Revanche1367

You're not contradicting anything the poster said. The reason for a lot of Chinese students and workers having high IQs wasn't the point of discussion, the sheer quantity was, and most people can see they have a lot of such people.

I had been on the pro plan for a few months and recently upgraded to the $200.00 AI Ultra plan because I've gotten good mileage out of 3.8 flash while keeping in mind that it's far from perfect. Argon is better in my experience in terms of reliability (especially in not claiming to have done things it clearly didn't) but I have to agree with the hallucination part, for a "SOTA" model (before Carbon came out at least), it hallucinates significantly more even on moderately complex tasks compared to Astra or Opus while being much slower than 3.8 flash (as expected of course). Another irritating thing is that like the 3.6+ models (but thankfully less often), it has a nasty habit of sometimes recognizing unstaged changes in the branch as not being its own work (whether done manually or with another agent) and deciding to undo them or even delete files. That's something I don't expect any SOTA model to do without at least asking the user.

Gemini so far has also had terrible computer-use capability or tool-use capability compared to ChatGPT (haven't used enough of Claude for these tasks lately to really compare). I tell ChatGPT to make some formatting changes to a LaTeX file I gave it and generate a PDF, it get's the PDF generated most of the time; occasionally the download link doesn't work, but that seems to be a server or link-generation issue, the actual PDF is generated and it quickly fixes the broken link when told. With Gemini, it's a long struggle and I use LLMs for formatting LaTeX a lot, so sometimes if ChatGPT is doing some long-running task for me, I have to begrudgingly give the document formatting task to Gemini, and it's dreadful every single time.

Before Argon was a thing, it once generated a PDF file, then started claiming PDF generation was not part of its functionality and the link that was previously working stopped doing so. I think I had inadvertently jail-broken a functionality because it refused to generate PDFs again and said the first time was a mistake. With Argon, it doesn't deny having the capability, but it takes much longer than ChatGPT, and messes up more formatting than it fixes. Its PDF viewer is also trash and seems like it gets built from scratch every time you want to view a PDF.

Plagiarism is on the part of the companies making them, the machine cannot really intend things and thus are not the things that are "guilty" of plagiarism. It also doesn't have to be the thing that decides anything, you can still decide anything of importance while acting as a guide.

I and countless others have been using them quite effectively for complex coding tasks since at least 2023. This seems to be a skill issue in itself or you haven't used any frontier models in about 3 years. Living in denial about new technology isn't very beneficial generally; look around you, the industry has already changed significantly, people like Linus Torvalds who are tough on even good human coders are willing to use LLMs nowadays.

If this was in another country that is not predominantly inhabited by people of western/central European heritage, US mainstream media would've been calling these "undercover" agents the "secret police" or "stasi" or some other term heavily connotated with tyranny and oppression.

Yeah...some people complain about OpenAI and Anthropic's model naming schemes but at least it's fairly obvious from the names how they scale, how is ordering for Argon and Carbon supposed to be interpreted? And there's the 3.x and 2.x models which I don't think have elemental names (at least not public ones). The obvious atomic number or standard weight approach doesn't work.

Maybe it's a Russian bioweapons company masquerading as an Internet traffic control company pretending to be in bioweapons manufacturing so any snoopers think it's just anti-LLM obfuscation.

Except in this case the monkeys are pretty good at coding as long as you have enough bananas (tokens). They can follow directions pretty accurately and work independently for at least short periods and incrementally test the work even if you’re not present onsite monkey-handling all the time. I don’t understand this hard anti-ai stance from open-source groups except as a submission volume issue. If and when nearly the entire professional industry changes to LLM-based code in most cases, what are they going to do to find those relatively few people who are still willing to write everything manually and spend their spare time maintaining these projects?

And warfare of course, the stuff they won’t openly talk about but we might find out about months or years down the line when it’s used to destroy another school or hospital with people inside.

How would we know if we can't identify them and they don't tell us? The ads are among us...

>many people were only familiar with LLMs as the generic AI tool for everything

That’s exactly how the biggest vendors have been marketing them, it’s no wonder non-experts think of them that way. I’m not an expert myself but have a passing familiarity with theory as a SWE and grad student who did some research for machine translation and currently am in a computing-adjacent field. I use LLMs heavily for work and have never come across any coder-oriented tutorial, guide, etc. encouraging use of LLMs as decision models specifically, even if they are being used that way “beneath the hood.” Jev’s announcement seemed sort of new functionality to me but even in the first post about it on HN (where I heard about it), iirc people were pointing out that LLMs do the same thing, at least to some extent.

It says at the bottom of the main page some articles at least are written with AI assistance in the linked article for “November Singularity” and mentions the author cannot guarantee accuracy.

While the people behind this project seem to be real and apparently have credentials, this article is giving me serious whatever the textual version of uncanny valley is called. It hits so many of what reads like assorted buzzwords meant for marketing but guised in programmer-speak. One moment, they’re talking about the Curry-Howard correspondence and then next moment they’re describing what a data type is to people who are presumably more than casually familiar with LLMs. And the worst offense imo is linking to that “November Singularity” bs clearly written by AI to sell hype to people without much of a technical background (blog writer even admits some articles are written with AI “assistance”).

Finally! As a SWE, I was getting worried scientists would be left out of the increasingly-worried-about-getting-replaced club that SWEs and mathematicians are currently enjoying being members of.

I wasn’t talking about altogether different fields, perhaps you meant branches of the same field in your last part?