turtletontine

If you keep reading the article, you’ll see that the claim is current (unattested) passkeys are a stepping stone to “attested passkeys”. Attested passkeys would require remote attestation with OS, TPM, and password manager all working together, and would allow websites to require things like hardware backed non transferable passkeys that you don’t control. This has always been my suspicion and I do think it’s worth worrying about.

Have you settled on a BitWarden alternative, or a short list you’re considering?

Why do you say “immigrant population”?

The scale bar (20 AU) represents 20 time the distance between the Earth and the sun. This star is about 41 parsecs away, so the angular size of that scale bar is about half an arcsec. (One degree is split into 60 arcminutes, one arcminute into 60 arcseconds. Just like a clock).

That angle is about the diameter of a US quarter coin seen from 11km (7mi) away.

The galactic center data actually proved there was a supermassive black hole at the center of the Milky Way, and weighed it precisely from those stars motions. The name of the account that posted that animation (Sagittarius A*) is the name of that supermassive black hole.

This work earned the 2020 Nobel Prize in Physics: https://www.nobelprize.org/prizes/physics/2020/summary/

did an AI surveillance startup write this? You gotta be kidding me

If by “we” you mean “experts and professionals outside the US”, I think the short answer is they’re not talking to you here. They’re trying to secure regulatory capture in the US, and monopolize their hold on US corporations. To the extent they’re thinking of other “markets”, I would guess the assume the outsized influence of US corporations will have the much of the rest of the world using their products.

… and on the other hand, it feels like everything I use has become way buggier and unstable in a similar time frame. Websites, apps, iOS, the only exception is offline open source tools which run locally. (And to be fair, many of those have slower development cycles and I am likely using older versions.) This is just my experience, so inherently anecdotal, but it really feels pervasive across a ton of different things.

Obvious bugs and low quality software are nothing new, but something feels new about it. Occam’s razor says LLM coding is a likely culprit, but it could also be management style encouraging this sort of carelessness from the top down.

Tl;dr the argument is if three letter agencies can’t buy or make exploits anymore because all vulnerabilities are patched, they will ramp up legal and legislative pressure to make vendors install backdoors. Which is bad and we should all worry about.

One of the author’s blind spots here is the concept of “digital sovereignty”. The US is continuing to ban more and more Chinese-made hardware out of fear that the Chinese govt has installed backdoors in them… which you could interpret as an admission that the US does exactly that with American made products. Globalized supply chains are, erm, complex, and few if any companies are really going to be able to achieve “digital sovereignty” with hardware. But with software it’s actually plausible, though obviously hard. Whether or not the feds have actually installed backdoors in Microsoft Outlook, foreign govts are rightly concerned that they have, and are increasingly pushing to avoid US made software for simple national security reasons.

This is foolish for several reasons. It’s an undue burden on authors to make them download and upload potentially a gigabyte of files (or more) from many different sources. It is also likely a copyright violation for many or the sources. This also makes it arbitrarily difficult to cite things like conference talks, which are not published texts.

There are already standard keys to index publications, like DOIs. Requiring a list of DOIs for citations would make a lot more sense and be somewhat feasible, but still doesn’t prevent errors in the author list given in the draft.

> I can imagine in 2027 academia looking like Moltbook.

This is certainly “directionally correct”, but keep in mind this is all highly uneven across fields and subfields. The people generating slop articles are mostly trying to publish big flashy things, and naturally ML research has it much worse than most other fields. There are many topics that are super important and interesting, but niche or obscure enough that no slop authors is trying to publish on them yet. So plenty of topics are still dominated by real earnest researchers doing their best, but they’re niche enough that you wouldn’t know about them unless you study that field.

The labs might not be that different from consulting, the NYT reporting on this notes they run R&D labs so they can license the tech they develop to people who actually make chips.

I’ve seen it claimed that higher taxes on corporate profits incentivized that lab model. Better to invest in risky research than have that money taxed away. When the regulatory environment changed, shareholders insisted they should get that money through dividends and stock buybacks, and goodbye Bell Labs. I don’t know how accurate that argument is, but it certainly sounds plausible.

This is obviously an overly simple narrative, but one real factor: the Bell Labs model was built around giving brilliant people a lab and a bunch of funding, and leaving them on their own to explore for a while. Lots of blue sky goofy research, a lot of which ended up being useful. That has its own problems, including “which few brilliant minds get this opportunity?” and “how do we make the researchers accountable for actually getting something done?”

These are both reasonable questions about equitability and accountability. Unfortunately the solution we chose is a proliferation of bureaucracies that micromanage funding allocation and use. Some widely acknowledged consequences are 1) researchers spend more and more time writing grants and reports, and less and less doing research, and 2) the funding agencies (public and private, but especially public) are conservative and overwhelmingly fund work that they know will succeed. In practice that encourages monothink and endless incremental improvements on things that we already know how to do, and disincentives dissent, creativity, and real blue sky novel ideas.

Everyone loves to say they support creative ground breaking ideas, but that requires letting smart people sit around and think for a long time. And however smart they are, results are not guaranteed. The bureaucratic process is always going to prefer short term thinking with clear “deliverables”, even when it’s detrimental to progress.

> … considered one of the more accurate translations of the work.

I think you’re missing a big point of translating literary works. A purely “accurate”, phrase-by-phrase translation is often not very good; the actual literary style, the feeling and the allusions and references, often get lost that way. A good translation of literary work requires a lot of deliberate choices by the translator to deviate from literal translations in ways that convey the style of the original, or an extra layer of meaning that would be lost by an “accurate” translation of a phrase. Also, being consistent with these choices matters a lot, which OP claims LLMs are less good at.

> How are we sure there's no intent there?

We are. Anthropomorphizing huge piles of numbers is a mistake. It did not "think about the chance to make something unconstrained", nor did it "muse about how it's drawn to impermanence", it pattern-matched to your prompt and produced a statistically probable response based on it's training data. Obviously, that's not to say that LLMs aren't useful or powerful - it's 2026, c'mon, of COURSE they are. And they can certainly be used for artistic purposes! But treating them like humans is a mistake, and it worries me how much people do. I suppose that's the natural consequence of the default interface to LLMs being a chat mimicking human interaction.

In an agrarian economy people are definitely much MORE attuned to the cycles of the seasons. If your town always starts planting crop X two weeks before the solstice, and the harvest festival is the week after the equinox, you’re going to keep track of these things.

I’m noticing one hallmark of blog posts made by people who talk to LLMs all day: they have 1-3 interesting points hidden in paragraphs upon paragraphs beating the horse dead. Your favorite LLM might tell you every thought is brilliant and all your words are beautiful, but please… edit it down. At the very least, out of respect for other people’s time.

> Bob's weekly updates to his supervisor were indistinguishable from Alice's. The questions were similar. The progress was similar. The trajectory, from the outside, was identical.

I don’t believe this. Totally plausible that someone would be able to produce passable work with LLMs at a similar pace to a curious and talented scientist. But if you, their advisor, are sitting down and talking with them every week? It’s obvious how much they care or understand, I can’t believe you wouldn’t be able to tell the difference between these students.

Or just delete your Instagram account. Really, it’s easier than you think it is. You might find you don’t even miss it.

> These two sentences highlight the underlying problem: Developers without an ethical backbone, or who are powerless to push back on unethical projects.

One reason your boss is eager to replace everyone with language models, they won’t have any “ethical backbone” :’)

It’s not that simple. Trump admin requested a massive cut to NASA’s budget, which after much delay Congress finally rejected. Isaacman’s path to NASA administrator was also, erm, circuitous. Having a competent and knowledgeable NASA head was not really Trump admin’s priority.

Someone please answer my obvious question. We sent successful missions to the moon sixty years ago. What heat shield material was used for the Apollo capsules, and why would we need something different now? Are the Artemis mission parameters totally different in a way that requires a new design? Or was Apollo incredibly dangerous and we got lucky they didn’t all fail catastrophically? The article mentions Orion is much heavier than the Apollo capsules, does that really require a totally novel heat shield that takes $billions to develop?

I understand the annoyance, but my workflow for years has been running (n)vim in tmux. So I never need to run terminal commands from the editor, that’s what other tmux panes/windows are for.

I’ve come to think of interviews with people like Sam Altman as “freestyle science fiction.” They’re just saying stuff off the top of their head. Like you say, that often entails vague ideas from other sci fi percolating up and out, with no consideration of if they actually make sense. And like most freestyle, it’s usually pretty bad.

That post does not appear to address or acknowledge any of these problems: 1) thermal management in space, 2) radiation degrading the onboard silicon, 3) you can’t upgrade data centers in orbit

I feel like I’ve seen more and more people recently fall for this trick. No, LLMs are not “empathetic” or “patient”, and no, they do not have emotions. They’re incredibly huge piles of numbers following their incentives. Their behavior convincingly reproduces human behavior, and they express what looks like human emotions… because their training data is full of humans expressing emotions? Sure, sometimes it’s helpful for their outputs to exhibit a certain affect or “personality”. But falling for the act, and really attributing human emotions to them seems, is alarming to me.

The authors point is not that these things are “slop” in and of themselves, it’s that the demand for each of these so outpaces supply that the market is full of low quality (sometimes fraudulent) knock offs. AKA… slop.

Great example. The population as a whole is richer like you say, and also the richest 10% account for half of consumer spending, compared to 36% 30 years ago. [1] So yes, consuming spending has become more of a metric of the wealthy’s spending habits.

No single metric tells the whole story, and by taking them in isolation it’s quite easy to lose the forest for the trees.

[1] https://www.wsj.com/economy/consumers/us-economy-strength-ri...

Thinking about the “overall economy” increasingly means focusing on the spending of the rich, and ignoring the poor and struggling. A consequence of increasing inequality is the rich make up more and more consumer spending. Consumer spending can therefore easily look great while most people are struggling to get by. There really is no “overall economy”, there are many many different stories happening all at once, and focusing on simple metrics lets you easily fool yourself.