Need to let loose a primal scream without collecting footnotes first? Have a sneer percolating in your system but not enough time/energy to make a whole post about it? Go forth and be mid: Welcome to the Stubsack, your first port of call for learning fresh Awful you’ll near-instantly regret.
Any awful.systems sub may be subsneered in this subthread, techtakes or no.
If your sneer seems higher quality than you thought, feel free to cut’n’paste it into its own post — there’s no quota for posting and the bar really isn’t that high.
The post Xitter web has spawned soo many “esoteric” right wing freaks, but there’s no appropriate sneer-space for them. I’m talking redscare-ish, reality challenged “culture critics” who write about everything but understand nothing. I’m talking about reply-guys who make the same 6 tweets about the same 3 subjects. They’re inescapable at this point, yet I don’t see them mocked (as much as they should be)
Like, there was one dude a while back who insisted that women couldn’t be surgeons because they didn’t believe in the moon or in stars? I think each and every one of these guys is uniquely fucked up and if I can’t escape them, I would love to sneer at them.
comment from friend:
sickos.jpg
I have asked if he can send me links to a few of these, I’ll see what I can do with 'em
Current flavor AI is certainly getting demystified a lot among enterprise people. Let’s dip our toes into using an LLM to make our hoard of internal documents more accessible, it’s supposed to actually be good at that, right? is slowly giving way to “What do you mean RAG is basically LLM flavored elasticsearch only more annoying and less documented? And why is all the tooling so bad?”
Our BI team is trying to implement some RAG via Microsoft Fabrics and Azure AI search because we need that for whatever reason, and they’ve burned through almost 10k for the first half of the running month already, either because it’s just super expensive or because it’s so terribly documented that they can’t get it to work and have to try again and again. Normal costs are somewhere around 2k for the whole month for traffic + servers + database and I haven’t got the foggiest what’s even going on there.
But someone from the C suite apparently wrote them a blank check because it’s AI …
Confucius, the Buddha, and Lao Tzu gather around a newly-opened barrel of vinegar.
Confucius tastes the vinegar and perceives bitterness.
The Buddha tastes the vinegar and perceives sourness.
Lao Tzu tastes the vinegar and perceives sweetness, and he says, “Fellas, I don’t know what this is but it sure as fuck isn’t vinegar. How much did you pay for it?”
I always saw it more as LMGTFYaaS.
The fuck’s a rag in an AI context
It’s the technique of running a primary search against some other system, then feeding an LLM the top ~25 or so documents and asking it for the specific answer.
So you run a normal query but then run the results through an enshittifier to make sure nothing useful is actually returned to the user.
Basically
so, uh, you remember AskJeeves?
(alternative answer: the third buzzword in a row that’s supposed to make LLMs good, after multimodal and multiagent systems absolutely failed to do anything of note)
NSFW (including funny example, don't worry)
RAG is “Retrieval-Augmented Generation”. It’s a prompt-engineering technique where we run the prompt through a database query before giving it to the model as context. The results of the query are also included in the context.
In a certain simple and obvious sense, RAG has been part of search for a very long time, and the current innovation is merely using it alongside a hard prompt to a model.
My favorite example of RAG is Generative Agents. The idea is that the RAG query is sent to a database containing personalities, appointments, tasks, hopes, desires, etc. Concretely, here’s a synthetic trace of a RAG chat with Batman, who I like using as a test character because he is relatively two-dimensional. We ask a question, our RAG harness adds three relevant lines from a personality database, and the model generates a response.
Maybe hot take, but I actually feel like the world doesn’t need strictly speaking more documentation tooling at all, LLM / RAG or otherwise.
Companies probably actually need to curate down their documents so that simpler thinks work, then it doesn’t cost ever increasing infrastructure to overcome the problems that previous investment actually literally caused.
Definitely, but the current narrative is that you don’t need to do any of that, as long as you add three spoonfulls of AI into the mix you’ll be as good as.
Then you find out what you actually signed up for is to do all the manual preparation of building an on-premise search engine to query unstructured data, and you still might end up with a tool that’s only slightly better than trying to grep a bunch of pdfs at the same time.
That seems suspiciously soon, but my impression is based on nothing but vibes — a sense that companies are still buying in.
I think there was a report saying that the most recent quarter still showed a massive infusion of VC cash into the space, but I’m not sure how much of that comes from the fact that a new money sink hasn’t yet started trending in the valley. It wouldn’t surprise me if the griftier founders were looking to cash out before the bubble properly bursts in order to avoid burning bridges with the investors they’ll need to get the next thing rolling.
yeah, i think this is a last gasp or a second-last gasp.
Ed Zitron says it’ll burn by end of the year, but he doesn’t list sources either so idk
We were asking around AI industry peons in March and they all guessed around three quarters too. I woulda put it at maybe two years myself, but I was surprised at so many people all arriving at around three quarters. OTOH, I would say that just in the past few months things are really obviously heading for a trauma.
I’m severely backlogged on catching up to things but my (total and complete) guess would be something like: all the recent headlines about funding and commitments are almost certainly imprecise in localisation and duration - everyone that “got money” didn’t necessarily get “money” but instead commitments to funding, and “everyone” is a much smaller set of entities that don’t encompass a really wide gallery of entities
So for all the previously-extant promptfondlers/ model dilettantes/etc out there, the writing may indeed have been (and may still be) on the wall ito runway (“startup operating capital remaining available and viable to avoid death”)
Based on the kind of headlines seen (and presuming the above supposition for the sake of argument), and the kind of utterly milquetoast garbage all the interceding months have produced, I don’t think it’s likely that much of the promised money will make it through to this layer/lot either. But that’s entirely a guess at this stage (and I can think of some fairly hefty counter-argument examples that may contribute to countering, not least because of how many people/orgs wouldn’t want to be losing face to fucking this up)
Put me down for “doesn’t think it will end.” Did crypto end?
crypto’s VC investment fell off a cliff after the crash, and that investment is what we were talking about there
hence their pivot to AI
Oh, OK. I think all the VC-adjacent people still really believe in crypto, if it helps. They probably also don’t believe in it, depending on the room. I think it will come back.
they’ve stopped putting fresh money in, but they believe fervently in the massive bags they’re holding
Perhaps that’s part of why so many SV types are backing Trump. Grifting off Trump may be their fallback after the AI bubble collapses.
deleted by creator