676 karma · joined February 13, 2026
In essence, it doesn't really mandate anything; it says you should have a plan, and only for "critical infrastructure facilities":
"Section 4. Infrastructure controlled by critical artificial intelligence system. (1) When critical infrastructure facilities are controlled in whole or in part by a critical artificial intelligence system, the deployer shall develop a risk management policy after deploying the system that is reasonable and considers guidance and standards in the latest version of the artificial intelligence risk management framework from the national institute of standards and technology, the ISO/IEC 4200 artificial intelligence standard from the international organization for standardization, or another nationally or internationally recognized risk management framework for artificial intelligence systems. A plan prepared under federal requirements constitutes compliance with this section."
So it's essentially lip service to AI safety, probably to quell some objections to a bill that otherwise limits regulation of tech platforms.
But if you're a line employee for a corporation, this is the wrong approach, for two reasons. First, you will encounter many people who misinterpret directness as hostility, simply because your feelings toward another person are hard to convey in a chat message unless you include all that social-glue small talk. And if people on average think you're a jerk, they will either avoid you or reflexively push back.
But second... you're not that brilliant. Every now and then, the thing you think is wrong isn't actually wrong, you just don't understand why your solution was rejected beforehand. Maybe there are business requirements you don't know about, maybe things break in a different way if you make the change. Asking "hey, help me understand why this thing is the way it is" is often a better opener than "yo dude, your thing is broken, here's what you need to do, fix it now".
The reality is that you could run LinkedIn with far, far fewer people. You probably need fewer than 100 for core engineering, and likely less than 1,000 overall if you include compliance, sales, and so on - especially since a lot of overseas compliance stuff is outsourced to consulting firms, it's not like you have a team of lawyers in every country in the world.
Before there was so much money in the system, we used to run companies that way. Two decades ago, I worked for a company that had tens of millions of users, maintained its own complex nationwide infra (no AWS back then), and had 400 full-time employees. That made coordination problems a lot easier too. We didn't need ten layers of people and project management because there just wasn't that many of us.
Let me ask you this, though: if we wanted to, what percentage of white collar jobs could have been automated or eliminated prior to LLMs?
Meta has nearly 80k employees to basically run two websites and three mobile apps. There were 18k people working at LinkedIn! Many big tech companies are massive job programs with some product on the side. Administrative business partners, program managers, tech writers, "stewards", "champions", "advocates", 10-layer-deep reporting chains... engineers writing cafe menu apps and pet programming languages... a team working on in-house typefaces... the list goes on.
I can see AI producing shifts in the industry by reducing demand for meaningful work, but I doubt the outcome here is mass unemployment. There's an endless supply of bs jobs as long as the money is flowing.
The axioms were not handed to us from above. They were a product of a thought process anchored to intuition about the real world. The outcomes of that process can be argued about. This includes the belief that the outcomes are wrong even if we can't point to any obvious paradox.
Also, English is really too verbose and imprecise for coding, so we developed a programming language you can use instead.
Now, this gives me a business idea: are you tired of using CodeSpeak? Just explain your idea to our product in English and we'll generate CodeSpeak for you.
I'm not a fan of all the documentation and marketing content for this project evidently being AI-generated because I don't know which parts of it are the things you believe and designed for, and which are just LLM verbal diarrhea. For example, your GitHub threat model says this stops "AI training crawlers (GPTBot, ClaudeBot, CCBot, etc.)" - is this something you've actually confirmed, or just something that AI thinks is true? I don't know how their scrapers work; I'd assume they use headless browsers.
In fact, even if you can ban the human for life, I'm not sure it solves anything. There are billions of people out there and there's money to be made by monetizing attention. AI-generated content is a way to do that, so there's plenty of takers who don't mind the risk of getting booted from some platform once in a blue moon if it makes them $5k/month without requiring any effort or skill.
By the same token, was Java or Flash more dangerous than JS? On paper, no - all the same, just three virtual machines. But having all three in a browser made things fun back in the early 2000s.
And now that we're getting close to have the right design principles and mitigations in place and 0-days in JS engines are getting expensive and rare... we're set on ripping it all out and replacing it with a new and even riskier execution paradigm.
I'm not mad, it's kind of beautiful.
> A 2x2 brick can withstand over 4,000 Newtons of force, which lets children build tall structures.
> But in an assembly system like LEGO's, small errors accumulate. Stack ten bricks end-to-end and the cumulative tolerance is ten times larger. This is why LEGO models larger than 1 meter become difficult to build
> The lesson isn't that everyone should match LEGO's tolerances. It's to understand what your product actually requires, then build your manufacturing system to deliver that at the scale and cost your business model demands.
I know I'm tilting at windmills, but come on.
It's as simple as that. In common use, "if x then y" frequently implies "if not x then not y". Pretending that it's some sort of a cognitive defect to interpret it this way is silly.
I've never heard about the Wason selection task, looked it up, and could tell the right answer right away. But I can also tell you why: because I have some familiarity with formal logic and can, in your words, pattern-match the gotcha that "if x then y" is distinct from "if not x then not y".
In contrast to you, this doesn't make me believe that people are bad at logic or don't really think. It tells me that people are unfamiliar with "gotcha" formalities introduced by logicians that don't match the everyday use of language. If you added a simple additional to the problem, such as "Note that in this context, 'if' only means that...", most people would almost certainly answer it correctly.
Mind you, I'm not arguing that human thinking is necessarily more profound from what what LLMs could ever do. However, judging from the output, LLMs have a tenuous grasp on reality, so I don't think that reductionist arguments along the lines of "humans are just as dumb" are fair. There's a difference that we don't really know how to overcome.
That's an OK view to hold, but I'll point out two things. First, it's not how the tech is usually wielded to interact with open-source software. Second, your worldview is at odds with the owners of this technology: the main reason why so much money is being poured into AI coding is that it's seen by investors as a replacement for the individual.
Can you reliably tell that the contributor is truly the author of the patch and that they aren't working for a company that asserts copyright on that code? No, but it's probably still a good idea to have a policy that says "you can't do that", and you should be on the lookout for obvious violations.
It's the same story here. If you do nothing, you invite problems. If you do something, you won't stop every instance, but you're on stronger footing if it ever blows up.
Of course, the next question is whether AI-generated code that matches or surpasses human quality is even a problem. But right now, it's academic: most of the AI submissions received by open source projects are low quality. And if it improves, some projects might still have issues with it on legal (copyright) or ideological grounds, and that's their prerogative.
https://www.yankodesign.com/2026/03/09/a-cluster-of-volcanic...
Different byline, but somehow essentially the same as this story that appeared several days ago elsewhere on the internet:
https://newatlas.com/architecture/volcano-in-hotel-of-arriva...
I think that the parent is trolling, but I don't think what you're saying is true. Low number of comments usually means that no one understands the topic, but they still want to upvote because it sounds interesting or geeky. High number of comments usually means a topic where everyone feels like they can chime in without reading the article, just reacting to the title.
We're not that sophisticated. And you have evidence of this on the front page right now. A story about AI copyright with 388 comments, versus Scott Aaronson's short rant about quantum algorithms with 12 comments.
I don't think that's a meaningful parameter to think about? I'd say that on any social network, I have meaningful, ongoing relationship with maybe 20 people. I suspect that's the norm. But that doesn't mean you can join a social network with 20 users and get that. I mean, if it's a mailing list for friends and family, sure. But not if it's 20 randomly-selected strangers from around the world.
So the critical mass to make the "random stranger" type of a social network work is much, much higher than the number of daily interactions you need to keep coming back.
This is also visible in your stats if you extend the time window. They had a peak in 2024 and are pretty much declining month to month ever since.
Mastodon ended up losing its user base to Bluesky during the early Twitter exodus because many influencers and journalists wanted to have an "elite" status and a special relationship with the platform, so they preferred a platform owned by Dorsey to some hippie open-source thing. Bluesky, in turn, ended up losing back to Twitter/X when it turned out to be a place where you mostly talk about how awful Twitter/X is.
I want to say that we don't need social networks where we constantly interact with hundreds of thousands of strangers, but I'm writing this on HN, so...
I collect vintage stuff that sometimes comes with paperwork, usually after spending a decade or two stashed away in the attic.
Pencil definitely lasts if the paper is undisturbed. I have some paperwork that's 100+ years old and with legible pencil text. On the flip side, if the paper is handled a lot, the writing will gradually fade because graphite particles just sit on the surface and can flake off.
On some level, the medium is your main problem. Low-grade paper, especially if stored in suboptimal conditions (hot attic, moist crawlspace, etc), may start falling apart in 20 years or less. Thick, acid-free stock stored under controlled conditions can survive hundreds of years.
We had people defending the fired Ars Technica guy, even though he admitted to using an LLM in some sort of a contrived non-apology along the lines of "I did it because I had a cold".
My main problem with that is that you can just generate an infinite supply of LLM op-eds about LLMs, and is this really what we want to read every day? If I want to know what ChatGPT thinks about the risks or benefits of vibecoding, I'll just ask it.
We'll leave the morality of war for another time.