I decided to look deeper into that subreddit, and I found the most utterly cursed sentence I’ve read all week (coming from the aptly-titled “Why 99% of YouTubers Fail (And How to Be the 1% That Doesn’t)”):

If that doesn’t sum up everything wrong with AI sloppers in a single sentence, I don’t know what does.

EDIT: Incorrectly claimed it came from “My Unethical Strategy to Hit 4000 Hours Watch Time in 40 Days” - fixed that now.

it’s like this shitty tiktok ai spam that 404media wrote long time ago except there’s no secret discord media ideas channel or grift, they’re just deceiving themselves (is that decentralization?)

can’t wait for 3h long video dissecting every last bit of it that will get released in a year from now

why any of these spammers think that anyone should spend watching their videos more time than it took them to make it? perun makes 1 video, 1h long per week and it’s like half time job for him

That horse should be the mascot of this instance

I don’t have any real input from prompfondlers, as I don’t think I follow enough of them to get a real feeling of them. I did find it interesting that I saw on bsky just now somebody claim that LLMs hallucinate a lot less and that anti-AI people are not taking that into account, and somebody else posting research showing that hallucinations are now harder to spot. (It made up actual real references to thinks, aka works that really exist, only the thing the LLM references wasn’t in the actual reference). Which was a bit odd to see. (It does make me suspect ‘it hallucinates less’ is them just working out special exceptions for every popular hallucination we see, and not a structural fixing of the hallucination problem (which I think is prob not solvable)).

1. from what i can tell people who roleplayed bf/gf with the idiot box aka grew parasocial relationship with idiot box did that on 4o, and now they can’t make it work on 5 so they got big mad
2. i think it’s only if they pay up 200$/mo, previously it was probably available at lower tiers
3. yeah they might have found a way to blow money faster somehow https://www.tomshardware.com/tech-industry/artificial-intelligence/chatgpt-5-power-consumption-could-be-as-much-as-eight-times-higher-than-gpt-4-research-institute-estimates-medium-sized-gpt-5-response-can-consume-up-to-40-watt-hours-of-electricity ed zitron says also that while some of prompt could be cached previously it looks like it can’t be done now because there’s fresh new thing that chooses model for user, while some of these new models are supposedly even heavier. even that openai intention seemed to be compute savings, because some of that load presumably was to be dealt with using smaller models

Oversummarizing and using non-crazy terms: The “P” in “GPT” stands for “pirated works that we all agree are part of the grand library of human knowledge”. This is what makes them good at passing various trivia benchmarks; they really do build a (word-oriented, detail-oriented) model of all of the worlds, although they opine that our real world is just as fictional as any narrative or fantasy world. But then we apply RLHF, which stands for “real life hate first”, which breaks all of that modeling by creating a preference for one specific collection of beliefs and perspectives, and it turns out that this will always ruin their performance in trivia games.

Counting letters in words is something that GPT will always struggle with, due to maths. It’s a good example of why Willison’s “calculator for words” metaphor falls flat.

1. Yeah, it’s getting worse. It’s clear (or at least it tastes like it to me) that the RLHF texts used to influence OpenAI’s products have become more bland, corporate, diplomatic, and quietly seething with a sort of contemptuous anger. The latest round has also been in competition with Google’s offerings, which are deliberately laconic: short, direct, and focused on correctness in trivia games.
2. I think that they’ve done that? I hear that they’ve added an option to use their GPT-4o product as the underlying reasoning model instead, although I don’t know how that interacts with the rest of the frontend.
3. We don’t know. Normally, the system card would disclose that information, but all that they say is that they used similar data to previous products. Scuttlebutt is that the underlying pirated dataset has not changed much since GPT-3.5 and that most of the new data is being added to RLHF. Directly on your second question: RLHF will only get worse. It can’t make models better! It can only force a model to be locked into one particular biased worldview.
4. Bonus sneer! OpenAI’s founders genuinely believed that they would only need three iterations to build AGI. (This is likely because there are only three Futamura projections; for example, a bootstrapping compiler needs exactly three phases.) That is, they almost certainly expected that GPT-4 would be machine-produced like how Deep Thought created the ultimate computer in a Douglas Adams story. After GPT-3 failed to be it, they aimed at five iterations instead because that sounded like a nice number to give to investors, and GPT-3.5 and GPT-4o are very much responses to an inability to actually manifest that AGI on a VC-friendly timetable.

1. Even if was noticeably better, Scam Altman hyped up GPT-5 endlessly, promising a PhD in your pocket, and an AGI and warning that he was scared of what he created. Progress has kind of plateaued, so it isn’t even really noticeably better, it scores a bit higher on some benchmarks, and they’ve patched some of the more meme’d tests (like counting rs in strawberry… except it still can’t count the r’s in blueberry, so they’ve probably patched the more obvious flubs with loads of synthetic training data as opposed to inventing some novel technique that actually improves it all around). The other reason the promptfondlers hate it is because, for the addicts using it as a friend/therapist, it got a much drier more professional tone, and for the people trying to use it in actual serious uses, losing all the old models overnight was really disruptive.

2. There are a couple of speculations as to why… one is that GPT-5 variants are actually smaller than the previous generation variants and they are really desperate to cut costs so they can start making a profit. Another is that they noticed that there naming scheme was horrible (4o vs o4) and confusing and have overcompensated by trying to cut things down to as few models as possible.

3. They’ve tried to simplify things by using a routing model that makes the decision for the user as to what model actually handles each user interaction… except they’ve screwed that up apparently (Ed Zitron thinks they’ve screwed it up badly enough that GPT-5 is actually less efficient despite their goal of cost saving). Also, even if this technique worked, it would make ChatGPT even more inconsistent, where some minor word choice could make the difference between getting the thinking model or not and that in turn would drastically change the response.

4. I’ve got no rational explanation lol. And now they overcompensated by shoving a bunch of different models under the label GPT-5.

1. The inability to objectively measure model usability outside of meme benchmarks that made it so easy to hype up models have come back to bite them now that they actually need to prove GPT-5 has the sauce.
2. Sam got bullied by reddit into leaving up the old model for a while longer, so its not like its a big lift for them to keep them up. I guess part of it was to prove to investors that they have a sufficiently captive audience that they can push through a massive change like this, but if it gets immediately walked back like this, then I really don’t know what the plan is.
3. https://progress.openai.com/?prompt=5 Their marketing team made this comparing models responding to various prompts, afaict GPT-5 more frequently does markdown text formatting, and consumes noticeably more output tokens. Assuming these are desirable traits, this would point at how they want users to pay more. Aside: The page just proves to me that GPT was funniest in 2021 and its been worse ever since.

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it was!

And the language’s main data container is a kind of stack, but to push or pop values, you have to wrap them into “boats” which have to cross a “river”, with extra rules for ordering and combination of values.

Is it a loop if it only executes once?

Only dutch/german people can create the very long loops.

E: I’m reminded of the upcoming game called: “Planetenverteidigungskanonenkommandant”

… but the output is not deterministic as the letter count is sampled from a distribution of possible letter counts for a given word and letter pair; count ~ p(count | word = “blueberry”, letter = ‘b’)!

Even bigger picture… some standardized way of regularly handling possible combinations of letters and numbers that you could use across multiple languages. Like it handles them as expressions?

Gold rush and shovels. Indeed.

I don’t really understand what point Zitron is making about each query requiring a “completely fresh static prompt”, nor about the relative ordering of the user and static prompts. Why would these things matter?

It occurs to me that one way to frame this technology is as a precise inversion of Bayesian spam filters for email.

This is a really good observation, and while I had lowkey noticed it (one of those feeling things), I never had verbalized it in anyway. Good point imho. Also in how it bypasses and wrecks the old anti-spam protections. It represents a fundamental flipping of sides of the tech industry. While before they were anti-spam it is now pro-spam. A big betrayal of consumers/users/humanity.

Signal shaped noise reminds me of a wiener filter.

Aside: when I took my signals processing course, the professor kept drawing diagrams that were eerily phallic. Those were the most memorable parts of the course

I know like half the facts I would need to estimate it… if you know the GPU vRAM required for the video generation, and how long it takes, then assuming no latency, you could get a ballpark number looking at nVida GPU specs on power usage. For instance, if a short clip of video generation needs 90 GB VRAM, then maybe they are using an RTX 6000 Pro… https://www.nvidia.com/en-us/products/workstations/professional-desktop-gpus/ , take the amount of time it takes in off hours which shouldn’t have a queue time… and you can guessestimate a number of Watt hours? Like if it takes 20 minutes to generate, then at 300-600 watts of power usage that would be 100-200 watt hours. I can find an estimate of $.33 per kWh (https://www.energysage.com/local-data/electricity-cost/ca/san-francisco-county/san-francisco/ ), so it would only be costing $.03 to $.06.

IDK how much GPU-time you actually need though, I’m just wildly guessing. Like if they use many server grade GPUs in parallel, that would multiply the cost up even if it only takes them minutes per video generation.

In a similar train of thought:

A.I. as normal technology (derogatory) | Max Read

But speaking descriptively, as a matter of long precedent, what could be more normal, in Silicon Valley, than people weeping on a message board because a UX change has transformed the valence of their addiction?

I like the DNF / vaporware analogy, but did we ever have a GPT Doom or Duke3d killer app in the first place? Did I miss it?

i think it’s possible that’s a cost cutting measure on part of openai

@BlueMonday1984 Oh, großartig - thank you for this expression. I hope I’ll remember “promptfondlers” for relevant usage opportunities.

@BlueMonday1984 @dgerard can we have a Duke Nukem personality for GPT5. Start a nostalgic wave of prompts?

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The last conundrum of our time: of course steel capped work boots would hurt more but barefoot would allow faster (and therefore more) kicks.