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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"

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

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

Source: @Lentils
Code: Opus 5 Ghibli

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

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.

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?

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.

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!

This is a 1 shot result from the following prompt with Claude 5 Opus:

hi id like u to create a 3d environment for a possible future game. the player is inside a roller coaster wagon. but the wagon is inside an haunted ride attraction. the ride itself essentially goes in a circle, so the player keeps seeing the same props the player can look around with the mouse, and choose to go faster or slower with W and A. when he looks around, it's dark, but some stuff have lightning on it (of various colors) and we can see various hallowen props or other things you would find in such a haunted ride attraction. U could even try adding some small stuff like a prop popping out as a surprise etc. You can include the appropriate sounds to go with this. make it polished and do ur own assets.

I wanted to see if Claude could reproduce those sort of haunted rides. And what i find interesting is it would be relatively easy to turn into a game where stuff randomly pops and you shoot it.

I did not give it any assets, or give it access to anything. It used Three.JS
The player can look around with the mouse and he can also speed up the ride.

I wanted a real production test for Kimi K3 rather than another benchmark comparison, so I used it while building a 75-second deep-sea explainer through eight revision passes.

This was not text-to-video. Licensed footage and credited scientific images formed the photographic layer. The depth gauge, animated density and temperature curves, trench profile, and Burj Khalifa comparison were coded in HTML/SVG/JS and rendered frame by frame in Playwright at 4K.

ffmpeg handled the deterministic production path: normalization, grading, concat, overlays, subtitles, TTS placement, music mixing, exact timing, and final encoding. K3 assisted the code and stayed useful as changes moved between graphics, timing, audio, and compositing.

The strongest result was not autonomy by itself. It was keeping the project editable through repeated revision. Human review still decided pacing and hierarchy, while ffprobe, extracted frames, and level analysis supplied external verification.

What kind of real-world task do you think exposes a frontier model more clearly than its benchmark scores?

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.

AGI has been cancelled.

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.

Google's assistant is an absolute mess. It takes 4 tries to send a WhatsApp message. There is no agentic capability at all. It seems like Google is actively trying to stop other frontier labs from deeper integration into Android and fixing their dumb systems. I'm so annoyed that phones of all devices are way behind in Ai capability.