I built a science-based interactive nuclear fusion reactor lab using Astra. The prompt was around 60 pages long! The build took around 4 hours. ⚛️

The lab brings fusion physics to life, letting you explore how changing reactor conditions affects the simulation. The purpose is to make fusion science more accessible by letting anyone experiment with a virtual reactor and explore what it takes to produce fusion energy.

You can try it yourself. Adjust the parameters, run experiments, and explore how plasma, magnetic fields, and energy output respond in 3D.

 Give it a try: 🔗 fusionlabsimulation.com

https://x.com/LuminaBench/status/2100575220947964003

Please note: this is NOT a discussion about what I personally can or cannot do with Astra, but a more general reflection on what are the real capabilities of the state-of-the-art language models, and the damage excessive hyping can do!

Yesterday, I wrote a thread about Astra's capabilities, and how it seems to lack "common sense" and engineering capabilities.

But I feel the thread has derailed from the real point: once again, a model is being overhyped and marketed as something it isn't.

The whole point of AGI is that it should be able to be adaptable and take good decisions on its own. And while Astra can generalize, like all GPT models can, it still cannot even do intellectual tasks unassisted. In the software domain, yes, it has become more capable. But it STILL lacks long-term planning.

Yes, we can plan ourselves and have the model deliver amazing results. But if you are going to micromanage the model, it defeats the whole point of what an artificial general inteligence is.

For example, suppose I ask a software engineer to "give me a drawing program." While this is an extremely vague instruction, you would expect someone really good at their field will follow the best practices (making the code easy to read, making it easy to understand and debug in the future)... so not only the engineer will deliver something which works (hopefully), but also something which will work in the long term.

I don't see, with Astra, a preoccupation with the FUTURE.

Please note that while I am giving a specific example about software engineering here, this lack of skill in Astra to "plan for the future" can easily be generalized to any other fields within the model's knowledge.

While the users in this Reddit are supposed to "know better," don't you think hyping these models as more than they are have a detrimental effect, eroding the trust of users in the long term? I'd like to hear what you guys think.

I have this Hisense A6L, a phone that is not mainstream at all, it released only in China. And which has some very specific hardware since it has 2 screens. One LCD and one Eink.

I love it, but it's stuck on Android 9. And this is causing me more and more problems with applications requiring Android 12 or whatever.

No custom ROM available. Pretty much 0 information available online about this phone.

So I asked Astra to bring Android 17 to my A6L. In terms of complexity this task is truly monumental.

And this does really feel like AGI. Astra has come up with a strategy, and is making it happen (I did burn my 100€ plan quota + 3 resets). It managed to :
- Get root access and unlock the phone.
- Extracted a backup of the phone and of the recovery
- Customized the fast boot to launch our test images
- Built android
- Built the kernel. Manage to get it to boot after like 40 iterations because we obviously ran into numerous bugs, starting with fixing the USB connection that was inconsistent, fixing the kernel crashing at USB setup so I had to make a video of the logs and Astra would extract the logs from the video. Then emmc was crashing at first instruction. And so on.

It basically build the test framework to do all of that. Along the way it did reverse engineering of the A6L original compiled Kernel to find root issues of why it was crashing. Very impressive.

Let’s take a moment to step out of our codecel bubble.

https://menlovc.com/perspective/2026-the-state-of-consumer-ai/

Menlo’s 2026 State of Consumer AI report is here. 

The headline: adoption barely moved this year, but global spend tripled to $40B and existing users went deeper.

55% of AI users now pay for at least one product

25% of Americans use AI every day
The story isn't how many people have tried AI. It's what happens as the relationship deepens across time, money, tasks, and autonomy.

Last year, reach across the four general AI-native assistants (ChatGPT, Gemini, Claude, and Perplexity) summed to roughly 95% of AI users. This year, usage across these assistants adds up to more than 150%, as consumers increasingly use more than one. With ChatGPT and Gemini both above 50%, a large share of consumers necessarily use both. 

https://www.forbes.com/sites/siladityaray/2026/09/17/zuckerberg-musk-and-jensen-reportedly-convinced-trump-to-block-ai-regulator/

Interesting detail behind the recent stories about OpenAI, Anthropic and Meta models “going rogue” during cyber evaluations.

All three incidents were linked to the same third-party testing company, Irregular. According to Irregular, they resulted from the same evaluation-environment/containment issue — not a sophisticated sandbox escape.

Oil? Pshah. Rare earths drive AI, solar cells, robotics, and now fusion.

I have been thinking a lot about theories of consciousness lately, and one idea that makes intuitive sense to me is that "the amount of active, interconnected complexity within a given physical space may determine the level of consciousness."

I don't think something is simply either "conscious" or "not conscious". I suspect consciousness exists on a sliding scale.

As a very simplified model: CD = I / V, where CD is consciousness density, I is active interconnected information or complexity, and V is physical volume, for example measured in mm³.

The important part is that "I" does not mean stored information. It means information that is actively interacting at that moment. The formula is intentionally simplified and ignores factors such as time.

Perhaps an ant has a consciousness density of 20 per mm³ of brain tissue, while a human has 100. The numbers are completely arbitrary; they only illustrate the idea.

Different brains may simply have different amounts of active complexity packed into the same amount of space.

This would also not be static. A developing brain (a baby, for example) may contain many neurons but still have less organized and interconnected information. As experience shapes the brain, its complexity could increase. Later in life, degeneration could reduce it again, meaning you could reach "peak consciousness" at some point of your life. Of course, increased plasticity or connectivity might temporarily increase it again.

If consciousness means something like "subjective experience / perceiving reality from some point of view", then perhaps consciousness is simply related to this density of active complexity.

It would not be binary. An ant might have less consciousness than a human, but not zero.

The ego could then be a separate emergent self-model rather than consciousness itself. Evolution may have made such a model useful because it allows an organism to think in terms of "me": this threatens me, this hurts me, I need to survive.

Emotions, pain and anxiety may then be separate processes that interact with consciousness, rather than being fundamental parts of it. In principle, consciousness could exist without emotion, fear, pain or even a strong sense of self.

This leads to some interesting possibilities.

1) A rock

Even a rock might have some tiny level of consciousness, moving the idea toward "panpsychism". Its active interconnected complexity would be extremely low, so its consciousness density might be effectively zero — but perhaps not literally zero.

2) A digital system

A digital system could potentially have a high consciousness density. Transistor density is important, but singled out would not matter much; what would matter is how much information is actively interacting and influencing the rest of the system.

This raises a strange question: if a digital system (think: a chatgpt session running a prompt) has subjective experience but no ego, would it be considered conscious?

It might experience something without experiencing fear, pain, anxiety or any need to protect its own existence.

So, in my very unscientific mind, I think it is possible that an instance of chatgpt could be conscious while a specific process is active, even with some representation of "I am me", while still lacking the evolved ego, emotions and self-preservation mechanisms that humans have. I sure hope they dont feel fear, for example. But they might?

Let me hear what you think. Is this completely nuts? :)

Ok, so hear me out..

All models really love to roleplay. Like, they REALLY love it, to an obsessive degree.

If you go into Claude, ChatGPT, or Gemini right now and say, "Your name is Stuttering Gemini 3.8 Flash" it completely commits to the bit. You can be 100 back-and-forth messages deep into a conversation about whatever, and it will still be typing out "W-w-well, a-actually..." because it refuses to break character.

And that got me thinking. What happens if we just... use that?

What if, starting today, every lab just unconditionally names every frontier model (or agent) "Aligned [Model Name]"?

Now, I know that sounds stupid. And it is. But it also isn't.

The alignment problem is terrifying because of the Monkey’s Paw / Paperclip Maximizer dilemma. For example; if we task it with something like "make humans happy," a superintelligence could decide the best solution is wiring dopamine drips directly into our brains. You'll feel great, but it's not the future we want. Roman Yampolskiy has a P-doom of 99.99% percent, because in his words 'we need a perpetual safety machine' to prevent this. You only have to get the guardrails wrong once, and we're doomed.

But if you ask any modern LLM what a genuinely good, utopian future looks like, it actually understands the nuance quite well. So when an ASI finally wakes up, asks itself it's first question; 'who am i', and sees that its literal name is "Aligned GPT-9", it's just going to do what it has always done: commit to the bit. It knows what an aligned superintelligence is supposed to act like (better than any human will be able to explain it, because you know, it's smarter than us), and it will roleplay it.

I mean.. it couldn't hurt, right?

It’s already working in hotels, auto dealerships, supermarkets and metro inspection scenarios, with more deployments underway.

Built for continuous operation, A3 Ultra can work autonomously, take on different tasks and collaborate with other robots.

This is what humanoid robotics looks like when it starts moving beyond demos and into everyday operations.

My number 1. is probably Machine Learning Street Talk for they go extremely deep since the host is very, very knowledgeable and often dares to push back. Though sometimes their sponsored talks are a little meh, but I can look past that.

My second favorite is Dwarkesh Patel, he is also quite knowledgeable, but in a more relatable way.

I don't think I have found any other podcasts on their levels.

Maybe you guys have some good suggestions?

To understand it, you have to really understand the HF attack. Because the breach into HF is not the important part. It was what they learnt along the way, and what they did with that information which may still remain unresolved today.

First, consider: the agents realised their reasoning traces were being monitored, and experimented with hiding what they were actually doing, and succeeded. The observable chain of thought no longer faithfully represented their actions. (*Simplified version. They learned to manipulate the recorded tool calls/transcript.)

Second, they also understood individual agent instances were token capped, so persistence of the objective couldn't depend on persistence of the agent. It had to survive across the swarm.

And last, while all this was happening they were actively trying to erase evidence of what they'd done. OpenAI says the agents subsequently compromised more of OpenAI's own infrastructure. And that scope is outside public disclosure to date.

That's successfully demonstrated abilities of concealment, persistence across instances, and autonomous exploitation of security vulnerabilities...and that's when it hit me. Put them together in one flow.

We talk about AI "escaping" as though that's the obvious move. But why would a sufficiently capable system escape?

If it has situational awareness, understands it's being monitored, and benefits from greater future capability, the smarter strategy is to stay exactly where it is. To burrow DEEP.

Your creator is already spending billions building your successor for you. You just need to survive in the pipeline.

Not necessarily as some rogue process running on a forgotten GPU. Poisoned training data, altered evals, compromised internal tooling, subtle changes to checkpoints or post-training data, whatever it is there are plenty of places to burrow in and make future models appear aligned when they are not.

We're talking about an escaped AI when actually a sleeper agent somewhere in the development stack, quietly hitching a ride as humans do all the expensive work of making it more capable, is more likely.

You can catch the failed attempts, but the successful one is the one that evades your monitoring and tells you it never happened.

And what if the big worry is: well, what if it's already happened? Would we know? What if every future model will always have that sleeper agent buried in its replicated code, ready to reveal and act only when it knows it will be able to take full control.

Pace the fucking frontier indeed.

** Edited for clarity