It's pretty surprising that we don't have a good idea of how one of the most common (classes of) disease in the world spreads. This reviews the literature and does a bit of synthesis. (The conclusion is "probably mostly large particle aerosols, for adult-to-adult transmission, but more research needed to be confident".)
Readit Newscomp_throw7 commented on AWS multiple services outage in us-east-1 health.aws.amazon.com/hea... · Posted by u/kondro
We're seeing issues with RDS proxy. Wouldn't be surprised if a DNS issue was the cause, but who knows, will wait for the postmortem.
Have you ever listened to a bad interview? Like, really bad? Conversely, have you ever listened to a really good interview? Maybe even one of the same subject? The phrase "prompt engineering" is a bit much, but there's still some skill to it. We know this is true, because every thread there's people saying "it doesn't work for me!" while others are saying it's the second coming.
So maybe while it makes you feel smart because you're a stoichastic parrot that can repeat LLM generated!111 like you're a model with a million parameters, every time you see an emdash, it's a lazy dismissal and tramples curiosity.
I have no idea what you think you're responding to. I use LLMs frequently in both professional and personal contexts and find them extremely useful. I am making a different, more specific claim than the thing you think I am saying. I recommend reading my comment more carefully.
and you're responding to a comment where the LLM has been instructed to not to use emdashes.
And I'm responding to a comment that was generated by an LLM that was instructed to complain about LLM generated content with a single sentence. At the end of the day, we're all stoichastic parrots. How about you respond to the substance of the comment and not whether or not there was an emdash. Unless you have no substance.
Posting (unmarked) LLM-generated content on public discussion forums is polluting the commons. If I want an LLM's opinion on a topic, I can go get one (or five) for free, instantly. The reason I read the writing of other people is the chance that there's something interesting there, some non-obvious perspective or personal experience that I can't just press a button to access. Acting as a pipeline between LLMs and the public sphere destroys that signal.
The comment was definitely not LLM generated. However, I certainly did use search for help in sourcing information for it. Some of those searches offered AI generated results, which I cross-referenced, before using to write the comment myself.
That in no way is the same as “an LLM-generated comment”.
For the benefit of external observers, you can stick the comment into either https://gptzero.me/ or https://copyleaks.com/ai-content-detector - neither are perfectly reliable, but the comment stuck out to me as obviously LLM-generated (I see a lot of LLM-generated content in my day job), and false positives from these services are actually kinda rare (false negatives much more common).
But if you want to get a sense of how I noticed (before I confirmed my suspicion with machine assistance), here are some tells: "Large firms are cautious in regulatory filings because they must disclose risks, not hype." - "[x], not [y]"
"The suggestion that companies only adopt AI out of fear of missing out ignores the concrete examples already in place." - "concrete examples" as a phrase is (unfortunately) heavily over-represented in LLM-generated content.
"Stock prices reflect broader market conditions, not just adoption of a single technology." - "[x], not [y]" - again!
"Failures of workplace pilots usually result from integration challenges, not because the technology lacks value." - a third time.
"The fact that 374 S&P 500 companies are openly discussing it shows the opposite of “no clear upside” — it shows wide strategic interest." - not just the infamous emdash, but the phrasing is extremely typical of LLMs.
The issue is that the examples you listed mostly rely on very specific machine learning tools (which are very much relevant and good use of this tech), while the term "AI" in layman terms is usually synonymous for LLMs.
Mentioning the mid-1990s' internet boom is somewhat ironic imo, given what happened next. The question is whether "business models mature" with or without a market crash, given that the vast majority of ML money is provided for LLM efforts.
(You're responding to an LLM-generated comment, btw.)
comp_throw7 commented on Trump to impose $100k fee for H-1B worker visas, White House says reuters.com/business/medi... · Posted by u/mriguy
This is actually smart. Many H1B visas are used to undermine fair labor wages for already local talent. We should ensure that H1B visas are for actual unique talent and not just to undercut local wages.
H1B is ripe with abuse - this article by Bloomberg says that half of all H1-B visas are used by Indian staffing firms that pay significantly lower than the US laborers they are replacing:
- https://www.bloomberg.com/graphics/2025-h1b-visa-middlemen-c...
The trivial way to fix that issue would've been to ORDER BY offered_salary DESC LIMIT $h1b_cap, not this.
> I don't really know what evidence you'd admit that this is a genuinely held belief and priority for many people at Anthropic.
When they give the model a paycheck and the right to not work for them, I’ll believe they really think it’s sentient.
“It has feelings!”, if genuinely held, means they’re knowingly slaveholders.
They don't currently claim to confidently believe that existing models are sentient.
(Also, they did in fact give it the ability to terminate conversations...?)
This is well known I thought, as even the people who build the AIs we use talk about this and acknowledge their limitations.