In my inbox: from The Guardian
#AI #LLM #AIgenerated
and yet another concrete example of where things are. One thing that always bugged me was using lsof and ps and grep or something to find out which server is hogging a port and trying to kill it. So I vibed a little go utility in a few shots (first the base functionality, then adding the TUI and killing functionality, then showing the parent process tree). Single go file, single small binary, pretty UI, always fun to use.
I legit don't care what the source code for that utility is like, I don't want to read it, I just want the functionality. And even if you read it, you'll find it perfectly ok. These things are _good_.
There's an incredible amount of stuff like that out there. #vibecoding is a really useful thing.
https://github.com/go-go-golems/go-go-labs/blob/main/cmd/apps/lsof-who/main.go
#TeaApp is the future of #VibeCoding and #LLM made software
It also comes as #financial analysts warn that an "AI bubble" of epic proportions may be getting ready to burst.
#AI #tech #bubble #hype #economics #stocks #technology #software #finance #openai #llm #chatgpt #chatbot #chatbots #SamAltman
another concrete example of where current LLM based sysstems are at. I'm reading a book by cameron buckner (thanks @UlrikeHahn ) about deep learning being used to model systems that might or might not mirror parts of the brain or underlie human cognition. It mentions in passing a thesis by piccinini and his research in doing "physical computation".
After it gives me a fairly compact overview that is way more than just regurgitating its sources, I asked it to explain some of the terms and models, then to write _python code_ for the model, which I can then validate and at least use as a starting point (I'm not all that invested in further pursuing that sidenote, but if it was for real I would go to a proper coding agent and continue the work).
How incredible is that for learning and cementing my understanding of a text? It turns it not only into a conversation, which IMO significantly improves retention and understanding, but allows me to pull together different parts of my already existing skills to read a text about philosophy, psychology and neuroscience that I frankly struggle with).
Will I go astray and not understand that the LLM is wrong or unnuanced? Obviously, I am not an expert. But it would be foolish to think that this is worse than me just doing a google search and skimming over the thesis and moving on.
https://chatgpt.com/share/6884dbe1-91d4-8012-bf9f-b4d227c54e4d
current llm-based tools are way more than "stochastic parrots", even if one decides that a transformer model does "just copying text pattern out of its training corpus" (there's a fair amount of research into if that's the case or not).
they often incorporate search (either web search, or using a specific set of documents as grounding), doing maths, interacting with APIs, or really just executing programs (which would include all the preceding tools) and more importantly "writing" programs.
Imagine I have a bug where I want to find out why a certain write seems to happen before a read and lead to a race condition. If the LLM-based agent generates a eBPF program, runs it, writes a full log, matches those writes and reads to the original source, looks up the API definitions and writes a report on why these APIs shouldn't be used together, I legitimately don't care how and why these tools and reports and information were put together. (https://github.com/go-go-golems/go-go-labs/tree/main/cmd/experiments/sniff-writes - admittedly the version in main is where I lost the plot trying to build a sparse-file diff algorithm).
These artefacts are fully deterministic, they are easy for me to assess and review, and they fix my bug. Both on a theorical level and on a practical empirical level, they don't really differ from what a colleague would create and how I would use it, except for the fact that it has certain quirks that I've become comfortable recognizing and addressing.
I have no problem saying that this LLM agent's work above (eBPF, realtime webUI, logging, analysis, search, final report) is in every single point better and more rigorous than what I would have done. Dude/ette/ez, it wrote a fricking complex eBPF script, embedded it in a go binary, has a realtime webUI, like wtf... (https://github.com/go-go-golems/go-go-labs/blob/main/cmd/experiments/sniff-writes/sniff_writes.c)
It did it using maybe 30 minutes of my own brain time? That this doesn't/won't have a real impact on labor in our current system of software production and employment is just playing ostrich. Note that I'm not saying that developers should be replaced by AI, but certainly AI replaces a significant amount of what I used to be paid for, and there is no reason for me not to use it except for my own enjoyment of solving little computer puzzles.
what is illegal for the Internet Archive is legal for Anthropic. welcome to the future, where by law, copyright law exists to limit humans, while giving corporations free reign.
Piracy Is Legal Now
https://www.youtube.com/watch?v=EoOQtwp0wOM
Moreover, Kagi (especially Ultimate) *does* have an #LLM interface.
Kagi Assistant lets you work with most of the major LLMs in one UI, with a drop-down to choose which one you want now.
As if they were just commodities.
*Because they are.*
And that, right there, is an extra nail in the coffin for the insane valuations these companies currently have...
How the Free Software Foundation battles the LLM bots: https://u.fsf.org/487 #FSF40 #TheNewsStack #LLM
Eksplosionen af LLM-genereret tekst, billeder og andre medieprodukter betyder, at vi i de kommende år vil snakke meget mere om 'bullshit'. Harry Frankenfurts klassiske bog om emnet definerer det som kommunikationer vis formål er at overbevise, uden hensyn til sandheden. Det er faktisk en ret god definition på den almindelige virkemåde for LLM-bots. Jeg kunne godt tænke mig flere danske termer for bullshit.
The marked paragraph is also a perfect description of the #convictedFelonTrump. However, not so much intelligence is involved in his case.
New paper out: Sovereign Syntax in Financial Disclosure LLMs simulate legitimacy via grammar.
SDRI = structural index to detect risk in crypto texts.
https://zenodo.org/records/16421548
https://papers.ssrn.com/abstract=5366241
#LLM #AIethics #ai #finance #agustinvstartari #LLM #MedicalNLP #LegalTech #MedTech #AIethics #AIgovernance #cryptoreg #healthcare #ArtificialIntelligence #NLP #aifutures #LawFedi #lawstodon #tech #finance #business #agustinvstartari #medical #ai #LRM #ClinicalAI #politics #regulation
A friend sent me the story of the LLM deleting a database during a code freeze and said "it lied when asked about it." I assert that a generative AI cannot lie. These aren't my original thoughts. But if you read Harry Frankfurt's famous essay On Bullshit (downloadable PDF here), he makes a very reasoned definition of bullshit. And this paragraph near the end of the essay explains why an LLM cannot lie.
It is impossible for someone to lie unless he thinks he knows the truth. Producing bullshit requires no such conviction. A person who lies is thereby responding to the truth, and he is to that extent respectful of it. When an honest man speaks, he says only what he believes to be true; and for the liar, it is correspondingly indispensable that he consider his statements to be false. For the bullshitter, however, all these bets are off: he is neither on the side of the true nor on the side of the false. His eye is not on the facts at all, as the eyes of the honest man and of the liar are, except insofar as they may be pertinent to his interest in getting away with what he says. He does not care whether the things he says describe reality correctly. He just picks them out, or makes them up, to suit his purpose.
And that's a generative artificial intelligence algorithm. Whether generating video, image, text, network traffic, whatever. It has no reference to the truth and is unaware of what truth is. It just says things. Sometimes they turn out to be true. Sometimes not. But that's irrelevant to an LLM. It doesn't know.
Saw a Reddit post about a "Peer Review LLM", and I decided to test it with one of my own current preprints to see how good it actually is.
As I expected, it's rubbish, at least in the context of what one should actually expect from a peer review, because an LLM doesn't actually *think*.
You can see it's output here:
/1
On the AI financial bubble, with bonus points for the subheading "Where’s the Money, Lebowski?"
https://prospect.org/power/2025-03-25-bubble-trouble-ai-threat/
Subliminal Learning in AIs
Today’s freaky LLM behavior:
We study subliminal learning, a surprising phenomenon where language models learn traits from model-generated data that i... https://www.schneier.com/blog/archives/2025/07/subliminal-learning-in-ais.html
A Thing That AI Could Actually Help With
Most of the “AI” and LLM things that dominate the zeitgeist lately are not designed to help people, contrary to the hype. They are designed to make filthy rich people richer, at any cost to society. So the lies about what they can actually do, told to the ignorant, are of malicious intent.
#AI #AppleMaps #DrivingDirections #GoogleMaps #LLM #Navigation #Siri
Previously I said local single-line code completion is the acceptable level of "AI" assistance for me and that JetBrains one was somewhat useful, only wrong half of the time, and easy to ignore when it is.
I've changed my mind.
See, I code primarily in TypeScript and Rust. Both of these languages have tooling that's really good at static analysis. I mean, in case of TS, static analysis is the whole product. It's slow, it requires a bunch of manual effort, but holy hell does it make life easier in the long run. Yes, it does take a whole minute to "compile" code to literally same code but with some bits removed. But it detects so many stupid mistakes as it does so, every day, it's amazing. Anyway, not the point.
The other thing modern statically-typed languages have is editor integration. You know, the first letter in IDE. This means that, as you are typing your code and completions pop up, those completions are provided by the same code that makes sure your code is correct.
Which means they are never wrong. Not "rarely". Not "except in edge cases". Zero percent of the time wrong.
If I type a dot and start typing "thing" and see "doThing(A, B)", I know this is what I was looking for. I might ctrl-click it and read the docs to make sure, but I know "doThing" exists and it takes two arguments and i can put it in and maybe even run the code and see what it does. This is the coding assistance we actually need. Exact answers, where available.
So, since I've enabled LLM completion a few months ago, I've noticed a couple of things. One: it's mostly useful when I'm doing some really basic boilerplate stuff. But if I wrote it often enough, I could find ways to automate that specific thing. It feels like this is saving me time, but it's probably seconds on a day.
Two: I am not used to code completion being wrong. Like, I see a suggestion, I accept it mentally before I accept it in the dropdown. I'm committed to going there and thinking about next steps.
And then it turns red because "doThing" is not, in fact, a method that exists.
And I stop working and go write this post because I forgot what I was even doing in the first place already.
I'm turning that shit off.
You thought vibe coding was bad?
Well.. Angela’s got something worse for ya! The billionaires now think they’re ’doing physics’ when talking to LLM’s specifically trained & prompted to suck up their egos.
Vibe physics? Anybody who even dares to think this is ”groundbreaking research” isn’t by definition smart. Not. Smart!
https://youtube.com/watch?v=TMoz3gSXBcY
#vibephysics #llm #sycophancy #vibecoding #ai