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Stubsack: weekly thread for sneers not worth an entire post, week ending 1st November 2026 (awful.systems)
submitted 1 week ago by to c/techtakes@awful.systems
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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.

(Credit and/or blame to David Gerard for starting this. Happy Halloween, fellow sneerers!)

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[–] 6 points 1 day ago (7 children)

So what’s with the phenomenon of people that are dismissive of the capabilities of chatbots and freely list their constant fuckups, maybe even habitually calling them ‘clankers’, and yet still constantly fucking using them, comparing harnesses, blah blah blah? It’s not our usual cocaine analogies because these people are hardly evangelists… maybe still covered by ‘gambling addiction’? It’s just baffling to me. You humbly suggest that maybe they shouldn’t be making so much use of a chatbot that constantly bullshits them, and you don’t get any actual objection to that… and yet they continue on.

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  • [–] 5 points 22 hours ago* (last edited 22 hours ago) (4 children)

    Am I part of this category? I have a rather powerful home server that, among other things, I use to:

    • Run a decent coding LLM that helps me vomit up simple python functions for moving around lots of data between CSVs and graphing stuff, set up new docker containers with less trial and error, and run ML experiments on my GPU that previously took me hours to set up. I'm a biologist and previously I could run a commandline without breaking a computer but was very slow and non-native, now I can do simple non-security-critical things faster and actually understand novel error messages. Of note I never have that hooked up in a way that lets it run things just produce text that I can then do things with and maybe run myself.

    • Run my own local RAG system with embedding models and a small shitty LLM to search a hundred gigabytes of scientific papers and other PDFs semantically for things relevant to roughly paragraph length queries.

    • Run my own web metasearch engine with RAG and embedding models pointed at the internet, such that when I remember a paper I read fifteen years ago and provide a three sentence description it reliably finds it or finds lots of relevant old research (or old news articles I remember) when I give it a query and let it churn for five minutes.

    I feel like the important thing here is that I almost exclusively use them as information channels that take something at one end and spit it back out the other in a different or filtered form rather than making stuff, or effectively a piece of reference material for something that is formally verifiable and NOT CRITICAL FOR A SYSTEM TO FUNCTION. I also feel like all the actual innovations are more than a year old and do not understand people hanging excitedly on every little development.

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  • [–] 5 points 21 hours ago

    I don't find myself having nearly as many occasions to make use of them as you do, but I do think that all the misplaced AGI hype makes it hard to evaluate the actually technological advancement that LLMs represent. Like, being able to statistically model language to the degree these things do is neat, and I would be more surprised if it wasn't possible to use that to do some useful and powerful things. But just like how it's impossible to separate the underlying math of the cryptocurrency sphere from the ideology of technolibertarian griftocracy, everything that the LLM space has produced is poisoned on some level by millenarian technofascism. Like, the degree to which our economy and culture have been warped by these things is entirely a product of the people who think that they're going to spit out a God that makes whoever does so first king of the world forever. All the actual useful things people are getting from it are around the edges and happening in spite of the demands to remove guardrails and quality checks and human accountability from these systems. But that doesn't necessarily mean that the underlying mathematics is without value and can't be part of good and useful systems in the future. Machine Learning as a whole had already demonstrated the value in automated pattern identification and reproduction in a variety of fields from medical diagnostics to pastry sales.

    Don't get me wrong, I'm strongly in favor of throwing out all this bathwater even if there might be a baby in there somewhere. I just think that "hard to evaluate independently" isn't necessarily the same thing as "without value in other contexts".

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  • [–] 2 points 21 hours ago* (2 children)

    Can you answer a question about RAG for me? My understanding is based entirely on this diagram from Wikipedia

    If RAG takes into account vector similarity between query and reference material, do you need an LLM to regurgitate the found best vectors back to you? Could you get back a list of documents that best matched your query, or are the results in an inscrutable LLM-internal representation that need to be transformered back into readable text?

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  • [–] 7 points 20 hours ago* (1 child)

    You can have a system that just gives you a relevance-sorted list, or a system that then ingests the chunks of those documents that are most relevant and gives a human readable summary in addition to the list.

    The indexing step requires a lot of processing, by which you chunk reference documents into couple hundred word chunks and cook each of those chunks into a high dimensional embedding vector. Once you have done that and saved the database of vectors, you can put in your query, embed it, check its dot product against all the other embedding vectors, and get your ranked list (potentially quite fast) without need to produce text out the other end.

    Making those embedding vectors works much faster on a GPU rather than a CPU, but can be done on either. Since I have one system set up to ingest a bunch of web documents and embed them and search them, I have that system already set up using the GPU to go faster, so there's no real disadvantage to having it also set up to spit out a small natural language summary using a shitty tiny local LLM to go along with the list. When running it on local files, you can totally ingest the local document stash slowly on even just a CPU and just store that and run the semantic search on CPU, no LLM required.

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  • [–] 5 points 20 hours ago*

    Thanks! Well generating the summary would have the downside (for me), that I would have to look at it.

    But the semantic search bit was the one thing I felt was an actual new capability. I'm trying to imagine what if Google tried to pick this as their new moat (a better but resource-intensive way to search, scaling benefits if you establish yourself as the Internet's main vector store, people might actually pay for queries...), but no they joined the team that wants so shove slop down everybody's eyeballs.

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  • [–] 5 points 1 day ago (1 child)

    There are also really people who think all crititcs also constantly use it. Which also baffles me.

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