I'm not a Catholic (nor religious at all), and do question whether schools truly are worse than churches in terms of how children are treated.
But that's not the point. You made up a quote I didn't say ("demonization"), and accused the former poster of doing something they weren't: defending the church.
Saying that schools are worse yet ignored is not defending the church. It may also be incorrect, but that's a separate assertion.
I hear you. Coincidentally, I was actually called out just last night for a message with em dashes:
“Are you AI-ing me? Em dash giving you away”
It’s a pretty load-bearing (lol) example, yet it was clear in context it wasn’t serious. I’m beginning to notice people getting pretty good at detecting AI content, which I find reassuring.
It makes sense: AI-written prose is basically the homogenization of all styles of writing into a single voice, so it’ll stand out against the unique personalities we are used to seeing. It is just taking a minute for the average person to gain literacy — but it seems to be happening rather quickly, again unsurprising since we’re such social monkeys with a lot of our 15 watt brains dedicated to socialization and identity recognition.
Truly enjoyed this article, but hate to admit one thing: I'm so burned by AI slop that once I noticed this seems to have been "assisted" by an LLM, I got irrationally angry because I felt tricked.
It hit me at the end, when I read this:
> The idea behind Ambiance is simple: the model's priors [...] Everything else here is just in service of that.
I've noticed "in service of" take off similar to "load-bearing" with LLMs, and the whole structure just pattern matched to Claude for me.
I went back and scanned it over again, and noticed several other tells:
* "Think of U/L [Unix / Linux] as a motivating analogy rather than a direct comparison."
* "Priors" _and_ "a priori" used in the same article.
* "A real kernel […]. The Ambiance Kernel […]. The Kernel […]." — LLMs love this pattern.
To be abundantly clear, *I'm not calling this AI SLOP*; it's obvious that a human put a lot of thought into this, gives a shit about the topic, and shared interesting ideas leading to a productive discussion. I really did like it.
There's just something empty or hollow about the LLM style; paraphrasing Joni Mitchell "there's something lost and some thing gained" [1] when using AI. Your writing, your code is more consistent, more structured, more planned out — generally better but in a way that loses the character behind human writing.
[1]: From "Both Sides Now", a great song about looking something from two perspectives: youthful innocence, and jaded cynicism. Listen to the original 1969 version first, and then the 2000 remake as you can tell she's singing from the respective perspectives. Deeply meaningful song!
I believe you and OP are in agreement — they were saying that the 2019 book had them, therefore the terms _do_ predate AI. Your point that AI was being trained on material than is load-bearing (lol) but in agreement with OP, not contradictory.
Only mentioning because your "actually" may imply you thought you were disagreeing, when in fact it's one big happy family!
Interesting! I'd love to hear what type of work you do where you can get away without any of those because frankly I think most of those are pretty garbage too.
Postgres is decent for a free ($$$) database, although it's lack of clustered indexes and in-place updates (its MVCC approach) sucks for many use cases. I find it a sensible default but not the best at any one use case.
Python, frankly, sucks nowawadays. Maybe it had its time, but there are so many better lingos now. It's got type hints that are ignored, really bad patterns ("dependency injection" that's really just the singleton pattern, FastAPI encourages you to open a db connection and a transaction at the front of every request and commit at the end while you're making other requests, writing to disk, etc), and it's slow in both user experience and runtime (no real parallelism).
But generally I have to make some trades to get a great job. I love Go, personally, and the incredible simplicity it encourages.
Seriously, if you'd be willing to share, I'd love to hear what you do!
If you're actively looking for a job, you should have some familiarity with the common dev stack when you're looking. Today you should be comfortable working on a Mac, know Bash / Zsh, a little bit of Vim for SSHing, git, docker, react, postgres, etc.
If you don't, spend a few weeks before you start your search. You're almost definitely going to need them. Unless you're in a niche where the common stack is different.
This isn't me gatekeeping or something, it's just common sense. When 80% of the jobs are Python + Javascript / Typescript, running in Docker, using Postgres, using React on the frontend, FastAPI on the backend, and git plus github for deploying and reviewing, you're going to stumble without cursory knowledge. You don't need to be an expert in it all…
First come first served is capitalism in disguise. Someone will automate a tool that watches for these and jumps on them, and the person with the most resources, can get a close network connection, etc will "buy" it.
To those who have used it: is it handy for situations where you have multiple repos that want to share a little code, but it's not worth the trouble of extracting a library, referencing it, publishing versioned releases, updating dependent repos, etc?
And instead just "sync" a code folder from one main repo (perhaps containing common domain models) to other repos?
Basically the Go philosophy that a little bit of copying is better than a lot of dependency?
As someone who spent over half a decade using MS SQL Server daily, I have to admit it's a very nice database, and better than Postgres for some use cases. This could be interesting!
Postgres made the foundational design decision that every update is an insert leaving behind a stale row that'll get cleaned up eventually with its MVCC model. It's great for non-blocking read-heavy workflows (presumably the most common), but it suffers for the inverse.
For example, I wrote a simple job queue in Postgres, which had a lot of contention over the oldest rows non-terminal rows. It had many runners trying to either mark a row as completed or claim a row by atomically marking it as started and returning the row's contents (using the "skip locked" functionality). This ran much faster using similar semantics on MS SQL Server or MySQL because they have in-place updates, not an append-only tuple log.
"""
A popular story that made the rounds a few months ago was “an LLM wrote a web browser in one week, completely autonomously! It’s buggy but it works.” I won’t deny that it’s an impressive demo, but in a practical sense … what was accomplished? We trained on every open-source web browser ever written to produce a version of “git clone firefox” that’s orders of magnitude more expensive, slower, and buggier.
"""
I could tell it was AI, but it was interesting nonetheless. FWIW, I don't think AI enablement is inherently bad; this can be a game changer for an individual with an interesting thought yet difficulty expressing themselves in written word. (Obviously, it's also created a real problem with the ability to create near infinite well-written content, especially in the case of propaganda.)
I do agree with you that the quotes cited out are literary constructs used by humans, and there's a risk we get trigger-happy in calling out AI-slop. Still, those are just the most obvious tells — there were absolutely other, less notable mannerisms that confirmed it for me. If you interact enough with an LLM, you can become quite good at detecting their output through subtle subconscious cues that are hard to put to words.
I do wonder where some of the tropes came from. Claude tends to say "____ is doing a lot of work in this sentence", yet I don't recognize that as a common construction for humans overall or even a specific community (e.g. journalists). Perhaps I'm just unfamiliar with some vernaculars found in training data. Yet sometimes, it legitimately feels like they've actually developed a lingo of their own — an emergent property.
I find it all truly fascinating (along with other feelings…), and I never expected computers to be able to "understand" language anywhere near the degree we see today. Will it soon plateau, requiring another breakthrough? Or is there plenty of juice left to squeeze?
Maybe this is making a slightly different point (i.e. the usage of immutable physical features as "passwords", which I agree is largely useless), I think we're just entering a post-privacy era. Being around 40, I willingly fed the machine (Facebook) with hundreds of pictures during university, and had I not, my friends would have more than made up for it. It's absolutely possible to photograph every person you pass by every day, and Facebook most certainly has the keys to identify most all of the people. That dystopian combination means that unless you're welling to move to a largely uninhabited place, each of our whereabouts can be largely resolved with technology available _today_ at a completely reasonable cost.
Not only that, almost everyone on this forum walks around with a device that shares their identity and location with unscrupulous companies (cell phone carriers) whose data is available en masse to the government. (N.B. I have an iPhone and appreciate and even _trust_ all the privacy work put into it; however, the cell phone tower thwarts location tracking, and participation in social networks thwarts face tracking.)
I've long thought that rather than try to limit the information about us, we ought to _flood_ the Internet with information about us. Make the data available untrustworthy.
Or, accept it. So long as it remains in the hands of corporations and not solely the government, it guts both ways — a senator can no longer be publicly opposed to same-sex equality legislation while engaging in a homosexual relationship themselves.
Idk if this is intentional or just part of an innocent site that’s unwittingly hosting these but I just got a “we’re verifying your browser” page, as if _I’m_ the suspicious one. Nice social engineering.
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