are so many cynical hot takes that don't seem to accomplish much beyond scoring social points.

This is pretty standard terminology involved in modeling the spread of anything. What do you take issue with?

I think that actually dropped to 212 just today.

https://x.com/ShivaKintali/status/2095547543061135765

To be fair, this was a commonly expressed opinion when the internet was in its infancy.

Paul Krugman (1998)

• "The growth of the Internet will slow drastically, as the flaw in 'Metcalfe's law'—which states that the number of potential connections in a network is proportional to the square of the number of participants—becomes apparent: most people have nothing to say to each other! By 2005 or so, it will become clear that the Internet's impact on the economy has been no greater than the fax machine's."

Clifford Stoll (1995)

• "We'll soon buy books and newspapers straight over the Internet. Uh, sure."

• "The truth is no online database will replace your daily newspaper, no CD-ROM can take the place of a competent teacher and no computer network will change the way government works."

• "Lacking editors, reviewers or critics, the Internet has become a wasteland of unfiltered data."

There's a massive tendency amongst even intelligent people to completely dismiss the potential of new technology based on that technology's current observed capability.

Agreed. Google search is still pretty rough, but it's hilariously better than it was when it was first released.

Apparently, I'm not supposed to extend that trend line though.

> he critical part of the backend where a new 300ms delay will cost the company millions then yeah.

If a performance regression like this isn't caught by an automated testing suite, you have made a horrible mistake.

I mean, even if you buy the idea that all LLMs are really doing under the hood is insanely good interpolation, the results produced by that interpolation are still novel and still get incorporated into the knowledge corpus of the next training run. I guess the implicit question there becomes whether that expansion allows the knowledge corpus to continuously grow or whether it eventually settles into a steady state.

It's more that it demonstrates that it's possible at all. We now know that the floor isn't an exponent of 2, which makes pursuing further improvements way more valuable.

This is only true if they can't saturate token production with a model that does less superfluous things. Given that they can (they're hilariously compute strained), having a model that solves tasks more quickly adds way more perceived value to users.

This feels like a missed opportunity for an OpenAI Nintendo Power Glove collab. Smh.

This seems hilariously, extremely revisionist.

Hell, the Opus 4.5 moment was only last November, and that was when agentic coding and most coding CLI tools became truly first class options. That's a wild paradigm shift. Hell, GPT-5 wasn't even out (that's August of last year). Most people were using 4o. Their current offerings are wildly better for coding than 4o was.