turtletontine

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.

On the bright side, I do think at some point after the bubble pops, we’ll have high quality open source models that you can run locally. Most other tech company business plans follow the enshittification cycle [1], but the interchangeability of LLMs makes it hard to imagine they can be monopolized in the same way.

1: I mean this in the strict sense of Cory Doctorow’s theory (https://en.wikipedia.org/wiki/Enshittification?wprov=sfti1#H...)

…lurked for years and even decades. Heartbleed comes to mind.

I don’t know much about Heartbleed, but Wikipedia says:

Heartbleed is a security bug… It was introduced into the software in 2012 and publicly disclosed in April 2014.

Two years doesn’t sound like “years or even decades” to me? But again, I don’t know much about Heartbleed so I may be missing something. It does say it was also patched in 2014, not just discovered then.

I have Kodi running on a raspberry pi plugged into my Google TV. The Jellyfin plugin for Kodi works flawlessly so far for me. It’s just great! Sure if I could put Jellyfin directly on the TV, that would save me the RPi. But not a big deal for me.

Tbh I can imagine this catching on if one of the big cloud providers endorses it. Including hardware support in a future version of AWS Graviton, or Azure cloud with a bunch of foundational software already developed to work with it. If one of those hyper scalers puts in the work, it could get to the point where you can launch a simple container running Postgres or whatever, with the full stack adapted to work with CHERI.

I think there is an important difference here from both Option<T> and Result<T, E>: the C3 optional doesn’t allow an arbitrary error type, it’s just a C-style integer error code. I think that makes a lot of sense and fits perfectly with their “evolution, not revolution” philosophy. And the fact that the syntax is ‘type?’ rather than ‘Optional<type>’ also eases any confusion.

Is there any potential market of parents like this: "my child wants a real phone, but I won't give them one because they'll melt their brain with tiktok and instagram"? I'm not a parent, but I imagine I'd feel something like this.

Many people are, indeed, being forced to use AI by their ignorant boss, who often blame their own employees for the AI’s shortcomings. Not all bosses everywhere of course, and it’s often just pressure to use AI instead of force.

Given how gleefully transparent corporate America is being that the plan is basically “fire everyone and replace them with AI”, you can’t blame anyone for seeing their boss pushing AI as a bad sign.

So you’re certainly right about this: AI doesn’t do things, people do things with AI. But it sure feels like a few people are going to use AI to get very very rich, while the rest of us lose our jobs.

Source?

Perhaps someone thinking “all publicity is good publicity”

On the RISC-V vector instructions, could you elaborate? Are the vector extensions substantially different from those in ARM or x86?

Framework 16 owner here, had mine for a year and a half. While most of this post is also true in my experience, I just don’t care. These are largely small cosmetic nitpicks that you need to look for to notice. The only complaints here that I really share are the jankiness of the spacers (a little jarring at first, but now I’m used to it), and the speakers really are noticeably worse than any of my other devices. So? I don’t watch movies on it, or listen to music, that’s what I have a TV and headphones for.

Seems inarguable that you can get a much more “premium” laptop for about the same cost. But I didn’t buy a framework for a “premium” feel… I was hoping to buy the last laptop I’ll ever need. And so far I’m happy with the result!

PS: the battery life is by far the best of any laptop I’ve owned. Maybe that just shows that my previous ones were junk, but I’m quite happy with it

PPS: I should note my employer was willing to buy it for me, so price was much less of a concern. Not everyone is so lucky ofc