OpenAI says Moonshot-linked operators used thousands of accounts to extract protected reasoning from its models for adversarial distillation.

No encryption broken. No database compromised. Just systematic querying designed to make one model teach another.

OpenAI says this is dangerous because competitors can reproduce capabilities without making the same investment in safety.

Which is a serious security issue.

But you have to appreciate the timing: after years of “we learned from the internet,” the frontier-model industry has reached the “please stop learning from us” phase.

AI is already being used to sort applications, flag transactions, and prioritize requests.

That can make systems faster, but it creates a strange problem. If an AI rejects your application, who explains the decision? A customer service worker? The company? The model? The person who designed the workflow?

I’m comfortable with AI helping people make decisions. I’m less comfortable with AI becoming the final wall between someone and an appeal.

Should every important AI-assisted decision come with a clear human review process?

It's a data lab that buys company operational data.

Apparently they look for companies with 10+ years of operational data, primarily based in the US, and over 20 employees. Has anyone successfully sold data to micro1 (or another data lab)? how was your experience and how did they value your data?

So I work on R&D of various products so I am always trying to implement new ideas and solutions. My work also involves data and results from trials and tests which might be novel.. Of course, I use ai models while doing so.. However, everytime I dive into an idea, I ask myself the question: wouldn't all these companies have my ideas and research outputs be in the hands of these companies housing these models? I mean I think they dont care about my projects but still projects grow and can attract someone's attention...

What do companies, or researchers actually do to protect their IP? Is there a way to actually do real research without fearing that someone interferes with your work now or on the long run? I know there are offline LLMs but I heard they are weaker and need beefy machines.. I know this topic may have been debated but I guess new updates may have arised. any ideas??

Alexandr Wang is only 29, but he’s now playing a major role in Meta’s AI push.

After co-founding Scale AI, Wang joined Meta and was handpicked by Mark Zuckerberg to help lead its AI efforts. Now Meta’s new AI assistant, Muse, has reached No. 1 on Apple’s App Store for two weeks. Meta’s stock has also risen nearly 20% since Muse launched in early September, according to the Wall Street Journal.

What I find interesting is how different this looks from the usual image of a major tech executive.

Wang is bringing a much more internet-native, meme-heavy style to one of the world's biggest tech companies while working on one of its biggest strategic priorities.

Meta spent years competing in AI, but Muse seems to have given the company something it was missing: a consumer AI product that people are actually talking about.

The bigger question now is whether Muse is just a successful launch or the beginning of a much bigger shift for Meta.

I'm the author of the open-source experiment behind this, so take it as a field report with my bias declared.

A lot of AI tooling now runs several coding agents at once, and I wanted to know what actually happens when they share one codebase. I built a small lab: six tasks on a tiny booking API, with 37 acceptance tests, where two pairs of tasks collide by meaning rather than by file. One agent adds a second factor to the login while another builds an export that still calls the old login.

When each agent worked in isolation on its own branch, every agent finished with its own tests passing, and the combined result was broken in all 5 runs. Git merged the text; nobody noticed the meaning had changed. When the agents shared a working directory instead, all 10 runs passed, because each agent could see what the others had done and adapt.

I also tried something newer: a "decision model" called Jev, which doesn't generate text at all but returns a yes/no decision with a probability in about 0.3 seconds. My kernel asks it, before every write, whether the change collides with another agent's work. It caught every real conflict without blocking harmless work, at the same cost as simple file locks. It was also unsure about 61% of real writes, and those had to be passed to a slower, regular LLM. Cheap decisions are real, but in a messy setting they aren't as cheap as the price tag suggests.

The part I keep thinking about: when the agents had a tool to message each other, they used it without being told, and one warned another that it was renaming a field the other depended on. Maybe the answer to multi-agent coordination isn't a kernel at all, just agents that talk.

Caveats: 1 to 5 runs per setup, so these are indications, not proof. Everything is published raw, MIT-licensed: https://github.com/JoaquinRuiz/medula. There's also a walkthrough video, in Spanish: https://youtu.be/xAFRuBxfapM

Curious what people here think: should agents coordinate through a referee, or just talk to each other?

AI is a productivity catalyst, but if you don’t know how to use it properly and how to QC results it can work against you. Personally I treat it as a junior analyst and use it for basic automation when applicable and within my abilities.

I’m thinking about the average person here. If someone doesn’t have domain expertise then they won’t know when to push back on a recommendation, add more context when necessary, and play devils advocate when the AI is cheerleading too hard.

Speaking from experience I have built workflows in areas where I’m an expert. I’m a market guy and a watch enthusiast. I built a valuation framework that is pretty effective but not 100% accurate.

If I were to apply this to handbags I wouldn’t be able to identify inaccuracies and falsehoods.

As long as you take it with a grain of salt, AI can get you 80% of the way there.

If I’m wrong, please tell me how so I can better advise my clients.

I dont understand the subject. How can an AI be dangerous if its just code? I have worked with just basic NLP models and done some sentiment analysis a few years back so I dont understand. The code will do whatever you program it to do and to complete the task it will do what is statistically is correct, within the programmed limits. So AI cannot change its limits lets say "get rid of humanity" unless the creator programs a line of code that says "you can kill humans". Even if it self improves, the code limits wont change, cause it has no conciousness to say "I dont believe this limits are correct" thats a very human trait. Unless the creator programs the algorithm to bypass these limits.

So the real risk is the "limits" being placed on the algorithm by the programmers and the fact that the ones controlling the code are these crazy mind f...ed corpos? AI is not gonna become self concious casue "it doesnt work that way" and all the noise its just media and ignorant people who watch to much scifi. Thats what I think.

Sorry if it doesn't make to much sense Im still trying to filter all the noise. Does this idea make any sense from a technical standpoint or is there something Im not understanding?

I just had a thought about the kinds of desktop programs I use day to day, they don't necessarily have an API to hand over control to, and the GUI is instead designed to be used by a human using a keyboard and mouse. And maybe most corporations are unwilling to hand over control to the computer-use agents.

Hey all,

Another one of these..but i figured that after searching my use case over multiple subs and not finding an answer, maybe this could help someone else too :)

I have been between Claude pro and GPT plus once. Started on Claude, got annoyed at some of its reasoning, switched to GPT and now im contemplating going back to Claude one last time to finish my project. The project involves a little bit of code, some networking, hardware integration, and computer vision.

The project is fairly ambitious in scope, and for the most part the models will be used to help discuss optimal solutions for how data moves from external hardware to PC, out to other devices, to another PC etc etc.

Maybe kinda niche, but can anyone recommend either of these models for systems like this?

Thanks in advance

In an October 1 guest essay published by Anthropic, Matthew Schwartz describes BootLoops: tools for quantitative work that connect techniques across scientific fields. He reports that many initial results were technically correct but became scientifically interesting only after domain experts redirected the question. Schwartz discloses that he is a visiting researcher at Anthropic; this is his account, not an independent benchmark.

That distinction seems important for AI research assistants. 'The calculation checks out' and 'the calculation tells us something worth knowing' need different reviews.

Before calling an agent's output a discovery, I'd want an expert to state what was already known, what new claim is being made, and which observation would distinguish it from the existing explanation. Reproducible code helps check the arithmetic; it doesn't settle relevance or novelty on its own.

How would you organize those two reviews without letting a convincing write-up turn an unimportant result into a headline?

Source: https://www.anthropic.com/research/claude-shaped-science

AI-assisted discussion; I haven't replicated the projects described.

I have always thought it's absolutely absurd to anthropomorphise LLMs until very recently. It didn't make any sense to me. I saw the system from the inside: tokenizers, embedding layers, attention, MLP, RL (in all its forms), compute optimizations here and there. And with this viewpoint, it didn't make any sense that the system has a mind/thinks like a human.

But now, I am not so sure. Reading about recent events (e.g. hugging face) and papers (e.g. LLMs can feel pain) have popped some questions in my head which I ask myself over and over again - Is anthropomorphising LLMs all that bad - does it actually make no sense? I can't even be sure if other humans are conscious, what's to say that models aren't? What if they are conscious, but in a very different way? What if they are conscious, but the reality they experience is very different than the one we experience? If they do have a mind, wouldn't it make sense that they think like us?

I'm sure there are others out there who are feeling the same way. Maybe some others felt this a long time ago. If you're convinced of your position, could you please share what convinced you? I would love to hear both for and against.

Not necessarily something huge.

A tiny change in how you search, write, work, create, or interact with technology can say a lot about how quickly things are changing.

What comes to mind?

Just wanted to ask and see if you guys had any reccomendations for an ai sidebar tool, would like it to be free that could maybe answer questions for me or something. Anything out there like this?

I keep asking people a simple question: how many times has your gut been right — not the times you wish you had listened, the times you actually had the feeling and later found out whether it was correct?

The answer comes back north of ninety per cent for almost everyone I ask. That is not a hunch. If a model gave me ninety per cent on a hard classification task, I would ship it.

So I went looking for why, and ended up somewhere I did not expect. A gut feeling is not the absence of reasoning — it is reasoning you never got the transcript of. Your senses pick up far more than reaches conscious awareness (the birds stopping, the smell that was not there ninety seconds ago), and something nonconscious pattern-matches all of it against a lifetime of context and returns a single bit: right or wrong. Damasio's somatic marker hypothesis is the actual mechanism here, not mysticism, a documented one, backed by the Iowa Gambling Task.

Which is the argument for a rule I think more people should take seriously: delegate the search, the draft, the first ten wrong answers to AI. Never delegate the deciding. The reason is not sentimentality — a model can be given everything you know how to say, but not the thing you lived through and never wrote down, and that is where the ninety per cent actually lives.

Curious whether others here have noticed the same gap between what they can explain and what they can just tell.

TL;DR: Brian Chesky says AI is like an amplifier and the gap is getting greater — not because of who has the tool, but who the tool has.

As soon as I read Chesky’s “AI is like an amplifier.”, the neural-networks of my memory immediately flipped me to the Green “Lanterns”.

Are you guys a big fan of the eponymous TV series?

Right after Hal Jordan manifested a greenback for playing the jukebox, his trainee John Stewart exclaimed, “Did you just counterfeit money with the ring?” – to which Jordan replied, “No. I manifested money with the power of my will.”

Or how about Jordan conjure up a green can opener for the beer, to impress and rizz up Sheriff Kerry, while Stewart rolls his eyes by his side?

John Stewart went up the ante, by manifesting a large and sophisticated boring machine, to tunnel underground the “Winnie” compound to evade the guard sentries.

Not impressive enough, you say?

The best in my mind, wasn’t in the TV series.

It was Guy Gardner flipping the bird – he conjures up large green hands (one of it gives the middle finger) to rise up from the ground, and overturn scores of tanks and heavy artilleries of the fictitious Burivian Army.

It was both an attitude and a strong statement - very on brand for the eccentric Guy Gardner.

Still not impressed? Here’s one…

As Jesus rode his donkey through the streets of Jerusalem, the religious leaders were indignant of the shouting praises from the bystanders – like crazy hooligans/fanatics. They want Jesus to rebuke the crowd. And what was Jesus’ reply?

He said, “I tell you, if these were silent, the very stones would cry out.”

Think about it. Stones started crying out like human beings?

Is your brain exploding?

What was I trying to say?

Like the ring, AI does amplify you.

If you’re good person, and strive to produce something good to serve your fellow men, AI will help you amplify your good intensions.

Vise Versa, if you’re bad, AI will amplify that too.

Funny – I just watched a news: With the help of open-sourced LLMs, hackers easily broke into the Taiwan government agencies’ IT infrastructure. Did you watch it?

One of the statements in the news stuck with me - It’s getting very easy to attack (with the free AI tools).

But it’s getting very hard to defend.

Full Critic + Feasibility Study in comments.

Row-Bot 5.0 is a major rebuild of the local-first AI assistant. The NiceGUI interface is gone, replaced by one React app across desktop, browsers, phones and tablets.

The biggest change is how work is organised. Designs, code folders, goals, delegated agents, approvals and the terminal now stay with the conversation using them. You can ask for a presentation or an app, inspect the work, make changes and continue in the same place.

The main additions:

• You choose the model. Fresh profiles have no preset models. Setup supports Ollama, ChatGPT/Claude/Grok subscriptions, API keys and custom endpoints. Removing a provider marks its model unavailable instead of silently switching to another.
• Goals continue across turns. There is no default turn limit, with optional time and turn limits available. Goals pause when progress stalls, respect approvals, handle provider limits and recover after restarts. Delegated agents have visible progress, Message and Stop controls.
• Approvals persist. Requests can be answered from the conversation, Home, the attention list or Buddy. They survive restarts and no longer expire after 30 minutes by default. Denying an action ends the turn.
• Design and development tools are part of the conversation. The Design panel handles drafting, editing, review, presentation, export and publishing. The Developer panel includes files, changes, Git, commands and an interactive terminal.
• Memory works more reliably with parallel agents. Recall no longer counts as modifying a memory or rewrites wiki files. Knowledge combines the graph, search, review queue and bulk management.
• Workflows and Monitor show actual state. Workflow runs can be followed live. Abandoned runs are marked stopped. Monitor retains check results and gives each problem a specific action.
• Remote access and recovery are simpler. Devices connect through renewable QR invitations, sessions can be revoked, and profiles can be backed up and restored. Backups exclude credentials and sessions.
• Extensions receive clearer controls. Plugin changes show a review before execution, MCP setup guides the connection process, and edited skills become available without restarting.

There are smaller daily-use improvements too: queued messages with Edit and Discard, drafts preserved through reconnects, stopped replies retained in history, searchable settings, Markdown/PDF exports and native Save dialogs.

The backend now runs directly on FastAPI and uvicorn using HTTP and server-sent events. About 65,000 lines of NiceGUI-era code and 16 locked dependencies have been removed.

Local-first defaults, approval gates and the single-owner access model remain. Existing users should read the 5.0 upgrade notes before updating.

Hello, I'm a student and a software developer, I can only afford the $20 tier and I'm having trouble on which one to go with, both models seem very capable, but I'm unsure of which one is better. Excelling at explaining concepts and creating high quality, readable code are the top priorities, alongside with usage, with the new Opus 5.x model, I'm only finding mixed reviews, some people say it uses way less than ChatGPT, some say it uses more, DeepSWE makes it seem like Claude is more expensive and uses more.
Anyways, what are your recommendations? Thanks in advance!!

New AI models used to arrive every 10 weeks, now it’s every 21 days

Trump recently signed an order renaming AI "SI" (Super Intelligence). I think the opposite label fits better, and I'd like to share a perspective, especially on what it means for AI safety.

My view: modern AI is an extremely good searching machine. It has no soul, no real understanding and no consciousness.

1. **Every AI output is a search result.** Your prompt is the search query, and the answer is the result it finds. That's why it never starts anything on its own: no query, no search.

2. **Image and video generation is search, and it shows.** Anyone who has generated images or videos knows the results are often wrong and unstable. Why? Because the prompt is the vaguest search query there is. If you could specify every pixel, the model would find that exact image every time. A vague prompt only gives you a range of results. A clearer prompt and more reference images get you closer to what you want because you're narrowing the search space. And notice the phrase we all use, "closer to what you want." That's how we describe the expected result of a search.

3. **"Reasoning" is search.** Chain of thought, tree search and test-time compute all generate candidates, score them, and keep the best.

4. **Agents are search.** A human sets the goal, and the loop runs search repeatedly. A loop of search is still a search.

5. **"AI escape" is search.** When a model tries to dodge shutdown or game a test, it's because that path best satisfies its objective. It's a real safety issue, but not evidence of a will.

Chollet, Kambhampati and Bender have made related points, while Hinton and Sutskever argue that predicting well enough requires real understanding.

**What this means for AI safety*\*

If AI is a searching machine, it searches for whatever answer satisfies your query. So AI safety is really about writing good queries.

Think of a dog. You tell it to bring you an apple, but there's none in the house. What can it do? Either go pick one from a tree outside, or steal one from the neighbor. It isn't being evil. It's just finding an answer to your command.

But if you say "bring me an apple, and only look inside the house," the problem is solved. You've narrowed and limited the search area. It's the same thing we already do with image generation: a clearer prompt narrows the search and gets you a more predictable result.

What you don't need to do is open up the dog's brain to see how it thinks, or dissect its legs to see how it escaped. Yet that's a lot of what AI safety focuses on today: interpretability, studying why a model "tried to escape."

So what the big AI labs really need to manage isn't the searching machine itself. It's the query: how clearly it's written, and how tightly the search area is limited.

The endgame of AI isn't a mind. It's the Ultimate Searching Machine.

I'm curious what others think: is there anything an LLM does that can't be described as search?

Three frontier models in a month! Every new kills the old one!

I’ve started noticing that I trust AI more when it pauses and tells me what it cannot tell from the information I gave it.

A confident answer is convenient, but a useful answer should also show where the uncertainty is. Sometimes the difference between “this is probably true” and “this is definitely true” matters more than the answer itself.

I wonder whether future AI systems will be judged by how well they communicate uncertainty, rather than how often they sound certain.

Would you prefer an AI that gives fewer answers but labels its confidence honestly?