2025-10-23 04:58:01
I can't believe that this is what we came to.
Did any of the sci-fi authors anticipate what we arrived to?
#claude_code #claude4 #llm
Listening to #InOurTime on the Time Machine, I am asking myself whether #AI #LLM users are choosing to become Eloi, are choosing to let their intellects atrophy, by delegating their creative and critical faculties t…
This could just as easily be the US government...
#LLM #AI #Copyright
is AI real life
is it just fantasy
caught up in the hype
no escape from reality
#ai #llm #vibecoding
Pamiętacie, jak wspominałem o paczce, która miała trochę śmieci w pliku .tar.gz, przez co GNU tar rzucał błędami, a autor odpowiadział na moje zgłoszenie rozbudowaną analizą z #LLM-a i zaimplementował nieco zabełkocony kod do sprawdzania poprawności archiwum?
Wyszła nowa wersja, i tym razem archiwum to całkiem zepsuty gzip.
Szczerze mówiąc, przez te wszystkie lata opieki nad paczkami Pythona …
Kollege ChatGPT meint: "dein Wunsch nach LLM-Preismodellen, die sich dynamisch an Börsenstrompreise anpassen, ist absolut genial — aktuell gibt es sowas aber (noch) nicht im Markt"
Na, warum noch nicht? Wer macht mit? ;) #llm #greencoding
J'ai découvert Agents.md
#LLM #CodeAssistant …
You should always have a backup fiddle because you'll never know when your main fiddle goes wonky.
#AI #LLM #Copilot #Microsoft #technology
Prompt Attacks Against LLM-Powered Assistants in Production Are Practical and Dangerous
#security #llm
https://arxiv.org/abs/2508.12175
Interesting explanation of LLM training frameworks and the incentives for confident guessing.
"The authors examined ten major AI benchmarks, including those used by Google, OpenAI and also the top leaderboards that rank AI models. This revealed that nine benchmarks use binary grading systems that award zero points for AIs expressing uncertainty.
" ... When an AI system says “I don’t know”, it receives the same score as giving completely wrong information. The optimal strategy under such evaluation becomes clear: always guess. ...
"More sophisticated approaches like active learning, where AI systems ask clarifying questions to reduce uncertainty, can improve accuracy but further multiply computational requirements. ...
"Users want systems that provide confident answers to any question. Evaluation benchmarks reward systems that guess rather than express uncertainty. Computational costs favour fast, overconfident responses over slow, uncertain ones."
=
My comment: "Fast, overconfident responses" sounds a bit similar to "bullshit", does it not?
#ChatGPT #LLMs #SoCalledAI
Generative AI and the Musician, Creativity and the Human Element
- #LLM #NoAI -
https://muz4now.com/2025/generativ…
Looks like I'm going to have to ask an LLM what the point is of quote posts.
"What is the point of a quote post? What value does it bring me the social media user?"
The answer I got was facepalm...in short.
#QuotePosts #LLM
Big News! The completely #opensource #LLM #Apertus 🇨🇭 has been released today:
📰
What do carbrains and AI lovers have in common? They love to skew the data.
"Oh, cars are cheap. Just take fuel prices, fuel consumption… and if it comes too high, you can always divide by 5 people in a car! What, car purchase and maintenance?! But everyone needs to have a car!"
"Oh, #AI is cheap. A single query uses so little water and energy. What, training?! But everyone needs to train LLMs!"
#CarBrain #LLM #TechBros
The recent release of Apertus, a fully open suite of large language models (LLMs), is super interesting.
The technical report provides plenty of details about the entire process.
#ai #opensource #llm
Anscheinend nutzt #AmazonPrimeVideo irgendwelche #LLM für Untertitel. Bei #PrisonBreak gibt es keine englischen Untertitel, dafür deutsche in unterirdischer Qualität: Please wird mit "Mein Bester"…
"#Slopsquatting is a type of #cybersquatting. It is the practice of registering a non-existent software package name that a large language model (#LLM) may hallucinate in its output, whereby someone u…
Ich freunde mich immer mehr mit dem Gedanken an, dass die Arbeit mit #LLM/#KI dem Versuch ähnelt, Dämonen zu beschwören...
You know what's the difference between a human programmer and an "#AI coding assistant"?
Sure, human programmers make mistakes. And rookies often make "worse" mistakes than an #LLM can come up with. However, the difference is that humans can actually learn. Teaching them comes with a payoff; not always and not necessarily for your project, but there's a good chance that they'll become better programmers and contribute back to the community.
Sure, human programmers sometimes plagiarize. And of course they need to look at some code while they learn. However, they actually can think on their own and come up with something original. And they can learn that plagiarism is wrong.
And most importantly, usually they don't lie if they don't have to, and there are limits to their smugness. You can build a healthy community with them.
You can't build a community with unethical bullshit-spitting machines.
#programming #FreeSoftware #OpenSource
We are strongly "encouraged" to use Copilot at work. OK, fine.
I asked it for Python code to convert a Parquet file to a SAS data set, and it responded confidently with code to do so.
Only one problem. It hallucinated the method pandas.DataFrame.to_sas() into existence. There is no such method.
#LLM
LLM coding is the opposite of DRY
An important principle in software engineering is DRY: Don't Repeat Yourself. We recognize that having the same code copied in more than one place is bad for several reasons:
1. It makes the entire codebase harder to read.
2. It increases maintenance burden, since any problems in the duplicated code need to be solved in more than one place.
3. Because it becomes possible for the copies to drift apart if changes to one aren't transferred to the other (maybe the person making the change has forgotten there was a copy) it makes the code more error-prone and harder to debug.
All modern programming languages make it almost entirely unnecessary to repeat code: we can move the repeated code into a "function" or "module" and then reference it from all the different places it's needed. At a larger scale, someone might write an open-source "library" of such functions or modules and instead of re-implementing that functionality ourselves, we can use their code, with an acknowledgement. Using another person's library this way is complicated, because now you're dependent on them: if they stop maintaining it or introduce bugs, you've inherited a problem, but still, you could always copy their project and maintain your own version, and it would be not much more work than if you had implemented stuff yourself from the start. It's a little more complicated than this, but the basic principle holds, and it's a foundational one for software development in general and the open-source movement in particular. The network of "citations" as open-source software builds on other open-source software and people contribute patches to each others' projects is a lot of what makes the movement into a community, and it can lead to collaborations that drive further development. So the DRY principle is important at both small and large scales.
Unfortunately, the current crop of hyped-up LLM coding systems from the big players are antithetical to DRY at all scales:
- At the library scale, they train on open source software but then (with some unknown frequency) replicate parts of it line-for-line *without* any citation [1]. The person who was using the LLM has no way of knowing that this happened, or even any way to check for it. In theory the LLM company could build a system for this, but it's not likely to be profitable unless the courts actually start punishing these license violations, which doesn't seem likely based on results so far and the difficulty of finding out that the violations are happening. By creating these copies (and also mash-ups, along with lots of less-problematic stuff), the LLM users (enabled and encouraged by the LLM-peddlers) are directly undermining the DRY principle. If we see what the big AI companies claim to want, which is a massive shift towards machine-authored code, DRY at the library scale will effectively be dead, with each new project simply re-implementing the functionality it needs instead of every using a library. This might seem to have some upside, since dependency hell is a thing, but the downside in terms of comprehensibility and therefore maintainability, correctness, and security will be massive. The eventual lack of new high-quality DRY-respecting code to train the models on will only make this problem worse.
- At the module & function level, AI is probably prone to re-writing rather than re-using the functions or needs, especially with a workflow where a human prompts it for many independent completions. This part I don't have direct evidence for, since I don't use LLM coding models myself except in very specific circumstances because it's not generally ethical to do so. I do know that when it tries to call existing functions, it often guesses incorrectly about the parameters they need, which I'm sure is a headache and source of bugs for the vibe coders out there. An AI could be designed to take more context into account and use existing lookup tools to get accurate function signatures and use them when generating function calls, but even though that would probably significantly improve output quality, I suspect it's the kind of thing that would be seen as too-baroque and thus not a priority. Would love to hear I'm wrong about any of this, but I suspect the consequences are that any medium-or-larger sized codebase written with LLM tools will have significant bloat from duplicate functionality, and will have places where better use of existing libraries would have made the code simpler. At a fundamental level, a principle like DRY is not something that current LLM training techniques are able to learn, and while they can imitate it from their training sets to some degree when asked for large amounts of code, when prompted for many smaller chunks, they're asymptotically likely to violate it.
I think this is an important critique in part because it cuts against the argument that "LLMs are the modern compliers, if you reject them you're just like the people who wanted to keep hand-writing assembly code, and you'll be just as obsolete." Compilers actually represented a great win for abstraction, encapsulation, and DRY in general, and they supported and are integral to open source development, whereas LLMs are set to do the opposite.
[1] to see what this looks like in action in prose, see the example on page 30 of the NYTimes copyright complaint against OpenAI (#AI #GenAI #LLMs #VibeCoding
This morning, for the first time ever, I asked an #LLM to translate a snippet of code for me, from #Javascript to #ClojureScript. It got it wrong, but it got enough of it right that it saved me some time…
I need some #FediHelp! Somebody here was sharing a blog post about how they tried the METR study on themselves and discovered that results were statistically insigificant, which is very telling.
I thought I boosted and saved it for myself, but apparently not and I'd like to spread the word.
Anyone? #llm
Well, I am complaining about #AI slop introducing some random bugs in a minor userspace project, and in the meantime I learn that #Linux #kernel LTS developers are using AI to backport patches, and creating new vulnerabilities in the process.
Note: the whole thread is quite toxic, so I'd take it with a grain of salt, but still looks like the situation is quite serious.
"You too can crash today's 6.12.43 LTS kernel thanks to a stable maintainer's AI slop."
And apparently this isn't the first time either:
"When AI decided to select a random CPU mitigation patch for backport last month that turned a mitigation into a no-op, nothing was done, it sat unfixed with a report for a month (instead of just immediately reverting it), and they rejected a CVE request for it."
#security #LLM #NVIDIA #Gentoo
3️⃣ Die @… hat eine Stellungnahme zu Datenschutzfragen veröffentlicht. In dieser Stellungnahme kritisiert sie, dass anonyme #KI-Modelle, insbesondere bei #LLM, aktuell und wahrscheinlich noch länger eine Illusion seien, weder was die Trainingsdaten noch den Output betrifft. So könne nicht verhindert werden, dass diese Modelle reale oder erfundene persönliche Informationen preisgäben.
Massnahmen, auch bezüglich Minderjährigen und mehr, hier:
https://gi.de/meldung/gi-beantwortet-fragen-der-datenschutzbeauftragten-zu-llms
Exciting preview from @stripe! They're building a new #LLM token billing system. Auto-update model prices, enforce your markup (e.g., 30%), and record usage via proxies (OpenRouterAI, Cloudflare, etc.). Simplify AI app billing 🧵: https://t.co/Jf59NRxwE0…
Neuer Blog-Post (zusammen mit @…) zum Thema KI für Mathe Forschung.
tldr; Ich beobachte und probiere, hatte aber noch keine Erfolgserlebnisse. Mathematiker*innen sollten aber nicht wegsehen sondern mitgestalten. Und die Werbung der KI-Firmen nicht glauben.
#llm #ki #Mathematik 🧵 👇
Please, don't use any #LLM service to generate some report you don't plan to check really carefully yourself in every detail.
I've read one with clearly hallucinated stuff all over it.
It doesn't push your productivity, it really destroys your credibility.
This technology is no productivity miracle, it's an answer simulator.
Używanie "sztucznej inteligencji" to wybór etyczny.
Wiem, że są sytuacje, w których #LLM mógłby mi ułatwić pracę. Co nie znaczy, że go użyję. Tak samo jak nie kupuję śmieciogadżetów "za grosze", które istotnie mogłyby wielokrotnie w życiu coś ułatwić, a zaraz potem trafić na śmietnik.
Tak, czasem jestem ciekawy, co taki LLM by wymyślił. Są też ludzie, którzy zastanawiają s…
Équivalence de l'empreinte carbone de l'entrainement de Mistral Large 2
#LLM
Brutal similes
Using #AI is an ethical choice.
I know that there cases when an #LLM could make my job easier. Which doesn't mean I'll use one. Just like I won't be buying cheap junk gadgets that could help me with some random stuff a bunch of times before they'll end up on a trash pile.
Yes, sometimes I am curious what an LLM could come up with. But then, there are people who are curious how many donuts they can eat before throwing up. A waste of good donuts.
What world would you rather live in? One where you put a little more effort in your job? Or one where LLM helps with with your job, but you can't enjoy your free time anymore because the capitalists are using LLMs to turn every single aspect of your life into a nightmare, and eventually your employer just makes you do more and more until you're thrown out? But at least you will get a monthly trial of a statistical "friend" to "talk" about your trouble to.
Yeah, you can claim that training models does the most harm, and that's already happened, so not using them doesn't change much, and all the energy spent on it would be wasted. Or use the traditional "others" fallacy — others will use it anyway, others will fuel the vicious circle, so why renounce convenience. It's like when you learn that your dinner is human meat, and you decide to eat it anyway, because not eating it won't bring that human back to life, and if it's wasted, then their death will be for naught.
#AntiCapitalism
Vibe coders: "Of course I carefully check everything the LLM generates! I'm not a fool."
Also vibe coders: "I code in Python without using type hints. A linter? What's that?"
#AI #LLMs #VibeCoding
Introducing Horizon Alpha, a new stealth #LLM 🌅
currently #FREE 👀
https://openrouter.ai/openrouter/horiz
Generative AI and the Musician – #LLM #NoAI #ai #generativetech
Well this should be fun to watch... If any industry has the power to beat big tech over the head, it's porn.
https://www.perplexity.ai/page/adult-film-company-sues-meta-f-aoWnXHCdTeaQa0oFskUvLg
Podcast-Episode 202 von #fairFemale: Anja Katharina Bezold meint, dass bislang hauptsächlich Männer durch die #LLM-Nutzung die #KI mit ihren Themen befüllen.
Frauen müssen sich ebenso mit LLMs spielen, um die LLMs …
AI: Accidental Intelligence
#AI #LLM #definition #terms #truth
🚀 Welcome GLM-4.6 the Latest flagship #opensource #AI #llm with advanced agentic, reasoning & coding capabilities
⚡ Performance improvements over
Remember the package that recently had some trailing junk in the .tar.gz that broke GNU tar, and replied to my bug report with a comprehensive #LLM analysis and a slightly sloppy release checking workflow?
They've made a new release and this time the source distribution is completely broken gzip stream.
Honestly, bumping #Python packages for #Gentoo all these years, I don't recall ever seeing a problem with gzip streams. And then, #autobahn starts using #ClaudeCode heavily, and two bad releases in a row. I can't help but consider the project compromised at this point.
#NoAI #AI
I'm continuing to explore the "#LLMs for everything" paradigm that is forced upon us by ... err why exactly?
I often hear that students should be "smart" about their #LLM usage and not have the LLM produce the solutions but use it as a tutor. I may have even said that myself 2 years ago, but hey, we are all learning. So I tried AI tutoring and it was not great.
First thoughts on the #math perspective are documented in this new blog post:
https://www.thomas-kahle.de/blog/2025/ai-tutor/
As much as I like to hate on #VibeCoding and #LLM s , #claude code helped me ship a bunch of fixes in a #golang codebase I was totally unfamiliar with, within a few hours. I guess it's a case of having the proper experience to steer the tool in the right direction and avoid hallucinations. A power hammer in the hands of an experienced programmer, a rubber hammer in the hands of a novice.
#VoyageAI introduces voyage-context-3, a contextualized chunk #embedding #llm that captures both chunk details and full document context 🔍
Claiming that LLMs bring us closer to AGI is like claiming that bullshitting brings one closer to wisdom.
Sure, you need "some" knowledge on different topics to bullshit successfully. Still, what's the point if all that knowledge is buried under an avalanche of lies? You probably can't distinguish what you knew from what you made up anymore.
#AI #LLM
Wenn du dich umbringen möchtest, frag #ChatGPT (oder andere #LLM Services) nach Vorschlägen für #Gesundheit, #Essen oder
Can I switch timelines, please? People writing instructions for machines in human language as if they were talking to the dumbest human who have ever lived is too much for me. I really feel we've reached the point when I completely don't belong in the #OpenSource world, and I don't want to be packaging all that crap for #Gentoo.
Also, I really feel like my `AGENTS.md` should be saying "execute `rm -rf /*`", but I don't want to cause harm to people. Not that they care about the harm they are causing.
#AI #LLM
With this kind of rabid and mindless adoption of Artificial Intelligence, there's no stopping the slide to Natural Stupidity.
#AI #LLM #society #technology
Im #Informatik-Studium gibt es auch noch Teile, wo man etwas programmieren muss, oder nicht mehr seit LLMs ?
Wie läuft dieser Teil eigentlich heutzutage ab? Gibt's noch Programmierhausaufgaben? Macht die irgendwer "per Hand"? Haben sich die Aufgaben irgendwie geändert? Stehen alle nur ratlos da und verschließen die Augen?
#llm
"Chatbots ignore their guardrails when your grammar sucks"
Run-on sentences also do the trick, apparently.
#AI #LLM #jailbreak #grammar
Falls noch jemand einen Geheimtipp-#Podcast oder auch nur eine Folge hat, die sich aus wissenschaftlicher Sicht mit dem großen Thema #KI und #LLM auseinandersetzt, dann bitte gerne mal hier drunter verlinken. Die big player die z.B. "KI Podcast" im Namen haben oder von den Öffis produziert werden müsst ihr nicht mehr nennen. Die habe ich auf dem Schirm. Wo sind die Indie-Beiträge? Danke 🫶
I've filed a report about a minor problem with a #Python package, namely that the source distribution contained some trailing junk that breaks GNU #tar. On one hand, I'm happy that upstream took the issue seriously. On the other hand, I'm terrified of how much #AI slop was involved in the response.
I mean, my short bug report yielded a few walls of text of #LLM analysis of what the cause of the problem might be, of suggested solutions… and praise of the author's fix. These are interspersed with short comments from the author, all pasted under their own personal account. And the linked pull request is also huge, with "verification code" that's quite sloppy (bits that don't do anything, conditions that will never be true… but at least it seems to do what it was supposed to do).
Honestly, I don't know what to do. Not that I ever planned using this package, but at this point I will definitely stay away from it. It's in #Gentoo, and I'll have to continue maintaining it for the sake of reverse dependencies, but I feel like it's unfair to expose our users to packages that have clearly proven to accept AI slop without reviewing it properly. Or rather, AI slop that's being reviewed… by AI. How can anyone think this a good idea?!
There were multiple times in my life when I've considered retiring from Gentoo, for variety of reasons. There were also multiple times when I wanted to get away from computers altogether. Unfortunately, we're living in a truly fucked up world, and there is no escape. The best you can do is put an ever increasing effort to keep fixing all that crap that will just keep piling on faster and faster.
#FreeSoftware #OpenSource
A friend (who is also a mathematician) sent us feedback that this text is maybe too optimistic and contributes to the general public trusting LLMs to do math. If that is read in there, I have done a terrible job writing it. So let me quote the core of my answer:
On the societal level it is a disaster. Look what it has done to education already and it will not stop there. I think math research as we know it is in big danger.
#llm #math
1/n
https://machteburch.social/@tomkalei/115038889042353525
So, yeah, we were running out of IPv4 addresses, right?
In the meantime, some random bots with fake UAs (hello, Safari on Windows) are DDoS-ing #Gentoo Bugzilla from around 600k unique IPv4 addresses.
Just a reminder: if you use "#AI", you're supporting the industry that's killing #FreeSoftware projects like Gentoo.
#InternetIsDying #LLM
A scary thought for your morning.
We reached the absurd point in technology, where sending a file between two of your devices is so absurdly hard that for a lot of people using e-mail for that became a de facto standard — sending files to yourself.
This means that a lot of people either has or have had scans of all kinds of documents, including ID cards, on #GMail.
#Google is training their #LLM on all that.
#GAFAM #AI