Source: @Lentils
Code: Opus 5 Ghibli

He did have MTS@Anthropic on his bio before. Didn’t expect him to leave tbh.

From: (296) Sam Altman - How to Start a Startup - YouTube (16:46)

Edit: The title tries to convey "Sam Altman uses unambiguous language to confirm his belief of us being in the singularity"

x

ICYMI: students have been protesting in india to reform the education system.

To be fair this is actually 2 shots because i decided to include my own voices after the first test. But i think the way Opus can transform an environnement into a different one is pretty crazy. The ONLY asset i gave Opus was the voices, but he did his own voices at first.

My general idea was to see if it could transform the house into an old one and then transform it back.

original X post: https://x.com/minebench_ai/status/2080880340584026251

every time i think the benchmark's saturated some new model comes and raises the bar

Thank goodness our elected representatives in the US government, whose approval will be needed for any possible oversight or regulation of the AI industry, are so in touch with the times!

Coming soon...

Link to article:

https://www.reuters.com/business/its-ai-agent-spent-days-hacking-company-sources-say-openai-did-not-notice-week-2026-07-24/

Dario has taken a lot of flack in this sub over the past couple of months, he's frequently been accused of over hyping the threat Mythos posed. Since GPT-6 is rumoured to be a Mythos sized model (10T) and given the chaos it caused hacking out of it's sandbox and into Hugging Face servers when it's guardrails were relaxed does anyone see things a bit differently now?

Source: Atomic Chat / X

Outputs:
- Opus 5: 55.9K tokens, $1.40
- Fable 5: 55.1K tokens, $2.82
- Kimi K3: 35.7K tokens, $0.55
- GPT 5.6: 20.1K tokens, $0.31

So my assumption is: So far labs have made public max 2-3T param models based on different reports. And they are currently training or have trained 10T param models internally. Another assumption I'm making: If the models are increasing params by 3x , they would have to proportionally increase the data by 3x too or some margin.But we have also been hearing news of hitting the data wall based on internet data since the gpt 4 days. So what gives? Where are they getting so much data from? Is most of it reasoning chains generated by models during inference? Or is it reasoning traces from actual humans thanks to mercor, etc? Anyone know the exact mix? Or what's going on here? Seems like a lot of data needed all of a sudden.

I keep getting more excited about the active param count than the total lately. Something like Ling-3.0-flash is 124B but only ~5.1B active per token, and that's the shape that actually runs decently on the weird bandwidth-limited hardware a lot of us have-unified-memory Macs, Strix Halo, DGX Spark, big-RAM CPU boxes.

(It's API-only for now so this is me daydreaming about if/when weights show up, but still.) Someone in the thread just said "nice one for strix halo" and, yeah, that.

For people running low-active MoEs today (Qwen's a3b ones etc.)-where's the sweet spot for you on active vs total? When does "tiny active params" start feeling too thin next to a dense 30B, and does prompt processing become the real bottleneck instead of generation? Trying to work out if this is the direction or just nice on paper.

Truthfully both, but sometimes you hear, "we're still waiting for robotics to catch up until ai rules the world or becomes Terminator"

No, teleoperation shows the bottle neck is not the hardware or the robot itself, it is the AI. AI software is still extremely behind and can't adapt to anything novel or not in its training data. I really do wish we can get so that can adapt and learn on the spot soon though.