My best guess is this is product strategy. A markdown file doesn't require maintenance, but a feature's surface area does. Every exposed mode is another thing to document, support, A/B test, and explain to new users who stumble across it. I'm guessing that someone decided "Study Mode isn't hitting retention metrics", and decided to kill it. As an autodidact, I loved the feature, but as a software engineer I can respect the decision.
What I'm wondering about is whether there's a security angle to this as well. Assuming exposed system prompts are a jailbreak surface, if users can infer the prompt structure, would it make certain prompt injection attacks easier? I'm not well-versed in ML security, and I'd be curious to hear from someone who is.
Honestly, it probably led to long conversations. The tokens/GPU time for one long conversation is more expensive than multiple short conversations. They’re trying to shore up their finances, and they’re moving away from the consumer market and towards enterprise, and students were probably a bad demographic to sell to.
I think this is pretty much the entirety of study mode. Never used it before but as long as there's no UI changes, yes, it's 100% replicable.
To users, that's a distinct, useful feature, and they don't care about how it's implemented.
They recently made "efficient" even more verbose, my custom instructions can't suppress it properly anymore.
These "little" changes are incredibly annoying.
Codex has also been fine, but I'm guessing they know better than to tweak it like that, given their target users.
Also discussed on HN. Yeah I can ignore them, but a lot of people watch those videos and fall for the grift (going by their views) and that's sad. It personally annoys me also when yt recommends them to me because it thinks I'm interested in software
It generally knew how to solve the questions, but does not know how to properly scaffold the solution. It mostly just prompts simple calculations, rather than guide to get the insight. What’s worse is that ChatGPT would occasionally disagree with my calculation because it can’t do arithmetic!
All of a sudden feels like it gives me boilerplate and boiler plate of PR and cheesy reasoning, and like no actual answers - worse even - highly confident wrong answers that it then seeks to justify or explain (like it doesn't seem humble enough to be like "Actually, got that wrong" or if challenged it just caves over, accepts too readilythe assumptions in what the user is asking, or just blindly accepts a premise of the question) it's almost useless, like before it used to seem like could get it to emulate the way a certain writer or discourse speaks, now it seems like this derpy highschool just wants to be in kid that went into public relations and the language no matter what the topic seems always the same, it's really spammy feeling,
I could be asking it questions about like how medieval monks talked about light and the breath in latin and it will be replying like I'm interested in monetising or improving my lifestyle or some b.s. I don't think it used to be this way?
reminds of a circa 2003-6 wordpress sites - blackhat seo - feeling to generate back links to push affiliate links or something, with markov generated content designed to push back links for the actual human written landing page
It's not like this on the other llms, something's up.
Or maybe they have just found the niche and it is a bunch of people who do think like that - like I dunno - middle management the world over
that is scary ... bonus ghastly incantations of the epistemology of middle management
But I'm starting to wonder about something.
I've noticed a lot of people claiming the models—all the models from all the big providers—are deteriorating, and then go on to describe the problems that skeptics picked up on during their first few days of usage.
The models really could be getting worse. I haven't noticed anything but I don't know.
But do you think its possible that this is more akin to a honeymoon period? Depending on how you use the system and a fair bit of luck, the problems may show up for you pretty early, or may take a while to become obvious.
Arbitration idea: if a user doesn't need high QOS of newest LLM, slip them a cheaper LLM, run their query at reduced quality. measure if they cost you fewer $s in the lower QOS. => profit.
For chatgpt the arbitration opportunity looks more like "we could allocate this amount of gpu to training or inference, we are losing money if we offer the highest quality infra"
In addition there's other interesting economics scaling that can be done outside of "models of models" that are far more profitable. I won't go over all of them (and some of them I feel are quite powerful) but the laziest one is that subscription models count on some zombie users as a counterweight to highly expensive single users, and as a source of stable cashflow.
Zombie users are ones that are paying for sub but not actively or barely using the service
> ghastly incantations of the epistemology of middle management
I mean, LLM writing has been like that from the early on. Its most perfect niche for writing is the LinkedIn blog post.
How: Like any other skill practice if you were in that kind of situation and you were stuck and trying to do sudo runs as if it was real and getting the right muscle memory.
Just working in it
It's not very complicated and I actually wanted to learn it. AI doesn't make learning magically easier, but it writes decent quizzes and debugs answers so it's better than just reading the manual
TL;DR: bet on stuff being removed
I personally dont want modes. It should be smart enough to infer my intention and act accordingly
Describes this whole LLM hype really. Will be jarring if it ends up being that the value created (in terms of revenues) is mostly around software production.
