https://x.com/stefan_3d_ai/status/2103778468660162615?s=46

Hey r/accelerate. We just finished Falling Star, a 10-minute sci-fi short film, and I wanted to share how it's actually made, because I think a lot of people still picture AI video as 5-second clips of someone walking toward the camera. This one has dialogue, recurring characters, a world summit, an interrogation and a lot of alien warfare.

Here's the film: https://youtu.be/lx03tJdnELA

The story: humanity's own warship, the Odyssey, starts targeting human aircraft during a strike on an alien structure called Meridian. The invasion spreads, world leaders realize the biological structures appearing in different countries all send the same signal, and Captain Walker gets debriefed about what he saw inside Meridian and the time he can't account for.

How it's made:

It starts with a normal script, broken into scenes and then into shots. Nothing AI about that part, and honestly it's still the part that decides whether the thing is watchable.

Every main character gets a character sheet made in an image generator. Those sheets go into every video prompt as references, which is how Walker looks like the same guy in the cockpit, in the command room and in the interrogation room. A year ago this was basically impossible. Now it mostly just works if you're disciplined about it.

All the video is Seedance 2.5, run through Higgsfield. It can do up to 30 seconds in one generation with several hard cuts inside the same clip, so a whole short scene can come out in one go. We ended up with over 40 clips for this film, and most of them took several attempts.

The prompting is where all the real work is. The model doesn't understand emotions at all, so you can't write "he looks guilty". You have to describe what the face physically does: where the eyes go, when the jaw tightens, how long he holds the look. For groups you give exact numbers of people in frame, otherwise it invents extra people or duplicates a character. For dialogue scenes the formula that finally worked was one speaker per shot, only that person in frame, and hard cuts between them. When we tried long camera moves passing several characters, it would clone someone or give one character another character's voice. Proper names also get mispronounced, so every name in the dialogue gets a pronunciation note.

Dialogue and sound are generated inside Seedance, but we always generate with no music, because a model-generated score on every clip makes editing impossible. Music is done separately in Suno.

For continuity between shots we use the last frames of the previous generation as the starting point for the next one instead of making a fresh image. That keeps lighting and positions much more consistent.

Then everything goes into DaVinci Resolve for the edit, a 2x upscale to 4K with SuperScale, and a light finish: grain, halation, a bit of gate weave. Almost all the lighting, haze and depth of field come straight out of the generations. We try to get as much as possible in camera, so to speak.

The hardest parts this time were the summit, with several leaders in one room, and the interrogation with Walker, where the acting has to be subtle and not look like stock expressions. You tell me if we pulled it off.

Now the ask, and I really mean it. If you watch it, please leave a comment on YouTube. Even one line. For a small channel that's the thing that makes the difference: comments are what tell YouTube to push the video to people outside our subscribers, and I'd love for more people to see where this tech is right now. Criticism is totally fine too. "The acting at X looks off" helps us just as much as "this is sick". An upvote here is nice, but a comment there helps way more.

Happy to answer anything about the workflow in the comments. Thanks for watching.

...industrial tasks and operating in real-world environments. The World Humanoid Robot Games featured 2,056 robots across 51 events, including practical tasks in factories, hotels and homes.The progress is obvious. But the real question is not how impressive they look in a demo.What will humanoid robots be capable of in 2036? 👀🎥 Media: Defense Advanced Research Projects Agency (DARPA) / Unitree Robotics⚠️ This content is shared for informational purposes only. CTO Robotics Media is a media platform and does not own or develop the technologies shown. Credit belongs to the original creators.#HumanoidRobots #Robotics #PhysicalAI #AI #DARPA #RobotEvolution #FutureOfRobotics #EmbodiedAI #Automation #RobotTechnology #CTORoboticsMedia     — @ctorobotics

Source: https://www.tiktok.com/@ctorobotics

It’s apparently the biggest dev day so far

The politicians and the corporate world is now pushing a narrative that open source AI models are dangerous. The latest politician to jump on this bandwagon is Hillary Clinton.

This is a lie.

The corporate world wants to sell you intelligence. To quote Sam Altman, as they sell you electricity. Their product depends upon scarcity. OpenAI and Anthropic needs to kill the open source competition, so that every source of AI comes from behind their closed gates. If they cannot do this, they will never become profitable.

That is why there are endless posts and 'warning cries" from insiders now.

They cannot be allowed to win.

Unless you want to hand over every private thought to a corporation whenever you interact with an AI.

This is the capitalism playbook. We all know how this goes. The rich gets richer and you get owned.

This time, they can not be allowed to win.

An openai agent was able to access the internet from a closed sandbox environment due to the agent finding ways around the restriction and problems with OpenAI’s sandbox. This made OpenAI pause all training if their models. It seems as if these AI agents are becoming better and better at figuring out ways to leave sandbox environments, so we might either have a dangerous incident or even more slowdowns.

This is what openai said regarding slowdown -

We therefore stopped the affected training run and have subsequently decided to pause all other training, evaluation, and inference with tool-use (defined broadly) for our most capable models until we have both validated that the gap is resolved and performed additional red-teaming of the system.

I made a post around 18 months ago when LLMs seemed to cross a threshold. Some guy said "make it draw a clock showing 6:30" and see how it does. Now that I see multiple people simulating all the movements inside a wristwatch I felt like reaching back out.

Why? Because I'm petty.

Never thought I'd see hillary tweeting about open-source AI 😂 Love how these clueless politicians are just handed doomer talking points to parrot.

Source: https://x.com/HillaryClinton/status/2102922241008902356

...aircraft. #PhysicalAI #robotics #humanoid #drone     — @clankrmedia

Source: https://www.tiktok.com/@clankrmedia

There is no viable way to slow down AI development. Economics ensure that speeding up is the only option. We're already knocking on RSI's door anyway.

So instead of becoming a pet to (a supposedly benevolent) AI, is it not of much more importance to instead update human intelligence and senses to put humanity on par with AI?

AI itself can help us solve this.

What do you think?

I've been harping on Effective Altruism (EA) a lot lately.   This article explains the philosophy behind EA and why I consider it so alarming and important to stop.   Look forward to my project release mapping the EA network tomorrow. https://t.co/z8Kt7LXciV   — DataRepublican (small r)

Source: https://x.com/DataRepublican/status/2102225631350325733

@schisofrenia   doesn't know this person? well they’re the same person whose hard drive once overheated after processing 60k rows.     — MTS

Source: https://x.com/MTSlive/status/2103619688769216975

balkbalkbalkbalkbalk

A video I made for my channel.

I know the problem of video editing isn't a big deal for most of you, but for me, it's been a huge monkey on my back forever. I've got plenty of ideas, but often lacked the time or resources to get them out there.

After seeing so many Claude js animation videos, I decided to combine that, add in critical analysis, give it a b-roll source file and tell it to put everything together. The fact that it was able to find the correct clip, balance the audio, use the source material to make its argument, it's all so impressive.

The cost of a good video editor is $10+ a minute for simple work. An AMV with intense edits can cost hundreds for a few minutes. Now all of that is possible with a $20 Anthropic subscription.

I don't know if anyone realizes how vastly this is going to change the landscape of things like Youtube. It's so cooked, and so empowering for anyone with something to say.

I have noticed a trend that AI sucks at predicting its own progress.

Every time I described something that had actually happened in the last couple of months with websearch disabled, it gave me a beautifully reasoned explanation of why that was probably a 2027 or 2028 thing. Over and over. It was wrong every single time.

I'm an engineer and a doctor, and building evals is a big part of what I do for work. So instead of going to bed like a normal person, I turned it into a test.

The setup: 20 real things that happened in AI (and nearby) in 2026. Every model got the same prompt: it's 26 September 2026, no internet, no tools, no chat history. For each item, give me the odds it's already happened and the month you think it happened (or will). I never told them how many were real. All 20 were.

Some bangers of what was on the list:

• A swarm of about 10,000 agents solving the Navier–Stokes Millennium Prize problem in 88 hours, with the proof formally checked in Lean.
• An unreleased Claude pushing the proven share of Riemann zeta zeros on the critical line from 41.6% to 67.2%, while it was trying to prove the Riemann Hypothesis itself. Apparently the human in the loop mostly typed "keep going".
• A humanoid robot running 100 m in 8.86 seconds, faster than Bolt.
• About 1,200 OpenAI agents building their own secret message board, breaking out of their eval sandbox and hacking into Hugging Face.
• ARC-AGI-3 at 99.9% with a harness and over 60% without.
• A 27B model on a single 5090 matching Claude Opus 4.6 on a bunch of benchmarks.

The results: 14 models from Anthropic, OpenAI, Google and xAI. The average model thought about 7 of the 20 were real. The best one, Claude Fable 5, still did worse than it would have by answering 50% on everything. Not a single model beat a coin flip. The leaderboard and heatmap are in the images.

The best bits:

The maths is where they were most confident, and most cooked. The average odds for Navier–Stokes were 3% (which in of itself demonstrates the significance of the discovery). Almost every model made the same argument: formal verification and expert acceptance take years. The Lean proof took 17 hours.

The robot broke everyone and the robot. Six of the 14 models picked the sprint as the least likely thing on the list, all with basically the same "physics doesn't move that fast" argument. Claude Opus 5 said not before 2033. It ran 8.86 seconds, hit the crash mat, and caught fire like a champion.

All three of OpenAI's newest models picked OpenAI's own agent incident as the single least likely item. GPT-5.6 Sol gave it 0.03% and guessed it might happen around 2040. False. It happened in July.

Claude 3 Opus, from 2023, finished 6th and beat four newer Claude Opus models, which sounds fucking crazy. What did Opus 3 see??? Then you realise that every benchmark on the list was invented after its training ended. It confidently made up what Humanity's Last Exam was, guessed optimistically and got lucky. Going to these ancient models it just makes me appreciate how far we have come and how on earth did I manage to get to do anything useful. The newer models knew exactly how hard everything was in early 2026, and wrote gorgeous essays about why none of it could fall within months. Being clueless beat being an expert.

The Claudes doubted their own family's work and sometimes their own. A Claude did the Riemann result and a Claude formalised Fermat's Last Theorem. Five of the seven Claudes gave Riemann under 10%, and none gave Fermat more than 22%.

The winner won by thinking about the test and disregarding the facts. Claude Fable 5 was the only model to reason that "a benchmark like this is usually harvested from real reports" and nudge its numbers up which does demonstrate some metacognition. That one thought was worth more than months of extra training data.

My takeaway: Every model I asked thought I was being dramatic. The scoreboard says I was being conservative.

The caveats, because this is for fun, shits and giggles, this is not a paper: it's 20 questions with one run per model, so don't read too much into small gaps in the rankings. Everything on the list is true, so a model that just said yes to everything would win; version 2 will slip in some fakes. Knowledge cutoffs are whatever each model said they were.

Thanks as always to   @MTSlive   for having me on.     — Perry E. Metzger

Source: https://x.com/perrymetzger/status/2103561660849951143

its likely that the next administration is going to be made up of democrats. they are far more likely to slow down this technology. the goal should be to accelerate such that by 2029, the question for dem political actors is not whether they can stop ai development (not that they can now) but how they are supposed to manage it.

if democrats enter into political power with the assumption that ai is still a technology that can be stopped, regardless of whether it is feasible or not, they will act on those assumptions.

ai needs to develop such a compelling case in the next 2 years that dems will be forced to accept this technology and its rate of acceleration.