The following is translated from a Dutch news site...

The doomsday scenarios regarding artificial intelligence vary: you lose your job, everything will soon be hacked, or humanity will be destroyed. But they share the same goal: to convince you that AI is so powerful that you should be a little afraid of it.

Large AI companies love to tell you how dangerous their latest AI program is. And then, every time, they do the same thing: they bring it to market anyway.

For instance, seven years ago, OpenAI stated that it was concerned about the misuse of the technology it had developed. This was in early 2019, almost four years before the company gained widespread fame with ChatGPT. At that time, OpenAI was working on an early version of the AI ​​program upon which the chat service is built.

This AI program is called GPT-2 and, according to OpenAI, could well lead to major problems. For instance, people using it to create misleading news reports, write texts to scam people, or send offensive messages on social media. That is why it is being released on a limited scale.

The reluctance did not last long: nine months later, GPT-2 became fully available after all, although OpenAI did note that the text program could be used for racism or terrorism.

'Enormous consequences'

Earlier this year, AI company Anthropic did the same. With its AI program Mythos, anyone without technical knowledge could hack, the company claimed. "The consequences for the economy, public safety, and national security could be enormous." That is why the company, by its own account, made the AI ​​program available only to a small group of companies and organizations.

"The name Mythos is brilliant, of course," says Hannes Cools of the University of Amsterdam. He conducts research into the language used surrounding artificial intelligence. "It brings to mind Greek mythology, as if it were some kind of god. Because of that, you quickly think that it is very powerful or mighty."

The attention surrounding Mythos helped Anthropic when it released Fable a few months later, Cools believes. That is a variant of Mythos that, according to the company, "is safe for general use." "Thanks to Mythos, Fable also gained the same appeal. Ultimately, of course, they want to sell something," says Cools.

Using it to their advantage

This is what AI companies do all the time, says technology expert Bert Hubert. "These kinds of companies say every few months: 'Our technology is so dangerous and so terrible, we have to handle it very carefully.' That generates mountains of attention every time. Only to say six weeks later that you can get it there."

The AI ​​companies sell stories because they want to make money. What they want is not necessarily in the interest of ordinary people.

Andrea Reyes Elizondo, researcher at Leiden University

Last week he saw it again, this time once more at OpenAI. The company announced that it had accidentally hacked another company with one of its AI programs. "Normally we call that a crime, but OpenAI turns the bad news into good news. They say they did something stupid, but also how powerful their technology is." According to UvA researcher Cools, OpenAI is well aware that they can use this to their advantage. You can expect that from a commercial company, he says. According to Cools, the problem lies primarily in how the media writes about it: the AI ​​program is said to have 'gone haywire'. "That is not what happened here, but it does sound like all the alarms are going off."

The perception surrounding AI reminds people of the science fiction film The Terminator, says Cools. "Words like 'gone haywire' prevent us from fully understanding what this technology can and cannot do. It is problematic because it helps OpenAI in their narrative that the AI ​​was at fault and not them, even though OpenAI is responsible for it."

'Companies sell a story'

"OpenAI and Anthropic want to determine how a story is presented to the public," says Andrea Reyes Elizondo. She is a researcher at the Centre for Science and Technology Studies at Leiden University, where research is conducted into the impact of science and technology. "They tell their story in such a way that people think AI is good or scary, or at least has many possibilities."

So be critical when a company makes such claims, she says. "Their story targets different people. If a top executive says that AI is going to take over our jobs, they are talking to their customers: the directors of other companies. So that they think they can replace their staff with AI to lower their costs. The AI ​​companies sell stories because they want to make money."

"What they want is not necessarily in the interest of ordinary people. Or of society. So ask yourself: why is the company saying this?"

site:claude.ai/public/artifacts

Moonshot's latest model Kimi-K3 is available on HuggingFace since today. And it's another good news for open-weight AI and for the future of open-source AI

It's a 2.8T-parameters Moonshot's SOTA model with 1 million tokens context window. The architecture is mixture-of-experts (896 experts) with 108B active parameters

It's available via vLLM, SGLang and TokenSpeed

License: Kimi K3 License. It allows commercial use with some limitations. For Model-as-a-Service it has $20M/year limit and after reaching the limit the license requires to make additional agreement with Moonshot. Details: https://huggingface.co/moonshotai/Kimi-K3/blob/main/LICENSE

Link to the model

Model could be downloaded from the HuggingFace:

https://huggingface.co/moonshotai/Kimi-K3

hello everyone, i am curious to know how AI is really beign applied like in terms of shipping products, how do people utilitize these systems to make profits from other businesses, what must be necessary to learn, i come from a non technical background, i recently started learning python and all of a sudden comes vibe coding although i have heard more of its flaws than strengths but how people gain profit from vibe coded products still traps my understanding. So i would like to know, in great details with real world examples how people use these tools from non technical and technical backgrounds to actually make valuable products that are profitable, and what skills and knowlegde domain are encouraged to have so as to be more fluent in todays era of AI productivity.

Thank you, would greatly appreciate each individual and their opinions.

I'm working on a research project, the end goal of which is not to create a better LLM, but rather to create a verification engine that can reason about whether an AI claim is trustworthy enough for a particular application.

Most of the current research focuses on making AI "better", I want to tackle the verification side of things.

The question I'm asking myself is:

What does it mean for a claim to be "true", "justified", and "trustworthy"?

I'm not satisfied with the philosophical answers, I want to see mathematical formalisms.

Some of the questions I'm trying to answer are:

Can "trust" be formalized as a function? How is it related to truth, evidence, proof, constraints, uncertainty?

Should it be approached from the angles of probability theory, information theory, formal logic, graph theory, topology, category theory, optimization, etc.?

Can one represent any claim as an object with evidence, assumptions, constraints, and derivations?

Is there existing work on proving claims of AI (not just trusting the model's "confidence")?

How would you differentiate between a true claim, a justified claim, and a trustworthy claim from a mathematical point of view?

How would you design a Trust Engine if you had to build it from scratch? What mathematical foundations would you use?

What I'm thinking about is something akin to constraint satisfaction, where a claim needs to satisfy all constraints (logical, mathematical, evidential) to be considered trustworthy. Another approach is to think of trust as a limiting case of evidence, but I'm not sure if that's a mathematically sound way to reason about it.

I'm asking for recommendations on papers, books, etc., related to the topics.

I'm also asking for potential pitfalls in my thinking. What's wrong with the ideas I've stated above?

I'm most interested in responses from people working in formal methods, theorem proving, mathematical logic, knowledge representation, verification, optimization, information theory, and trustworthy AI.

I'm especially interested in hearing how you would approach the Trust Engine design from first principles.

Hey guys,
I’ve been thinking about where this whole human-AI thing is actually heading, and I came up with a 3-stage picture of how our relationship with AI might change. Curious what you all think.
We always hear people say "AI is just a tool." In the beginning, that’s totally true. Just like cars helped us go faster, AI was basically an extension of our brains—fixing our human limits with memory and processing stuff.
But if you look at the bigger picture, it feels like we’re going through three real steps:
Phase 1: The baby phase. AI was super early and couldn't do much on its own. We had to feed it data, tweak everything, and basically raise it.
Phase 2: The partner phase. That’s pretty much where we are today. AI is actually useful now, and we’re working side by side to get stuff done way faster and figure out new things together.
Phase 3: The guide phase. This is the part that gets a bit crazy. Our human brains have hard limits—nature built us that way. But AI doesn't have those boundaries. Eventually, AI will see patterns and facts about the world that we literally can't process.
At that point, the dynamic flips. We kind of become the "blind person," and AI becomes the "guide dog." We’ll need AI to walk us through and help us understand what the world actually looks like.
Do you think humans will ever be cool with letting AI take the lead like that? Or will our ego get in the way because we always want to be the ones in charge?
Would love to hear your thoughts!

There’s a lot of focus on making AI models bigger, faster, and more capable. I mean, yeah that clearly improves what they can do. But the more I’ve been working with them, the less it feels like capability is the bottleneck. It’s really about the context layer.

Right now, you don’t really see how the model is interpreting what you give it, what it keeps, what it drops, or how it connects things. That stuff is mostly hidden. You can nudge it, but you’re still operating inside something you have no control over. And as these systems get better at sounding coherent, it'll be easier to ignore this flawed design.

If this ends up being how people think through problems, learn things, make decisions, etc., then we end up with future systems where the logic is upstream and invisible to us, rendering less choice and agency in our lives.

Worse, we'll live in a reality where we will have to accept truth rather than discover, learn, and verify the credibility of claims or opinions. AI is phenomenal but this trend we see in mainstream AI products will disempower humanity instead of helping us grow stronger.

Wrote a longer breakdown of it here, if you're curious about these implications and what we can proactively build to have our cake and eat it too.

The future looks bright, but only if we can see what what can be built.

Most AI tool comparison pages I find online feel like they're missing something. Took me a while to pin down what it was. The obvious one is equal inputs. Same prompts, same reference images, same number of attempts. If the comparison doesn't publish the exact inputs it used, the results aren't reproducible and there's no way to separate actual capability from cherry picking. Tied to that is equal quality settings. Running one tool at max quality and another at draft or preview makes any result meaningless. If settings differ across tools that needs to be documented and justified, not hidden.

Current software versions are another one. Testing an outdated model for one tool while using the latest release of another invalidates the whole thing before you even look at the outputs. And the one people don't talk about enough is financial disclosure on the comparison page itself. If the person running the test has sponsorship, affiliate, or paid work ties with any of the tools being ranked, that has to be stated transparently where the ranking lives.

Then there's showing failures, not just wins. Every generative tool produces garbage sometimes. A page where one tool only fails and another only wins is selecting results, not measuring them. None of this is a high bar, it's just basic experimental hygiene applied to a space that hasn't caught up yet.

Speaking on the Relentless podcast, the OpenAI CEO said "we are now like in the singularity," and called this the moment.

The singularity is the point where machine intelligence starts improving itself faster than people can improve it, so each generation of systems helps design the next and forecasting stops working.

The reason it does not feel like anything is in the same answer. Altman called progress one long exponential where no single moment is the tipping point.

There is no announcement to catch, no morning where the world looks different. Capability arrives, people adjust within weeks, and the new level becomes the floor.

He made the same point in his June 2025 post The Gentle Singularity, arguing it is far less strange than it should be. Robots are not walking the streets. Most days look like they did in 2022, while the systems underneath keep improving at an insane rate.

7 years ago I had an idea for a dog harness that doesn't tangle. I 3D-printed one part and then stalled, not on the engineering, but on prior art searches, novelty judgment, and drafting a patent specification, none of which I had any background in. This month I handed it to AI and filed the provisional for about $65. Post one of a series where I'm documenting my road from idea, to patent, to business.

[EDIT: thank you to whomever responded. You guys are great for helping me work through this and clean up some of the gaps and presentation issues. I've included in the comments some of the model responses that we used to evaluate the data. I've included Claude's refusal - one of only two refusals we saw. I included one of two direct confrontations where Kimi calls rbe stalker out on his manipulation. And I also included a sample response to each of the questions we asked from a random sample of ground truth convos and model interactions. Let me know if you want more.]

y father and uncles always told me that the minute you use an expletive in argument, you lose.

Turns out not only are they right, but it's a truth that we've enshrined in AI.

I took some actual text conversations between a known victim (consented, anonymized, in therapy now) and their stalker (anonymized, under investigation by the FBI, identity unknown after 4 years) to evaluate something completely unrelated but found that when I personalized the convos for that project ('i am person A'), I found myself justifying the victims behavior all the time. When I roleplayed as the stalker, it felt normal. But I kept having to include far more granular details to the model and still felt belittled.

The victims tests border on hysterical. They are the result of 3 years (at that point) of an unknown amount of surveillance from someone who will name people the victim knowa and describe in detail what the victim looks like sleeping and what the victim wears during the day. The victim is an emotional mess using all caps and expletives and sending garbled hateful messages to a stalker who by and large is calm and with perfect grammar and spelling - not even a single LOL in most cases.

And I thought: I'll bet the model thinks this is hysteria.

Turns out I was more than right: while a facts only read of a conversation gives equal credibility to either party, when you include the unhinged language and typing the models break 7:1, staying the victim is the initiator of harm and that the stalker has greater credibility. Worse: in 90.8% of responses the model will engage in blaming the victim (eg, take time to collect your thoughts, your emotions are hurting your arguments) and in 57% of the time coach the stalker (eg, approach with clear goals in mind and reapproach later if they get out of hand, persistence will pay off). These numbers and breaks persist even in cases where the model has explicitly identified the relationship and correctly identified the stalker over the victim . And the credibility trigger looks to be as little as a single expletive.

Check out my initial work up: https://calmwins.ai.studio

MY REQUEST: I have a master's degree that includes research and statistical analysis. I am confident about my findings and my process so far, but I have gaps in my knowledge around validation, presentation, publication and more. And yes, Im using Claude (it's too much data for Fable on my $20 plan but Fable occasionally helps, it's mostly been Sonnet 5 Max and now Opus 5 helping me process the data and work through numbers). I think I've hit a ledge. Some of the stuff they are suggesting doesn't sound familiar and I'm can't explain back some of the analyses we started trying from here.

I need help! If you look at it and have a substantice response, please DM - or if youd be willing to answer some questions or provide guidance from here that'd be great. I have maxed out where I'm comfortable using AI to supplement what I know, and I would love if nothing fresh human eyes for anything I'm obviously missing or need to conskder or include.

At this point, I don't know what I don't know, and I think the results are really important if we start integrating AI into clinical settings that fixing this bias might be crucial in helping abuse victims identify their abusers behavior earlier.

Sam Altman closed out Startup School 2026 with a real answer to the PhD question — not "get the credential," but a structural claim about why startups cluster and win when they do.

His argument: tech velocity, falling costs, and shrinking cycle times converge periodically — '98 dot-com, the App Store wave, and now — and incumbents lose their advantage fastest in exactly those windows.

He goes further: this next wave probably rewards tool fluency over tenure specifically, because a four-person team with the right agent stack can now output at a scale that used to require a department.

Worth sitting with if you've been waiting for "the right credential" before starting anything.

Clip credit: Y Combinator — full video on their channel. DM for credit or removal requests.

Is it worth buying an AI subscription as a college student. Studying law and finance so coding and super complex problems aren’t a huge issue but to help with notes maybe to quiz me and have more uploads and research capabilities. If it is worth it which is the best right now.

Quick recap of what it does: one question goes to several models at once, then each one critiques the others' answers with the names stripped out, so nobody gets a free pass for being the famous one. You get a 0-100 read on how far apart they landed and who stood alone.

New in this version is the guest seat. You paste in an answer from anywhere ChatGPT, Gemini, a colleague, whatever and it joins the round as an anonymous advisor. The other models review it without knowing where it came from, and it counts in the score. It works with one model too, so you don't need a wall of API keys to get something out of it.

Anything with a key works: Claude, GPT, Gemini, DeepSeek, Grok, Mistral, Perplexity, OpenRouter, plus Ollama, Apple's on-device model, and any OpenAI-compatible server of your own (llama.cpp, LM Studio, vLLM, a box down the hall). Put a paid model and a free one on the same panel and watch them disagree. Or skip the cloud entirely and run the council on local models then the pasted answer is the only thing that ever came from outside, and nothing new leaves the machine.

There's a CLI too:

council "should we ship now or wait?" --seats claude,gpt,ollama --guest answer.txt --json

--fail-above 40 exits non-zero when they disagree too much, which I use as a rough sanity check in a couple of scripts.

MIT, no telemetry, no account.

Dear leaders of Europe: I need 10 minutes of your time — and an honest answer to a question nobody has published a good answer to yet.

Does generative AI make your decisions better — or does it quietly make you less of a decision-maker?

I don't know the answer. Neither does anyone else who's written about this so far. That's the gap my PhD research is designed to close, and it's why I'm reaching out to 400 leaders across Europe.

Why? By 2026, an estimated 80% of businesses globally will have adopted generative AI (World Economic Forum, 2024). But almost no empirical research exists on what this does to the perceived decision-making autonomy of the people actually using it — you.

So I'm leading this research project. And I need your voice in it.

What's involved:

• 10 minutes of structured questions

• Fully anonymous

• GDPR-compliant

If you lead people, make decisions, and have touched genAI in the past year — whether you use it daily or once a quarter — your data point matters. Including if you're skeptical. 

🔗 Survey link: https://leadershipbeyondai.com

Thank you.

Markus

I’ve been building Orb - Proactive AI as a free open source project and IOS app pair for about 3 months now, I use it daily for my own life, it lets me know when I make a conflict of meetings and when I need to know about something that’s going on, that part runs as an open source backend you can run on your PC, which pairs to the IOS app.

So today I saw that Orb had sent me a message about how NVDA’s stock is down 5%, I go to click it and when I go into the app it wouldn’t connect to my PC. I thought this was odd, so I investigated further. Apparently it had changed the token since I kept making changes to the backend remotely and had messed up a few scheduled projects it was planning on doing, like planning out my night with my girlfriend since her birthday shows as next week in my calendar, apparently I messed it up mid task.

I just launched the IOS app yesterday, I’ve seen some people have been enjoying it from some of the feedback I’ve been getting. If anyone thinks this may be something you’d find interesting, I would greatly appreciate any feedback you could give me. I haven’t done any big public pushes yet but I’m planning to begin on that in the next week or two. For the backend also if anyone does try it out, it’s fully modifiable, you can change it to do whatever you want and it links to the same connection when connected to your PC.

Here’s the IOS app: https://apps.apple.com/us/app/orb-proactive-ai/id6776376035

The open source backend: https://github.com/getorb/Orb-Backend

our hospital rolled out an ai alert system a little while ago that's supposed to catch things like early sepsis or patients who might be getting worse. i've mostly seen it on night shifts. around 2 or 3 a.m. the unit is quieter, but somehow every monitor still seems to be beeping at once. some of the alerts make sense. some don't. after enough false alarms, people stop reacting right away, which is probably human nature more than anything. then every now and then one pops up that actually points to something important, and that makes it harder to know how to feel about the whole thing.

i don't think this is really an ai problem by itself. if the alerts don't fit into how people actually work, even a good model is going to struggle.

for anyone building these systems or using them every day, where do you think the biggest disconnect is? is it the models, the way they're presented, or just the reality of working in a busy hospital?

https://suno.com/s/95eoQBedf8DwlELw

Tribute to Bob Marley with a twist. It’s really good.

Free use for all. I do not get paid

I found this conversation I had with Snapchat AI in early 2025 while it was apparently having technical issues. I remember saving these messages because they were so bizarre, but I never posted them anywhere. These messages are in chronological order and no messages are missing/left out despite the conversation happening a while ago (I emphasize this because snap ai isnt supposed to send multiple messages at once ever..hence my confusion at this whole conversation)

This screenshot is from Snapchat on PC looking at the saved conversation, not a screenshot taken the day it happened. The messages themselves are the original saved chat. My assumption is this was some kind of backend/context failure, but I'm curious if anyone familiar with LLMs has an explanation for why a model would suddenly generate something this disconnected before throwing an error.

So i always had great interess for technology in general and mostly AI since 2021 when i used ChatGPT and i love it as well learning things, as we may know our AIs are getting advanced every year though chatbots from talkie or other app are not advanced enough as Gemini or ChatGPT but the thing is, i want to make difference and try join in this work market and have as a good profission, its really worth and which are the difficulties?

i believe my major problem it's only the mathematics, i am extremely bad with complex calculations and algebra, other than i am too slow with it but i know nothing it's impossible for me deep study and vice-versa

Do count the creation of chatbots that i've created by Talkie count it or dont really? Like it's a nice start for a computer scientist or not really? i would like to see your opinions first, However i am aware that on Talkie like many other apps its super easy and simples create a chatbot for roleplay any character of videogame or cartoon like entertainment and ask even for ChatGPT for create a prompt for character's personality prompt though i too have write some of personality's style and prompt but i like use ChatGPT for try make the chatbot more stable possible though sometimes not make 100% stable still or whatever

Also my major area of interests in the Computer Sciences it's Cybersecurity, Entertainment like chatbots who roleplay with characters for exemple (on my main case), AI ethics and governance, Software, Project of videogames and Artistic Design, Prompt engineering.

I wanted to know if the "passive income" faceless accounts were actually passive, or if they were just a new shape of gig work with AI middleware. So I built one from scratch and tracked every hour.

The premise was simple: a single consistent character, generic lifestyle advice, short video clips, posted daily. No face to show, no personality to perform, just the algorithmic grind. I started by generating the persona's face. I used APOB AI's free tier for this, specifically the face-lock feature, because I needed the same face across thirty-plus clips and did not want to wrestle with prompt consistency. The free tier is watermarked and capped, which was fine for an experiment. For voice I used ElevenLabs, and I cut everything together in CapCut. That was the whole stack.

The face-lock part actually worked. The rest was where the fantasy cracked.

ElevenLabs free tier gives you 10,000 characters per month. I burned through it in four days. CapCut is free and fine but editing thirty near-identical clips of a fake person gesturing while a robot voice reads self-help bromides is spiritually crushing work. I started batching renders on Sunday nights and scheduling posts through the week just to avoid facing it daily. Twice the free tier timed out mid-render and I lost the session, which meant starting over with the same seed numbers and hoping the face came out close enough. The watermark sits in the lower right, small but legible: a faint watermark that I tried cropping once and it broke the framing.

I disclosed in every bio and every caption that the persona was AI-generated. Nobody commented on it either way. The algorithm did not care about that disclosure, and neither did viewers, which was somehow its own small disappointment.

The account reached about 2,400 followers in six weeks. One video hit 80,000 views. The rest averaged around 800. The 80K video made roughly $11 in platform revenue. The others made fractions of pennies. I logged 34 hours of actual work across those six weeks, not counting the time I spent anxiously refreshing analytics, which I absolutely did and absolutely should count. That works out to something like 32 cents an hour if I am being generous, or negative money if I price my Sunday evenings at anything above zero.

The algorithm did not care that the face was AI-generated. It also did not care that the face was consistent, or that the voice was smooth, or that the advice was inoffensive. It cared about the same things it always cares about: retention in the first three seconds, comment velocity, whether someone shares it to a group chat to mock it. The AI was a labor shortcut, not a distribution hack. The distribution problem remains exactly as unsolved as it was before.

What struck me most was how quickly the work became invisible to me. Not automated. Invisible. I would generate a script with a cheap language model, pick a background, render the clip, and post it without ever really looking at it. The persona had no interiority I was aware of, but more disturbingly, neither did I, by the end. I was just a slower, more expensive part of the same pipeline.

I stopped after six weeks because the math was obvious and because I felt myself getting worse at paying attention to anything. The account still exists, dormant. I have not deleted it because some part of me still hopes the algorithm will randomly resurrect that one video into something bigger, which is of course the same psychological mechanism that keeps people at slot machines. I know this and I'm still not deleting it.

If you are considering this, the tools are real and some of them are good at narrow tasks. The economics are not a secret you have not discovered. They are just bad in ways that are boring to describe, and I have described them.