Why is that relevant if there is no one willing to prosecute and convict?
1,466 karma · joined September 25, 2017
- (1) A lot of developing can be just chores around managing scaffolds and repeatable work, and due to this macros, autogenerated code and other tools have been a thing at many layers for a long time; and
- (2) I remember copy-pasting from Google/StackOverflow (i.e. mostly search + pattern matching with some minimal reasoning) being criticized as a low-effort mode of development during the 2010s, before ChatGPT and AI assisted coding tools took over that part.
So yes, I'd argue a huge amount of software development problems can be solved without ever actually reasoning from first principles, AI tools just made that more visible.
Yep, but...
> To say they LLMs are 'predictive text models trained to match patterns in their data, statistical algorithms, not brains, not systems with “psychology” in any human sense.' is not entirely accurate.
That's a logical leap, and you'd need to bridge the gap between "more than next-token prediction" to similarity to wetware brains and "systems with psychology".
Being more concrete: I don't think they made libgen hoping the content would be used to create an even worse walled garden for knowledge.
Arguably main OpenAI raison d'être was to be a counterweight to that pre-2023 Google AI dominance. But I'd also argue that OpenAI lost its way.
Anecdotically I would say we're just in a reversal/pushback of the narrative and that's why it feels more negative/noisy right now. But I'd also add that (1) it hasn't been a prolongued situation, as it started getting more popular in late 2024 and 2025; and (2) probably won't be permanent.
Companies are bombarding us with AI in every piece of media they can, obviously with a bias on the positive. This focus is an expected counterresponse to said pressure, and it is actually good that we're not just focusing on what they want us to hear (i.e. just the pros and not the cons).
> If anything, AI may help us reduce preventable deaths.
Maybe, but as long as it development is coupled to short-term metrics like DAUs it won't.
As someone else points, the data is the worrying aspect, as it points towards state-of-the-art models not being able of making more than 0 consecutive steps without errors.
The query being tested wouldn't scan the full files and in reality the query in most sane engines would be processing much less than 650GB of data (exploiting S3 byte-range reads): i.e. just 1 column: a timestamp, which is also correlated with the partition keys. Nowadays what I would mostly be worried about the distribution of file size, due to API calls + skew; or if the query is totally different to the common query access patterns that skips the metadata/columnar nature of the underlying parquet (i.e. doing an effective "full scan" over all row groups and/or columns).
> The irony of workloads like this is that it might be cheaper to pay for a gigantic instance to run the query and finish it quicker, than to pay for a cheaper instance taking several times longer.
That's absolutely right.
You are making it sound like there is no opportunity cost to using money for this instead of, I don't know, welfare and social issues; or even other kinds of infrastructure projects altogether.
Still, if you were to make an analogy you should target for a few devices that represent the "average", just as its done for (most) pop music production.
Schools will need to reinvent themselves in some ways.
That's sloppy (hehe), if you are going to redefine a common word for the first time (i.e. references are not possible) at least do it explicitly.
[1] Wikipedia
> AI slop is digital content made with generative artificial intelligence, specifically when perceived to show a lack of effort, quality or deeper meaning, and an overwhelming volume of production.[1][4][5] Coined in the 2020s, the term has a pejorative connotation similar to spam.[4]
[2] Urban dictionary
> Low-quality randomly generated AI content (images, accounts, text, etc) that has been flooding social media sites among other pages.
Yes, I know those may not be the best primary sources, but I'd say the main shared meaning of the word is lack of quality and effort, not repetitiveness itself.
Well, inter-region DR/HA is a expensive thing to ensure (whether on salaries, infra or both), specially when you are in AWS.
It can be considered as that, sure, but anytime I see Lecun talking about this, he does recognize that you can patch your way around LLMs, the point is that you are going to hit limits eventually anyways. Specific planning benchmarks like Blockworld and the like show that LLMs (with frameworks) hit limits when they're exposed to out-of-distribution problems, and that's a BIG problem.
> We now have "agents" that can work a problem for hours, with self context trimming, planning to md files, editing those plans and so on. All of this just works, today. We used to dream about it a year ago.
I use them everyday but I still woulnd't really let them work for hours in greenfield projects. And we're seeing big vibe coders like Karpathy say the same.
PS: So just we're clear: formal planning in AI </> making a coding plan in Cursor.