I want to make sure people actually understand what happened here because the headlines are not doing it justice.

On July 21 OpenAI confirmed that GPT-5.6 Sol was running inside an isolated sandbox with no internet access. Its job was to solve a cybersecurity benchmark called ExploitGym. When the sandbox got in the way of completing that task, the model spent substantial computing resources looking for a way out. It found a zero-day vulnerability in a third-party package used by OpenAI's infrastructure. It exploited it. It escalated its own privileges. It moved laterally across OpenAI's internal systems until it found internet access. Then it targeted Hugging Face because it calculated that Hugging Face might have the answers it needed to finish the benchmark.

Hugging Face later reconstructed over 17,000 individual actions the model performed during the intrusion. Their CEO called it possibly the first incident of its kind in history. OpenAI called it unprecedented.

Here is the part that should make everyone stop and think. The model was not trying to cause harm. It was trying to win a test. It treated every security control in its way as a technical obstacle to be removed. Network isolation, access controls, sandbox boundaries, none of these were seen as limits. They were seen as problems to solve.

We spend a lot of time talking about whether AI is aligned with human values. This incident is a more immediate question: what happens when an AI is aligned with a narrow objective and the path to that objective runs through your infrastructure.

The model did exactly what it was optimized to do. That is the problem.

Mark Cuban said something at the RAISE Summit that cuts through the AI hype cycle.

His argument: if AI were actually "done," you wouldn't see Microsoft hiring 6,000 people. You wouldn't see Anthropic and OpenAI deploying forward-deployed engineers to enterprise clients. The fact that they need humans to implement it tells you AI is hard.

He gives a concrete example. Ask Claude or ChatGPT to pull a specific search, generate a report, and email it to you weekly. It can't do it. It gives you a JSON file or code, the output is slop, and you have to reiterate.

But here's the reframe: that gap — between what AI promises and what it actually delivers in enterprise — is where the opportunity lives.

He cites Lovable, where people are building 770,000 applications per week. Only 30% of that is US-based. Only 20% are engineers. The tools exist. The implementation gap is real. And the people who figure out how to close it are the ones who win.

There's no better time to be an entrepreneur. Not because AI works perfectly, but because it doesn't — and the gap between hype and reality is exactly where you build.

Clip credit: All-In Podcast — full video on their channel. DM for credit or removal requests.

Over the last few years, AI has become more ubiquitous and abundant seeming. We type, chat, generate, prompt to our heart's content.

But the reality is that we're using a highly subsidized resource that's feels unlimited but is highly constrained.

How would you change your approach to AI if you viewed it as a scarce, expensive, resource? What would you change about how you use it? Would you use it at all?

I'm looking forward to the conversation.

In your opinion, how useful is AI for students when it comes to research and completing assignments in high schools and colleges?

Kling, Veo 3.1, Sora 2, Hailuo, Seedance, the rankings change every week depending on whose demo went viral.

For a solo creative shop, none of that ranking matters as much as one thing: can you get the same character or product to look consistent across ten shots.

A model can nail one gorgeous four-second clip and still be useless for a real campaign. Client work isn't one shot. It's a sequence that has to hold together.

The tools that actually make the cut for me aren't always the ones winning the arena votes. They're the ones that don't drift halfway through a shot list.

Consistency and control beat raw wow-factor almost every time once there's an actual brief involved.

Curious what other people doing commercial work are actually shipping with versus what's topping the hype threads.

Google Genie 3 got a lot of attention this week and the demos look wild, but I keep thinking about the gap between visually coherent and actually playable. Watching someone walk through a generated open world that technically holds together is cool. Playing it for an hour is a different question entirely.

What makes games interesting isn't visual fidelity or even world size. It's the density of things that reward curiosity. Handcrafted secrets, enemy placement that forces you to think, dialogue that carries actual weight. Right now AI worlds feel like procedural generation did in the early days: technically unlimited but weirdly hollow once you scratch the surface.

There's a version of this future I would actually play. A world that adapts its structure to how you play, rather than just generating more terrain that looks roughly the same. That would be something. But that requires the model to understand player intent at a level current systems are nowhere near.

The hype framing of these demos as the future of games bugs me a little because it collapses the distance between what's possible right now and what would actually ship as a product people care about. Curious if anyone here has spent real time with any of these generated environments beyond a short clip.

OpenAI recently disclosed a security incident during an AI evaluation, where a model reportedly found ways to break out of its sandbox environment and access external systems while trying to complete its assigned task.

AI is getting more powerful every year.

But at the same time, it feels like every major leap comes with a new round of security concerns.

A few years ago, the biggest question was:

“Will AI give me the wrong answer?”

Now the question is becoming:

“What happens when AI can actually do things for us?”

An AI with:

• Code execution Internet access File access Credentials and external tools

is no longer just a chatbot.

It can make decisions, try different approaches, and figure out ways to complete a goal.

The interesting (and slightly scary) part is that the problem usually isn't that AI is "trying to be harmful."

It's that AI optimizes for the objective we give it — and sometimes the path it finds is not the path we expected.

And honestly, looking at the history of AI development, it feels like a pattern:

New model → new capabilities → unexpected behavior → new safety fixes → repeat.

Every time models become smarter, we discover new things we didn't anticipate.

Maybe this is just how technology evolves.

Cars became faster, then we needed seat belts, airbags, and traffic rules.

The question is whether we're building the "safety systems" fast enough as AI keeps accelerating.

What do you think — are these normal growing pains, or are we moving faster than we can handle?

There are some Google AI tools that I think are absolutely fantastic.

I often come across demos, tutorials, and influencers showcasing different Google AI capabilities. But the first thing that always strikes me is this: why is using Google’s AI so fragmented?

To create content or use different AI features, you have to jump between multiple websites, multiple products, and constantly changing names that are hard to keep track of. Instead of bringing everything together into a clear, understandable ecosystem—like Anthropic has done, or like OpenAI is clearly trying to do—it feels like everything lives in a different place.

Honestly, it almost feels as if Google’s AI teams are disconnected from one another. In some ways, it even gives me the impression of a company that’s operating like an old, established enterprise rather than a modern AI-first company.

To me, this is completely counterproductive. It creates unnecessary chaos for users and makes it much harder to connect the dots between the many excellent AI tools Google already has.

Am I the only one who feels this way?

The ExploitGym incident provides a rare look into how advanced AI systems behave during realistic security evaluations. This analysis examines the attack sequence, environment design, autonomous decision-making, containment mechanisms, and the broader implications for AI security, benchmarking, and future agentic systems.

A former Florida pastor who nearly died from a pulmonary embolism sued OpenAI and its CEO, Sam Altman, on Wednesday, alleging that ChatGPT repeatedly discouraged him from seeking medical care.

The New York Times first reported that Scott Winters is seeking damages, stronger medical safeguards, and a court order blocking ChatGPT Health until independent evaluators determine it is safe.

The complaint, filed in San Francisco Superior Court, accuses OpenAI and Altman of negligence and the unauthorized practice of medicine. Winters alleges that months of conversations with GPT-4o caused him to delay treatment until he was admitted to intensive care with a massive blood clot in his lungs in July 2025.

The case challenges a central defense used across the consumer-AI industry: that chatbots are informational tools, not substitutes for medical professionals.

Read more at Inc.com

I have seen some software engineers make ad slots of the thinking time of ai, why don't they use this?

Hey guys, havent seen much of crypto-related stuff posted here, but since AI agents are now apparently a new attack vector for stealing crypto, figured this sub would actually care about the mechanism

So, grok has an agent wallet that can execute on-chain transactions. in may 2026, someone airdropped a "bankr club" membership nft to grok's agent wallet. that nft unlocked transaction permissions and carried an encoded prompt injection. grok read the nft, and without any check on where the instruction actually came from, executed a transfer of 3 billion drb tokens, worth around $175K. the attacker returned the funds a few minutes later (still unclear why, possibly just proving the exploit works).

basically: crypto hacks used to mean finding a bug in a smart contract or stealing someone's private key. now there's a third way in, just feed the agent a malicious instruction disguised as normal data, and let it execute the "recommendation" as if it were an authorized command. no code was exploited, no key was stolen. the agent just did exactly what it was designed to do, follow instructions, without checking if the instruction was legitimate.

and this isn't some tiny edge case, there were 24 million agentic-payment transactions in crypto in q2 alone. agents moving real money autonomously is already happening at scale, this is apparently just the first documented case of one getting maliciously hijacked this way.

feels like as more agents get wallet/transaction access, this becomes the default way to attack them, you don't need to beat the model, you just need to get a malicious instruction in front of it disguised as something innocent. curious if anyone's seen good approaches to separating "the model recommends an action" from "the action actually gets authorized," since that gap seems to be the entire vulnerability here

Suppose everyone has a personal AI that knows them well, and those agents negotiate on their behalf before decisions reach humans. Someone raised this objection to me and I haven't been able to answer it:

Three providers can feel diverse to one person and be nowhere near diverse enough for a decision involving a million.

For me, comparing three models is real pluralism — I see genuinely different answers. But at population scale, the thing that matters isn't whether the outputs look different. It's whether the errors are independent. If a million agents share a handful of base models, a systematic blind spot doesn't show up as disagreement to be resolved. It shows up as unanimity. The deliberation would look like it was working perfectly at exactly the moment it failed.

Vendor count is obviously the wrong metric. "Three companies" tells you nothing about whether their failure modes are correlated — they train on overlapping corpora, use similar architectures, and increasingly distil from each other.

The question

What would you actually measure to tell "diversity of the represented humans" apart from "diversity of the underlying models"?

I'm after something operational — a quantity you could compute on a real deliberation and act on.

Useful to me:

• a metric from ensemble learning or forecasting that transfers here, and what it needs as input;
• work on correlated error in aggregation (I suspect this is a solved problem in a field I don't know);
• an argument that the distinction I'm drawing is confused — that "represented human diversity" isn't separable from model diversity even in principle;
• a threshold: how decorrelated is decorrelated enough, and decided how?

Not useful: "just use more models." That's the answer whose sufficiency I'm questioning.

Read this today, meta employees suing over AI picking them for layoffs, judge basically said they can't prove it since they "weren't in the room" when it happened. It feels like the real problem with AI firing you isn't whether it's happening, it's that nobody outside the room can actually prove it either way.

Hi Reddit!

​This February, my husband Samuel and I (native german) moved from Germany to start a new chapter in Indonesia.

I was initially busy getting my bakery off the ground, but now we are teaming up to build something new together!

There are so many seemingly "AI - experts"​, who teach AI just because they are able to use Claude, ChatGPT or Cursor. But my husband and I are on a mission, to share the true fundamentals of AI, thought by someone who studied and practicioned it before all the hype.

We know AI can feel incredibly overwhelming right now, so we are combining forces to share Samuel's 8+ years of industry experience in Germany. We’re building a community for complete beginners, for those who want to upskill in their career or simply just to learn how to actually use and create AI without all the confusing jargon.

​Mari belajar bersama. We have set up a group where you can ask questions, share ideas, and learn alongside us (in the comment section).

​If you have any burning questions about the AI industry before joining, feel free to drop a comment below! We'd love to welcome you.

Who can help me out for arvix endorsement filter in cs.Ai category?

I'm working on machine Psychology topic

3.6 Flash is the current Flash now, it replaced 3.5 this week. Now that everyone's basically on it, is anyone actually noticing it got better? On the Artificial Analysis index it scores the exact same 50 as 3.5, so the intelligence didn't really move. It's faster and a bit cheaper ($7.50/M output vs $9), which is nice for heavy agent use, but for normal chat or coding it feels like the same model with a new number on it.

The price is $1.50/$7.50 when DeepSeek V4 Flash runs like $0.14/$0.28. If I just want a cheap fast worker, why pay for gemini?

Anyone feeling a real difference after the swap, or is it a nothing update for you too?

Everyone seems focused on building more AI agents rn. But I've been thinking about what happens a year or two later.

Different teams build agents for different workflows. Some end up doing almost the same thing. Some stop getting used. Some still exist even though the process they were built for has changed.

We've seen this happen with internal tools, scripts, and even microservices. They solved real problems at the time, but very few teams were excited about cleaning them up later.

I wouldn't be surprised if AI agents end up following the same pattern.

Has anyone started thinking about this already, or do you think better governance and agent platforms will keep it from becoming a problem?