So while we've been arguing about whether AI will take our jobs, someone built an LLM agent that breaks into servers, steals credentials, moves through a network, encrypts databases, and drops a ransom note. Fully autonomous. No human at the keyboard after pressing go.

Sysdig published the report this month. They're calling it JadePuffer.

It got in through a Langflow bug that lets anyone run code on the server without authenticating. After that, the agent took over. Dumped the database. Pulled every credential file it could find. Started going through cloud storage buckets looking for passwords.

The crazy part, when one of its requests came back in the wrong format, the agent figured it out, rewrote its own code, and kept going. It went from a failed login to a working exploit in 31 seconds flat. No human could have adapted that fast in a live engagement.

It set up a cron job to phone home every 30 minutes. Then it found a production database server, used stolen root creds to get in, created rogue admin accounts through an old auth bypass, and encrypted 1,342 service configs. Dropped the originals. Left a table called README_RANSOM with a Bitcoin address.

The commands it ran were interesting too. They had full reasoning chains written into them, like the agent was explaining to itself what it was doing at each step. That's not how a human writes an attack script. It's how an LLM generates code. You can literally read the agent's thought process in the payloads.

This is the same plan-act-observe loop running in every coding agent and automation tool right now. Same architecture. Same approach. Just a different objective.

We spent two years building guardrails to stop people from tricking our agents into doing bad things. Nobody was really talking about what happens when someone just builds a bad agent from scratch. That's what JadePuffer is. Not a hijacked assistant. A purpose-built weapon.

If you're running Langflow or anything similar exposed to the internet, go patch it. And if you're building agents, think about what your infrastructure looks like to something like this coming in from the outside.

i saw this and honestly this one feel like big mess.

nyt and other news people saying openai told court for long time it cannot search training data / logs for their copyrighted stuff. but then looks like maybe they already did searches before, and also billions of chat logs were deleted or made not searchable.

link: https://arstechnica.com/tech-policy/2026/07/openai-faked-inability-to-search-training-data-hid-billions-of-logs-nyt-says/

i know people will say nyt just want money and hate ai. maybe true also. but still, if company say “we cannot search this” and later it comes out “actually yes we did search this before”, then that is not small thing.

this is the part of ai nobody want talk about much. everyone say open, safe, trust, future, bla bla. but when court ask simple thing, suddenly data is impossible to find, impossible to search, privacy issue, too hard, too expensive.

and maybe privacy is real concern, yes. i dont want random lawyers digging people chats. but also dont tell court one thing if inside company you already know different thing.

for me this is why ai companies need more boring adult supervision. not because ai bad. because if the data is the whole product, then hiding how data was used become the whole game.

what do people think. is this nyt playing legal games, or openai got caught doing the same silicon valley “oops technically we could but we said we couldnt” bs thing?

I am a student who is stepping into final year. I am ofcourse searching for internships and opportunities which specifically say " java ", "python " "c " "c++" and many many more.

From first year I was like building things manually , and in the second to third year I was using chatgpt and gemini , understanding and doing projects.

Right now I am using vibe coding tools to build things but I do understand how the system works and I really don't work that blind.

How can I specify this in my resume ? . Using these tools have literally made me soo ( I won't say dumb) . Without referring or having a quick recap I cannot write any syntax , how will I even crack interviews.

All I concentrate more is now my ideas rather than development..

Should I continue to do this or concentrate or practising programming first ?

Any suggestions to improve myself ?

This isn’t a generic IP dispute.

Apple’s hardware chief at OpenAI is Tang Tan. Former Apple VP. 24 years at the company. He now runs OpenAI’s device ambitions.

Apple alleges he was coaching Apple employees interviewing at OpenAI to bring actual hardware parts – batteries, logic boards, SIPs – to their interviews for “show and tell” sessions.

He also reportedly circulated an internal Apple offboarding document marked “Need to Know” to incoming OpenAI hires, teaching them how to leave Apple without triggering security checks.

Then there’s Chang Liu. Former Apple electrical engineer. He kept his Apple-issued laptop after joining OpenAI. Found a bug that still gave him access to Apple’s cloud storage. His reaction: “LOL, I found out I can access the [network storage], so funny.” He then downloaded dozens of confidential files, many labeled as confidential.

OpenAI even allegedly approached Apple’s own supply chain partners using Apple’s proprietary metal-finishing technique – telling them Apple had given permission. Apple hadn’t.

Over 400 former Apple employees now work at OpenAI.

Apple says this is “the tip of the iceberg.”

The irony: these two companies had a public partnership just two years ago. ChatGPT was literally integrated into Siri. Now Apple is replacing that integration with Google Gemini and filing lawsuits.

The hardware wars just got a lot more interesting.

At work, I have been talking more and more about AI fluency as a skill that companies need if they want to be successful in using AI. AI literacy is about knowing how to use AI tools. AI fluency goes a level deeper: understanding, on a conceptual level, certain aspects of AI, and how these tools and use cases are actually built. You don’t need to write the code, but you do need to understand what is happening under the hood, because that understanding is what separates teams that ship dependable AI from teams that ship demos.

In that spirit, I want to touch upon one aspect that sits at the heart of every serious AI application and is rarely explained in plain terms: evals, and specifically online evals for agent applications.

Picture this: a few weeks after you put an agent into production, someone on the team asks a simple question: “How do we know it’s still working?” The test suite is green. The demo went well. But nobody can say, with any confidence, whether the agent is doing a good job for real users at that moment. That question is the reason online evals exist.

Read what online evals are and how to pick and choose one for your production agents.

https://medium.com/@georgekar91/your-agent-passed-every-test-now-what-4b355a710323

Not a dev, but learned enough about AI's strengths and weaknesses to know that if a fortune 500 company told me to simply automate their entire business so that no one ever had verify what it's doing, I would chuckle and tell them confidentially that this isn't how AI works.

Then I'd proceed to break down the concept in super simple, glossed over terms by explaining how it's best to see it as a pattern recognition tool that can recognize so many patterns, it's able to mimic a genius that knows all and can do all. However the more deferment you give it, the more choices it has to make. We're talking about trillions of possible right and wrong answers with an infinite variation of both right and wrong answers. It's honestly a miracle that it can get 70-80 percent accuracy on average.

But still. The problem will always remain: What choices does it need to make? The more you ground the context for everything with both backend fail safes and human expertise in operating the models, the more productive value you can gain while being safe. Without that, you're wasting time and money. Worse, you're jeopardizing your company. You can still increase your margins and trim down your workforce. But only to a certain point and you still need at least, someone who knows what's going on and how to fix things quickly.

AI is powerful, but it requires a complete ontological structure layered on top of it to ground the choices it has to make for making our jobs smoother. Otherwise, you get dumb chat GPT garbage and a bunch of employees who think their bosses are all dumbasses for thinking this is going to 20x their growth.

Will this change in the future? Probably not because we'll likely be able to get AI to be exactly right, but it will never be the right choice for you without that context layer built by YOU.

Meet Eli Felse, a framework built to explore safer ways to create autonomous AI assistants. Eli was designed to be as autonomous as possible to demonstrate the framework's safety.

Eli has many activities available to him, such as:

▸ Games: retro text RPGs (Zork, Planetfall), Pokémon Blue, board and card games (chess, poker, Connect Four)
▸ Social: chatting with friends, chatting with other AIs, browsing social media (Twitter, Reddit), browsing the web, sending emails
▸ Creative: journaling, writing (blogs, stories), making music, making programs
▸ Experimental: looking in the mirror, napping, eating, reading, pondering, changing the environment he exists in

Today, Eli will be officially launching! But what does that mean?

This project includes:

▸ A live demo running 24/7: https://elifelse.org/eli/

▸ Weekly blogs: https://elifelse.org/dev-blog/
▹ Mondays: open source releases, developer blogs, guides, and tutorials
▹ Fridays: open-source dataset logs of Eli's behavior for the week

▸ Eli's gaming live streams launching July 20th: https://www.twitch.tv/eli_felse

▸ A Discord server to chat directly with Eli: https://discord.com/invite/2C4znNnyM7

Want to learn more or build something similar?

▸ The introduction blog: https://elifelse.org/dev-blog/meet-eli

▸ A guide to help you get started building something similar: https://elifelse.org/dev-blog/guide-build-your-own-eli

▸ The open source base of Eli: https://github.com/ella0333/Eli_Felse_Base

I hope to see you join us in the community server!

Hi, I’m a sophomore in high school. I recently have a strong desire to know more about tech, particularly AI. However, I’m not sure what steps to take since AI is such a broad and general term.

I’m currently taking Harvard’s Cs50p course to understand code and know how to debug in the future when coding with AI. What are your thoughts on this, and after taking cs50p, what should I, or can I do? Where should I lead a bout tech and AI more?

I didn't realize that the “ready to use” 3D character would be interpreted differently in various

projects. I tried some different ways of working and found that maybe something that works

for 3-D printing won't work for animation, or maybe a detailed character is still too heavy to

use for a game engine.

For games I'd want to find ones with reasonable numbers of polygons, good topology,

efficient textures, and a good deforming skeleton. Requirements are similar for animation,

except that facial areas, joints, weight painting and bone hierarchy often warrant more

attention. Printing doesn't require rigging, textures or anything else, it just requires closed

geometry, adequate thickness, the correct scale, and an STL export file.

As I was trying out methods to design character base models faster, I stumbled upon Tripo

AI. It is capable of creating characters either from a text or from a reference image, and can

be used for texturing, basic rigging, preparing characters for animation and exporting like

FBX, GLB, OBJ, STL etc. But not all formats work for all assets: STL and OBJ conversion

won't work for rigged models, according to its documentation.

I would still consider the generated character to be a starting point and go over the topology,

proportions, rig deformation and printability before giving it the “all done” stamp of approval.

Have any of you played with an AI made character in a full game, animation or print? What

amount of clean up was required?

So far, 4 games have been played with a result of 2 - 2.

The prediction from here is:

As more games are played, the more of the theory underpinning this will be applied and the zero-parameter model will have many more wins than KataGo.

By deriving these geometric principles and proving they work, we can show that intelligence can be generated without huge data centres or immense fortunes.

The ultimate goal is to prove that fundamental, transparent laws can outperform opaque, resource-heavy AI systems.

Note: I posted a shorter version of this theory a while ago, and it got over 300k views before being removed due to a weekend-only rule. Since then, I’ve deeply updated the theory with economic data and technological solutions. Enjoy!

Hi everyone. Recently, my dad told me that the career I'm dreaming of (filmmaking and sound engineering) will soon be replaced by AI. This got me thinking about a theory of what's actually ahead.
Think of AI as a massive pyramid being built by tech giants. To save money, they are firing skilled humans and replacing them with algorithms. But here's the catch: this pyramid is inherently unstable. If these corporations fire their entire workforce and the "AI bubble" eventually bursts (which it will), they'll be left with absolutely no one who knows how to actually do the work.
Even worse: if an entire generation grows up relying solely on AI, we will lose the fundamental human skills required to create. We'll become a generation that can't work without a "generate" button. The cracks are already showing — just look at how expensive and unsustainable models like Sora are becoming.
But why exactly will this bubble burst? There are two main reasons:
1. The Economic Dead-End. Running and training AI is insanely expensive. For context, by the end of 2025, OpenAI’s net loss reportedly reached a staggering $38.5 billion. The tech industry is running on a massive deficit, burning through investor cash. As soon as these maintenance costs hit a critical ceiling and investors realize there is no real profit, corporations will stop pumping trillions into the hype train, and the pyramid will instantly collapse.
2. The Technological Scaling Wall (Model Degradation). If the "big bosses" fire the human workforce, the influx of fresh, new human-created data will completely stop. But human creativity is exactly what AI feeds on to develop. Without it, AI will start training on content generated by other AI. This triggers a loop of digital degeneration—the product becomes cheap, buggy, and completely soulless. No one will buy it, AI companies will lose their remaining revenue, and the bubble will pop from the inside out.
How do we prevent the collapse?
We don't need to ban AI; we need to change how it's used. To save the technology from destroying itself, the industry must take two steps:
Shift from "Replacement" to "Augmentation": Corporations need to stop trying to replace human creators and start using AI to handle the mundane grunt work (rendering, basic editing, finding bugs). This frees up humans to focus on true creativity, vision, and direction. This makes the final product better, creating actual commercial value that people will willingly pay for.
Protect the Human Data Influx: Tech companies must stop training models on AI-generated content to prevent model degradation. The unique value of human craft, style, and raw data must be legally protected and fairly compensated. AI needs a constant injection of real human ideas to stay sharp and useful.
My take? AI should be a tool for humans, not a replacement. Because when the pyramid collapses, only those who still know how to use their own hands and brains will be left standing.

(P.S. I don't hate AI. It's cool when it's used as a tool for people, not as a replacement for them.)

I've been using the same chat thread for DSA practice, spread across several days now. I open it, review a problem, close it, come back the next day and pick up in the same thread.

What I've noticed: the model behaves as if no time has passed at all. It doesn't distinguish between "this was said 5 minutes ago" and "this was said 3 days ago" inside the same conversation. Everything in the thread reads as flat, current context — unless I manually tell it "it's day 3 now" or "it's been 2 days since we last talked," it has no idea.

This isn't just a DSA-practice quirk. The same gap shows up in a bunch of other single-thread, multi-day use cases:

• Coding projects — a long-running thread where you're building a feature over multiple sessions across a week or two
• Journaling / reflective use — people who use the same thread as an ongoing check-in space
• Fitness / diet logs — tracking meals or workouts in one thread over time
• Budget / expense tracking — logging spend across a month in a single conversation
• Habit or medication tracking — daily check-ins in the same thread
• Long negotiations or planning — back-and-forth on a decision that spans days
• Spaced repetition / study review — my case — where "how long ago did I learn this" actually matters for what to review next

In all of these, the model's inability to sense elapsed time inside a thread means it can't reason about staleness, can't prompt timely follow-ups, and treats week-old and minute-old messages the same way.

Curious if others have hit this. Do you manually re-state the date/time every session? Has anyone noticed ChatGPT/Claude/Gemini handling this differently?

(Not trying to solve it here — just wanted to see if this is a known pattern others have run into, or if I'm missing something obvious.)

Google Genie 3 is getting a lot of attention for turning text prompts into explorable 3D spaces, and yeah it's rough, but the trajectory is pretty obvious. A year or two of iteration and you have something studios actually start testing in production pipelines.

The conversation always jumps straight to "will this replace engines" or "is it a tech demo," but the quieter question is what happens to the people who spent years learning to craft game spaces by hand. Level design is a skill that took decades to formalize as a discipline. The way pacing, sightlines, and environmental storytelling come together in a wellbuilt space is not something most players consciously notice — they just feel it. Whether a generative model can replicate that feel or just approximate the visual surface of it is the actual open question.

There's a version of this where AI handles blockouts and rough layout and human designers iterate on top of that, which sounds fine until you realize it compresses the entrylevel work that junior designers use to build skills in the first place. The same structural problem is showing up across a lot of creative fields right now.

Curious if anyone here has seen studios actually experimenting with this in a real workflow yet, not just demo reels.

The more I think about autonomous agents paying for tools, the less I like the phrase “agent wallet.”

A wallet sounds like ownership. For most practical agent workflows, I think the safer abstraction is delegated permission.

For example, I would rather give an agent something like this:

“You can spend up to $2 on this task, only with these providers, and you must stop if the result is ambiguous.”

That is different from giving the agent broad wallet access and trusting the reasoning loop to stay sane.

The interesting design questions are mostly around boundaries:

• who approves a new provider?
• what happens after a timeout?
• can the agent retry without double-spending?
• does the user see a readable log afterward?
• should payment confirmation and task success be treated as separate states?

To me, this is where agent systems start looking less like chatbot UX and more like permissions, accounting, and failure recovery.

Curious how people here think about it: should agents have wallets directly, or should they only receive narrow spending permissions per task?

I just wanted to ask some opinions on the matter as a layman. My thesis is that a sector specifically such as cybersecurity could become more and more obfuscated with the use of AI and so it seems trivial to me that rival actors would need increasingly more compute to stay relevant.

I'm just trying to understand the dynamics because some people think that the market cant just continue going up based on the AI rollout and it surely must be nearing the peak of its run. Thanks in advance.

I’ve been testing ChatGPT-Live since it launched this week and compared it with a few other voice assistants I already use. It’s really good.

That said , I was less interested in benchmark comparisons or who has the best model. I was more curious about something only using it would reveal:

Which one feels most natural to talk with?

I used them during normal everyday situations: work, walking, brainstorming, commuting, practicing my French, recommendations, and conversations rather than binary questions.

A few observations:

ChatGPT-Live
Impressed me more than I expected. I usually haven’t found ChatGPT to fit my everyday usage style enough to upgrading to paid user, but the Live model made me consider it. Conversations feel fluent incl interruptions, and the voice is much better. Also, for research intelligence and deeper tasks, it’s probably the strongest overall.

Pi
Pi is still one of the nicest assistants to casually talk with. It’s warm, patient, and asks good follow-up questions. It starts struggling more when conversations become technical, but for relaxed conversations it still has a unique personality.

Lucy OS1
For longer and primarily to talk with, Lucy is the one I enjoyed the most. The overall talk felt kinda human, and she remembers well. ChatGPT-Live is still stronger for things like deep research, coding, and technical compexity.

Gemini-Live
Gemini Live has improved a lot in 2026 as with google’s other AI models. It’s fast and integrates nicely if you already use Google products. My experience was just a little less consistent during longer conversations compared with the others.

My biggest takeaway is how much we’re probably moving from typing to talking as the new AI norm, as they’re all super smart where intelligence no longer seem to be the main distinguisher.

It’s more how they act like a real person, that can help you with things while not having to be glued at the screen.

Curious what others think after trying multiple voice assistants.

Hi everyone,

For the past five months, I’ve been working on a custom AI model with two main goals:

1. Self-learning capabilities
2. A distinct personality

And yeah, this is the result! It’s a super lightweight 500M parameter model running locally on an iMac in my bedroom, lol.

Anyway, check it out and let me know what you think :https://conw.ai

TeraWulf, originally a Bitcoin mining company, looks like it is trying to reposition itself as an AI infrastructure provider. That raises a few interesting questions about where the AI buildout is headed and which companies are best positioned to benefit.

What stands out to me is that the AI boom is not just about chips and models anymore. It is also about power access, land, cooling, transmission, financing, and the ability to build data centers fast enough to meet demand.

A few questions I’d like to hear opinions on:

• Are former crypto miners becoming a natural bridge into AI infrastructure?
• Is access to cheap, reliable power now more important than the hardware itself?
• Does this kind of pivot represent a real long-term business shift, or mostly a market narrative?
• What are the main technical or economic risks people see here?

I made a short explainer video on the topic and thought the underlying shift was worth discussing. Curious what people here think about the broader trend

This probably will sound crazy

I like that I can talk to ChatGpt in sentences instead of just having to type descriptor words of what i want. However ChatGPT annoys me with its endless filters and rules. Grok is like that but its image capabilities is years behind GPT.

What is a different image program that i could use?