• 1 Post
  • 29 Comments
Joined 8 months ago
cake
Cake day: October 14th, 2025

help-circle

  • I don’t know if this is a good idea necessarily, but what I do to get grime out from under my nails is to (very carefully) scrape under the nail and across the quick with the tip of my pocketknife. it’s got a very sharp, narrow blade with a fine point. Between that and a heavy-duty degreaser soap, something you might find at a mechanic shop, I can usually get my nails clean enough to eat with in fairly short order.

    I have cut myself under a nail using this method and that is very unpleasant so it’s probably not ideal, but it works well enough with a steady hand that I keep doing it. Been many years now since I’ve drawn blood.

    If time isn’t a factor, I’ll just wash normally and let whatever doesn’t come off just sit under the nails for a while. eventually it’ll grow out and wash out on its own and I don’t have to go poking around. I only use the knife if I don’t feel comfortable letting whatever’s under my nails stay there. Garden dirt? Let it ride. Automotive fluid medley? Probably not great to absorb through the skin, gonna try to get that off quickly.


  • My town does have a website but it doesn’t do much other than list phone numbers and office hours for a few departments. what services are available on it are contracted out to corporate partners. I would not be surprised if the website itself is managed by a corporation as well. It is mostly useless and there is very little motivation to improve it.

    But I’m not really talking about a municipal datacenter, more like a community center or library branch having a digital commons for the neighborhood, with some useful tools, access to reliable data, and maybe some recreational software. Something like what Nextdoor should have been but not enshittified to death by VC.




  • I would love a (solar-powered) community datacenter that hosts services for the local population. Community bulletin board or forum to share event notices, lost pets, road closures etc, simple messaging and filesharing utilities for those not technical enough to host their own, maybe some simple games like chess or cards.

    The problem with the current explosion of datacenters is that they don’t benefit the community at all, they’re just digital oil rigs that drain the community of resources while also actively poisoning the area they’re in. Small wonder communities are against them.





  • Right, i mean if you made the context window enormous, such that you can include the entire set of embeddings and a set of memories (or maybe, an index of memories that can be “recalled” with keywords) you’ve got a self-observing loop that can learn and remember facts about itself. I’m not saying that’s AGI, but I find it somewhat unsettling that we don’t have an agreed-upon definition. If a for-profit corporation made an AI that could be considered a person with rights, I imagine they’d be reluctant to be convincing about it.



  • There’s no reason an LLM couldn’t be hooked up to a database, where it can save outputs and then retrieve them again to “think” further about them. In fact, any LLM that can answer questions about previous prompts/responses has to be able to do this. If you prompted an LLM to review all of it’s database entries, generate a new response based on that data, then save that output to the database and repeat at regular intervals, I could see calling that a kind of thinking. If you do the same process but with the whole model and all the DB entries, that’s in the region of what I’d call a strange loop. Is that AGI? I don’t think so, but I also don’t know how I would define AGI, or if I’d recognize it if someone built it.







  • This is in some ways an easier problem than classifying LLM vs non-LLM authorship. That only has two possible outcomes, and it’s pretty noisy because LLMs are trained to emulate the average human. Here, you can generate an agreement score based on language features per comment, and cluster the comments by how they disagree with the model. Comments that disagree in particular ways (never uses semicolons, claims to live in Canada, calls interlocutors “buddy”, writes run-on sentences, etc.) would be clustered together more tightly. The more comments two profiles have in the same cluster(s), the more confident the match becomes. I’m not saying this attack is novel or couldn’t be accomplished without an LLM, but it seems like a good fit for what LLMs actually do.


  • Why not? if LLMs are good at predicting mean outcomes for the next symbol in a string, and humans have idiosyncrasies that deviate from that mean in a predictable way, I don’t see why you couldn’t detect and correlate certain language features that map to a specific user. You could use things like word choice, punctuation, slang, common misspellings, sentence structure… For example, I started with a contradicting question, I used “idiosyncrasies”, I wrote “LLMs” without an apostrophe, “language features” is a term of art, as is “map” as a verb, etc. None of these are indicative on their own, but unless people are taking exceptional care to either hyper-normalize their style, or explicitly spiking their language with confounding elements, I don’t see why an LLM wouldn’t be useful for this kind of espionage.

    I wonder if this will have a homogenizing effect on the anonymous web. It might become an accepted practice to communicate in a highly formalized style to make this kind of style fingerprinting harder.