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 youāll 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 cutānāpaste it into its own post ā thereās no quota for posting and the bar really isnāt that high.
The post Xitter web has spawned soo many āesotericā right wing freaks, but thereās no appropriate sneer-space for them. Iām talking redscare-ish, reality challenged āculture criticsā who write about everything but understand nothing. Iām talking about reply-guys who make the same 6 tweets about the same 3 subjects. Theyāre inescapable at this point, yet I donāt see them mocked (as much as they should be)
Like, there was one dude a while back who insisted that women couldnāt be surgeons because they didnāt believe in the moon or in stars? I think each and every one of these guys is uniquely fucked up and if I canāt 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, Iāll see what I can do with 'em
That seems suspiciously soon, but my impression is based on nothing but vibes ā 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 Iām not sure how much of that comes from the fact that a new money sink hasnāt yet started trending in the valley. It wouldnāt 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 theyāll need to get the next thing rolling.
yeah, i think this is a last gasp or a second-last gasp.
Ed Zitron says itāll burn by end of the year, but he doesnāt 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.
Perhaps thatās part of why so many SV types are backing Trump. Grifting off Trump may be their fallback after the AI bubble collapses.
Iām 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 āgot moneyā didnāt necessarily get āmoneyā but instead commitments to funding, and āeveryoneā is a much smaller set of entities that donāt 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 (āstartup operating capital remaining available and viable to avoid deathā)
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 donāt think itās likely that much of the promised money will make it through to this layer/lot either. But thatās 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 wouldnāt want to be losing face to fucking this up)
Put me down for ādoesnāt think it will end.ā Did crypto end?
cryptoās 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 donāt believe in it, depending on the room. I think it will come back.
theyāve stopped putting fresh money in, but they believe fervently in the massive bags theyāre holding
Current flavor AI is certainly getting demystified a lot among enterprise people. Letās dip our toes into using an LLM to make our hoard of internal documents more accessible, itās supposed to actually be good at that, right? is slowly giving way to āWhat do you mean RAG is basically LLM flavored elasticsearch only more annoying and less documented? And why is all the tooling so bad?ā
Our BI team is trying to implement some RAG via Microsoft Fabrics and Azure AI search because we need that for whatever reason, and theyāve burned through almost 10k for the first half of the running month already, either because itās just super expensive or because itās so terribly documented that they canāt 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 havenāt got the foggiest whatās even going on there.
But someone from the C suite apparently wrote them a blank check because itās AI ā¦
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, āFellas, I donāt know what this is but it sure as fuck isnāt vinegar. How much did you pay for it?ā
The fuckās a rag in an AI context
NSFW (including funny example, don't worry)
RAG is āRetrieval-Augmented Generationā. Itās 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, hereās 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.
Itās 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 thatās supposed to make LLMs good, after multimodal and multiagent systems absolutely failed to do anything of note)
I always saw it more as LMGTFYaaS.
Maybe hot take, but I actually feel like the world doesnāt 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 doesnāt cost ever increasing infrastructure to overcome the problems that previous investment actually literally caused.
Definitely, but the current narrative is that you donāt need to do any of that, as long as you add three spoonfulls of AI into the mix youāll 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 thatās only slightly better than trying to grep a bunch of pdfs at the same time.
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