The worst was you could tell when someone had kept feeding the same image back into chatgpt to make incremental edits in a loop. The yellow filter would seemingly stack until the final result was absolutely drenched in that sickly yellow pallor, made any photorealistic humans look like they were all suffering from advanced stages of jaundice.
If there's a hint of sepia in the original image and the training data contains a lot of sepia images, it will certainly get reinforced in this process. And the original distracted boyfriend meme certainly has some strong sepia tones in the background. Same way that Dwayne Johnson's face looks a tad cartoonish. And in the intermediate steps they both flow towards some averaged human representation that seems pretty accurate if you consider the real world's ethnic distribution.
- Lucretius in "De rerum natura", probably
I don't think it's training data overrepresentation, at least not alone. RLHF and more broadly "alignment" is probably more impactful here. Likely combined with the fact that most people prompt them very briefly, so the models "default" to whatever it was most straight-forward to get a good score.
I've heard plenty of "the system still had some gremlins, but we decided to launch anyway", but not from tens of thousands of people at the same time. That's "the catch", IMO.
All people repeat the same stories and phraseology to some extent, and some people are as bad or worse than LLM chat bots in their predictability. I wonder if the latter have weak long-term memory on the scale of months to years, even if they remember things well from decades ago.
Learning a language is a big complex task, but it is far from real intelligence.
I was told this was possible many years ago by a researcher at Google and have never really seen much discussion of it since. My guess is the labs do it but keep quiet about it to avoid people trying to erase the watermark.
I thought this was an established term when it comes to working with codebases comprised of multiple interacting parts.
https://softwareengineering.stackexchange.com/questions/1325...
> the term originates from Michael Feathers Working Effectively with Legacy Code
I haven’t read the book but, taking the title and Amazon reviews at face value, I feel like this embodies Codex’s coding style as a whole. It treats all code like legacy code.
FWIW, I found the concept of "seams" from that book useful back when working on some legacy C++ monolithic code few years back, as TDD is a little more tricky than usual due to peculiarities of the language (and in particular its build model), and there it actually makes sense to know of different kind of "seams" and what they should vs. shouldn't be used for.
Other references (and all predate chatgpt):
>Seams are places in your code where you can plug in different functionality
>Art of Unit Testing, 2nd edition page 54
(https://blog.sasworkshops.com/unit-testing-and-seams/)
>With the help of a technique called creating a seam, or subclass and override we can make almost every piece of code testable.
https://www.hodler.co/2015/12/07/testing-java-legacy-code-wi...
> seam; a point in the code where I can write tests or make a change to enable testing
https://danlimerick.wordpress.com/2012/06/11/breaking-hidden...
Maybe it all ultimately traces back to the book mentioned before, but I don't believe it's an obscure term in the circles of java-y enterprise code/DI. In fact the only reason I know the term is because that's how dependency injection was first defined to me (every place you inject introduces a "seam" between the class being injected and the class you're injecting into, which allows for easy testing). I can't remember where exactly I encountered that definition though.
I'm a non-native English speaker, so maybe it's a really common idiom to use when debugging?
In the future these tells will be more identifiable. We will be easier to point back at text and code written in 2026 and more confidently say "this was written by an LLM". It takes time for patterns to form and takes time for it to be noticeable. "Smoking gun was so early 2026 claude".I find thinking of the future looking at now to be refreshing perspective on our usage.
No. But it is something goblins say a lot.
Also "something shifted" or "cracked".
Then there’s the whole Pomona College thing https://en.wikipedia.org/wiki/47_(number)
[1] https://en.wikipedia.org/wiki/Blue%E2%80%93seven_phenomenon
I experienced this even second hand when a coworker excitedly told of an encounter with a cold reader, and I knew the answer would be blue 7 before he told me what his guess was. Just his recap of the conversation was enough.
I quite liked this term when it started using it. And I appreciate the consistent way it talks about coding work even when working on radically different stacks and codebases
Frequent words I see from GPT: "shape", "seam", "lane", "gate" (especially as verb), "clean", "honest", "land", "wire", "handoff", "surface" (noun), "(un)bounded" (and sometimes "unlock")
It feels like AI really likes to pick the shortest ways to express ideas even if they aren't the most common, which I suppose would make sense if that's actually what's happening.
Another I've noticed more recently is a slight obsession over refering to "Framing".
It was using it like every 3rd sentence and I was like, yeah I have seen people say wired like this but not really for how it was using it in every sentence.
It's all one big incestuous mess. In a couple of years we'll be talking about AI brainrot.
I think a lot of the “clean” stuff stems from system prompts telling it to behave in a certain way or giving it requirements that it later responds to conversationally.
Total aside: I actually really dislike that these products keep messing around with the system prompts so much, they clearly don’t even have a good way to tell how much it’s going to change or bias the results away from other things than whatever they’re explicitly trying to correct, and like why is the AI company vibe-prompting the behavior out when they can train it and actually run it against evals.