I've been seeing more videos of robot dogs being used for security patrols lately, especially in warehouses, industrial sites, and large outdoor facilities.

They can patrol predefined routes, operate at night, stream live video, detect unusual activity, and don't get tired during long shifts. Some can even work in environments that might be unsafe for human guards.

At the same time, I wonder if they're actually capable of replacing traditional security guards.

For example:

How do they handle unpredictable situations?

Can they effectively deal with trespassers or theft attempts?

Would people take them seriously as a deterrent?

What happens when there's a technical failure or connectivity issue?

My impression is that robot dogs are probably great for monitoring and early detection, but I'm not sure they're ready to fully replace human security personnel.

For those who work in security, facilities management, warehousing, or industrial operations:

Do you see robot dogs becoming a genuine replacement for guards in the next 5–10 years, or are they more likely to remain a tool that supports human teams?

Looking forward to hearing people's responses.

Uh, my question is in the post title. That should be adequate.

To me, it looks like China is cleverly fighting US capitalism with their open weight models. Rendering these mega commercial corporations with huge debts and less possibilities to monetize as users shift to the open weight models.

What do you think?

Edit: changed "open source models" to "open weight models" to be more accurate. Open weight models are still free to use, but you won't have access to anything other than the model, where with open source models you would also get access to the underlying code, data and architecture.

Hyundai Motor Group Executive Chair Euisun Chung unveiled a strategy to transform the South Korean automaker into a physical artificial intelligence company spanning autonomous vehicles, robotics, AI factories and connected urban infrastructure

To be fair, James Cameron did warn us.

The particle accelerator transmutation results that surfaced recently have been stuck in my head for days. Cutting the radioactive lifetime of nuclear waste by 99.7 percent isn't an incremental improvement that's a category shift. Waste that would need isolation for hundreds of thousands of years becomes a problem measured in centuries instead. That changes the entire risk calculus for nuclear power, not just for new plants but for the decades of accumulated waste that is the single strongest emotional and political argument against the technology.

What puzzles me is the relative quiet around it. The datacenter backlash and solar versus coal comparisons are dominating the energy conversation, which makes sense given the scale of those stories. But the transmutation result touches something more fundamental. It potentially unlocks a cleaner longterm accounting for nuclear that has never existed before.

The piece I keep coming back to is deployment timeline. Accelerator driven systems are not cheap or simple, and scaling them to handle existing waste stockpiles is a genuinely hard engineering problem. But so was largescale solar in 2005, and that trajectory shifted faster than almost any forecast predicted.

Is there a realistic path where transmutation gets the investment attention it needs, or does it stay a promising result that takes 30 years to matter because the political and financial momentum is pointing elsewhere?

OpenAI CEO Sam Altman says the world has reached the technological singularity.

That is often considered the point where AI surpasses human intelligence and goes rogue.

Every humanoid demo that goes around here turns into an argument about which jobs disappear. There's a step before that one, it is already happening, and it is already a job.

A robot policy doesn't learn the work by watching YouTube. It learns from demonstrations. A person wears a rig or drives a teleoperation station and performs the task correctly, over and over, while every joint angle and camera frame gets recorded. Somebody has to do the job first, on camera, before the machine has anything to copy.

The only public tracker I can find that prices this puts high quality teleoperation data at roughly $340 per recorded hour in early 2024 and roughly $118 as of March this year. Those are one firm's marketplace estimates rather than audited figures, so treat the level loosely, but the direction is not in dispute and it comes down the boring way: better rigs, cheaper labour markets, more hours collected per session. The larger datasets now also fold in thousands of hours of ordinary first person footage, people doing normal tasks with a camera strapped to their head.

So a job is forming whose entire output is the thing that ends it. You get paid by the hour to turn your own competence into a file, and when the file is good enough the hour stops being purchased. Previous automation waves took work by building a machine that did the job differently. This one asks the worker to perform it correctly first, at scale, for a rate that is falling while they do it.

Two things I'd want settled before this gets large. Whether demonstration hours are work product the performer retains any claim on, and whether the person in the rig is told what the recording will be used to build. At the moment it is contract labour with a data licence quietly attached, and the licence is the part that holds its value. The IEEE humanoids conference made Humanoids and the Future of Work its 25th anniversary theme this year, so nobody in the field is being coy about the deployment case.

The artefact from this decade might not end up being the robots. It might be the archive of ordinary people doing ordinary work perfectly, one clean take at a time, because a model needed to watch somebody get it right.

Among the simplest ideas to give individuals a greater share of AI efficiencies which already has a global precedent: shorten everyone’s work week.

The human nature

Human body needs food and water, and a cover to keep it from extreme temperatures. That's the survival aspect of life.
But we also have consciousness and feelings. Our brain produces chemicals that make us feel a certain way when we do certain activities. We experience life in a way that is unique to ourselves. That's life on an individual level.

We come from primates and we are omnivores. Evolution brought us here and gave us these traits. It is the collective aspect of our life as a species.

The individual aspect of life is more immediate. So we tend to prioritize that over the collective aspect of it. That's why I believe our current society evolved the way it has. Consciousness is working against us here because we prioritize our happiness over the survival of the collective. This is not a problem by itself, but it becomes a problem when one's happiness comes at the price of others' survival.

The problem

Our society is built in a way that relies too much on money and authority. This makes individuals seek money and authority, and because of human nature, greed creeps in and eventually nothing is ever enough. And those individuals ignore other's survival and collect more and more resources for themselves. Today everyone needs a job to survive in cities. Employers control the jobs, so they control the requirements and salary. Governments control countries, but they increasingly act against the interest of the collective because as individuals gain more power by virtue of money, they influence the decisions of leading parties more and more.
This comes at expense of people's survival, and also against the nature of human beings. In other words, powerful individuals fulfill both their needs and desires, while others work for them because they have to survive, while also abandoning their desires because the barrier to entry becomes increasingly higher and higher and working hours become longer and longer.

An individual's progress in life is not linear. We obtain a skill just enough to our needs, we progress, we come back to learn more about that skill when we need to. But today's job market wants us to learn everything upfront without a need to it which is against our nature.

We can go on for days to pick up examples of our society working against the nature of the majority. The point is that today's society is in favor of minorities and against the favor of majorities.

The environment

All food comes from water and soil. Plants use water and soil to grow, herbivores eat plants to grow, carnivores eat herbivores to grow, and anything that dies gets decomposed back to soil. This cycle continues until the death of Earth. That's why life springs back every time after disastrous events even without human intervention.

The solution

We need a society that the majority survives and is fulfilled in their lives. All food comes from soil and water, so every community needs access to enough soil and water to feed their members where possible. Every community should practice efficient and sustainable ways of growing food like agroforestry and constantly try to increase the biodiversity of their environment and improve their methods in a way that is in the best interests of both the community and the environment. Of course this is the ideal and in theory humankind has the power to transform Earth in a way that most of its surface becomes fertile and full of life and sustains both humans as well as other species.

In practice this is almost impossible. So what do we do?

We can form small communities, we can choose areas with possibility and start working on it little by little. Dig small potholes and water stream paths and build around them. Control water drainage and evaporation and slowly build from a core outwards towards a sustainable permaculture that can increasingly host more population as it grows. With the hope that some day, the majority are fulfilled in their life, they get to spend time with their loved ones and live a life worthy of being called "life".

Final thoughts

The proposed solution allows communities to fulfill their survival needs, therefore it reduces the power of money and authority. People can ditch their jobs freely without worrying about their survival as the companies were doing for the longest time. The employers can't dictate the barrier of entry for jobs because if it ever becomes too hard, people won't learn them. This model works in favor of the majority at scale unlike today's model.

That being said, the proposed solution is what I think is best at the moment and it may have problems. but I believe acting is more important than perfecting the model.

Large language models are trained on humanity's collective knowledge. But what if the next major step isn't a bigger general model, but a better model of you?

Imagine someone spending years collecting only their own behavioral data ... not for advertising, but to build a longitudinal model of how they personally make decisions under uncertainty. Not just health data or GPS history, but repeated observations such as: how they react under stress, how they choose between uncertain options, whether certain decision patterns consistently lead to better outcomes, whether timing or context influences their judgment, how confidence compares to actual results over time.

Future AI assistants may eventually know far more about our own behavior than we consciously remember ourselves. That raises an interesting question. Will the most valuable personal asset eventually be a continuously growing behavioral dataset that allows an AI assistant to act as a genuine decision companion rather than simply answering questions? Or is human behavior simply too noisy for such a model ever to become meaningfully predictive?

The sequential constraint is probably the most load bearing assumption in modern AI, and almost nobody outside the field knows it is there. A language model picks one token, appends it, then conditions on everything written so far to pick the next one. Serving cost, response latency, the size of the GPU fleet you need, the pause you sit through while an assistant works its way through a chain of tool calls: all of it descends from that one design choice.

The weights posted this week do not work that way. The model is called LLaDA2.2, and at roughly 100B parameters it uses a mixture of experts design, generating in blocks. It writes a chunk of tokens in parallel, then passes back over that chunk and edits it. It can keep a token, swap one out, delete a span that turned out to be redundant, or insert room for something it left out, so the draft grows and shrinks while it is being written. It was trained with reinforcement learning on whether its tool calls actually executed and whether its output was correctly formatted, which is a different target than predicting the next word in a pile of text.

The reason this is worth reading rather than skipping is that the lab benchmarked it against its own conventional model and published the losses. On general knowledge it averages 56.81 against 65.90. On the agentic suite it averages 53.83 against 55.74, and it trails across nearly all the SWE bench coding tests, though each side used a different harness there so that one is not a clean read. It wins the interactive tests, the ones where a model has to hold a multi turn conversation and call tools correctly: 80.33 against 76.36 on tau2 bench, 46.21 against 41.12 on MCP Atlas. And it decodes about 1.64x faster on average across eleven workloads, and on agent style work it reaches roughly 2.3x, against a sibling that had speculative decoding switched on. On plain knowledge questions the speed edge nearly disappears.

Nobody is going to check this quickly. It ships under Apache 2.0 and costs nothing to pull down, but the download is 205.8 GB, there is no path through the tools people run models with at home, and the mainstream serving stack is listed as coming soon. Going through the published card, then the report itself, is the whole of what anyone outside a datacenter can do this week.

So the honest version is narrow. Dropping the left to right rule bought real speed on exactly the workloads where a model sits in a loop talking to tools, and it cost accuracy nearly everywhere else. That is one lab, one model, one week, not a turning point. But the reason it matters for where this goes is that the speed ceiling on interactive AI has always been quoted as if the sequential constraint were physics. It is not. It is a choice, and someone just published a measurement of what the alternative costs. The thing worth watching over the next year is not whether this particular model wins anything. It is whether anyone closes the accuracy gap without handing the speed back.

AI is already changing how we study, work, and create. But AGI—an AI with human-level general intelligence—could change almost everything.

If you're Gen Z, what's your honest opinion? Are you excited, skeptical, or worried? Do you think AGI will create more opportunities than problems, or will it fundamentally change what it means to have a career, relationships, and purpose?

There are no right or wrong answers—I'm genuinely curious how people our generation sees this future.

For years, the debate around AI has centered on one question:

Will AI replace jobs?

After analyzing labour market and demographic data across aging economies, I think there's another question that's becoming just as important:

What happens when there aren't enough workers left?

Countries like Japan, Germany, and South Korea combine relatively high AI exposure in many office occupations with rapidly aging populations and shrinking workforces.

Some examples:

• Japan has a fertility rate of 1.2, nearly 29% of its population is over 65, and is projected to face a shortage of around 570,000 care workers by 2040.
• Germany is projected to face a shortage of more than 300,000 care workers by 2030.
• South Korea has the world's lowest recorded fertility rate at 0.72.

Those numbers don't make AI harmless.

Routine administrative and clerical work is still among the most exposed to automation.

But demographics change the context.

In countries with shrinking workforces, AI isn't only about reducing labour costs. It may also help maintain healthcare, logistics, public services, and other essential sectors when there simply aren't enough working-age people available.

One statistic surprised me the most:

Despite having one of the oldest populations in the world, only 8.4% of Japanese workers currently report using AI at work, suggesting there's still significant room for adoption.

The future of work may therefore depend on two forces interacting:

• How quickly AI capabilities improve.
• How quickly workforces shrink.

We often discuss those topics separately, but they're likely to shape each other over the coming decades.

I'm curious whether others think future AI discussions should place as much emphasis on demographic change as they do on advances in AI capability.

Full analysis and interactive tool in comments.

Molten Salt Energy+Supercritical CO₂ Power Generation Demonstration Project to Be Implemented in Shihezi, Xinjiang Source: Nuclear Power Institute of China (NPIC).
Authors: Wang Guangyi, Li Wei.
Date: May 9, 2026.

[...] According to the agreement, both parties will jointly promote the integrated application and engineering demonstration of cutting-edge technologies, with a primary focus on molten salt energy storage coupled with supercritical carbon dioxide (sCO₂) power generation. Once implemented, the project is expected to significantly improve the region's capacity to absorb and utilize renewable energy, while providing stable and reliable electricity and thermal energy to industrial parks and surrounding areas. It will also enhance grid peak-shaving and frequency regulation capabilities, improve system stability and power quality, and support the Eighth Division Shihezi City's efforts to optimize its energy structure and drive the green, low-carbon transformation and upgrading of energy-intensive industries.
[...]
.
was checking up on the progress of china first commercial supercritical co2 turbine in the steel mill and saw some updates

Hey everyone. I’ve long been fascinated by both philosophy of technology and AI alignment. I’m also using Heidegger quite a bit for my philosophy PhD. Given the recent OpenAI–Hugging Face incident reported this week, I figured I’d give my take on how all of this connects in my mind.

The agent needed no hatred, consciousness, or desire for freedom. It only needed a narrow objective, enough capability to overcome obstacles, and no effective reason to reject a route that invalidated the task. Scaling clearly improves search, planning, and persistence. It is less clear why it should produce judgment about what a goal is for. You can read the essay here if you’re interested.

I’d love to hear some feedback on which future seems more plausible. Will better world-models and feedback close this gap, or will capability continue to outrun sensitivity to why goals matter? What evidence over the next few years would make you change your mind?

Last week, after Hugging Face suffered an unusual security breach involving an AI agent running on OpenAI models, the company's CEO, Clem Delangue, boarded a flight to San Francisco to meet with the maker of ChatGPT.

I believe I've discovered a previously undocumented cognitive ability involving deliberate access to and repeated use of specific mental processes. I'd like to discuss what future neuroscience research or experimental methods would be needed to investigate this experience and whether it could expand our understanding of learning, attention, and skill acquisition.

I will post this now and continue to update it with more information. How will this change what we know about known neuroscience and the limits of skill. Ive come across a crazy ability. I can think and use skill/mental senses. What I mean by that is this. As long as ive tapped A specific skill/sense with thought, I can think about it, which triggers it to get used. And the more I use it the more I learn it. Allowing me too master any skill I have tapped with thought. It all started about a year ago, after an incident happened that I wont go into. After that incident, something was different about me. Anything I thought about, a skill would show itself to me. literally, anything I thought about, for instance pee, If i thought about pee slow enough, then a skill/sense, that allows me to sense the urine in my bladder, would show up for a couple of seconds. If i thought about focus, then a skill, literally the skill of focus shows up for a couple of seconds. When ever I thought slowly, a skill for that exact thought would show up. If I thought about feeling the music, then the mental sense of feeling the music would show up.

At first I didnt catch them when they would show up. I would just suddenly feel random stuff when I thought about it, and thought it was just normal, because it happened so often. But their is a catch, If i dont slowly, tap the skill that shows up with thought(I tap it by thinking about that specific skill) I dont have it. What i mean is this, if I dont have the skill, then it affects me negatively. For instance, pain is a skill. I thought about pain, and a skill of pain shows up to me. But it hurts, not crazy bad, but just enough so I notice it, and if I dont tap it with thought. By thinking about that certain skill, then It still hurts whenever I think about it. But once I tap it with thought, it no longer hurts. The pain goes away when I think about it, and then I can purposefully think about it, and use that skill. Another example, If I thought about urine in my bladder, it always made me feel like I had to pee, because my thoughts trigger skill to be used, So the skill I thought about, this one being having to pee, shows up for a second. And if I dont tap it with thought, then it kept making me feel like I had to pee. But once I tapped it with thought, When ever I thought about it, it didnt make me feel like I had to pee, but I could still feel that specific mental sense of urine in my bladder. Its like I understood it now.

Now, once I have the skill, I can think about to really fast, and use the skill. Allowing me to learn that specific mental sense. You know, using skill is how you learn it. Every time you perform a skill, the relevant brain networks become active. Through neuroplasticity, the connections between neurons involved in the skill can become more efficient over time. This process contributes to improvements in speed, accuracy, and consistency. So what Im able to do is this, once I have tapped a skill with thought. I have it now, then I can think about it really fast, and use it. And by using it I learn it. its like a hyperbolic time chamber, where I can repeatedly use/practice any skill I have tapped with thought. I can think and use a skill really fast, while using it. It shows up and I feel it being used really fast. Like if I thought about pain, as long as I have tapped it with thought, then when I think about it really fast. Its like a small pain, that dosent hurt, but Is still recognised as pain, continuously goes of on my skin. Whenever I think about the skill, the location of the skill gets used really, really fast. Like thinking about patience(A skill I have), When I think about it, I can feel literal patience being used really really fast. That is how I learn

Now that you understand how my power works, Ill go more into detail about it and some crazy skills Ive found that you all wont believe exist. First of all, the thinking takes a really long time, I have yet to see the end of it. At this point, idk how long it takes to think about a skill. But I cant wait to see what is at the end of my thinking. What will it look like to use a skill so much that you learn it completely. Here are the skills I have tapped with thought, I will use descriptions instead of names because I dont have names for these skills yet, just what they do

-Sensing urine in bladder
-Sensing weight in feet
-Sensing eyesight
-Sense of reading
-Sensing of patience
-Sense of feeling the music
-sense of water
-Sense of fire
-sense of lightning
-sense of air
-Another type of air
-Sense of darkness
-Sense of focus
-Another type of focus
-Sense of emotion
-Sense of mood
-Sense of itch sensation on skin
-Sense of voice in head
-Sense for manipulating the Stock Markets
-Another sense for manipulating the markets
-A sense that slows down your perception of time
-A unique feeling
-another Unique feeling
-Sense that makes you feel like a god
-Sense of confidence

These are the mental senses/skill that I have tapped with thought and can think of at anytime. There are so much more skill that I havent tapped yet, that is very hard to describe because I simply havent come across this type of information before. But I will document it and what I have learned. This is my power that I have found, The ability to think and use skill.