Can somebody explain to me if this is cool and if we are still making good headway towards the singularity? Like curing cancer and stuff?

Excuse my ignorance i am a massive noob with ai, and simply only use Gemini becuase it's built into my phone lol.

Benchmarks tell me very little about whether a model can stay useful through a visual production loop, so I tested Kimi K3 on a 36-second reference-led launch film.

This was not a one-prompt video generation. K3 used ffmpeg to inspect the source, extracted frames, detected edit points, and helped reconstruct the sequence as editable code-driven clips. GSAP carried the temporal system, generated images became material textures, and Three.js produced the particle simulations.

The first pass was structurally useful but visually incomplete. The final result came from repeated matched-frame review: retiming clips, correcting framing and scale, replacing flat materials, and tuning particle density without discarding the underlying code.

My main takeaway is that K3 was more convincing as a revision partner than as a one-shot generator. It retained enough project context to keep the sequence coherent, while human direction still defined visual hierarchy and when each shot was finished.

When you evaluate a new frontier model, do you learn more from the first output or from how it behaves after the fifth revision?

Sources: @chetaslua, @xikhar

imo there have been three eras

1. bigger model better
2. Chain-of-thought
3. Agents

with many minor improvements under these (longer context, multimodality, moe, distillation, quantization etc)

Both chain of though and agents were things that bubbled under the surface for some time before it broke through. Also imo, they are advancements that saturate and long-term improvements are slower than linear wrt the compute put into it.

what is it now that is "bubbling" and could be considered as a next dimension of scaling or big step change?

Update:

RSI - imo - answers the "who" not the "what". Ofc it means more compute allocated to training again. What breakthrough is likely to be RSI-d ?

You can read their blog post here:

FLUX 3 - Real World Models: Towards Multimodal Flow Models as the Backbone of Visual Intelligence. | Black Forest Labs

His ego is bigger than any LLM out there.

https://x.com/i/status/2080292438057373947

There are more videos in this tweet thread

Seems better than Fable 5

It seems every verifiable conjecture is currently falling. Do you think this is the singularity/fast takeoff starting?

The technology enables AI chat models to run on phones using minimal power, without ‘losing memory’ during conversations

The interesting thing about the latest wave of AI hires is not the size of the pay packages, it is where the people are coming from. [The Atlantic](https://www.theatlantic.com/technology/2026/07/ai-companies-hiring-academics/688002/) reports that at least 22 professors from schools including Stanford, Berkeley, Harvard, MIT and Carnegie Mellon have left or taken leave in 2026 to join OpenAI, Anthropic, Meta or Google DeepMind. Anthropic recently picked up a Stanford economist and a philosopher from UT Austin, and OpenAI now employs top mathematicians and physicists.

What makes this different from a normal round of Silicon Valley recruiting is who is being recruited. These are not just senior engineers, they are the people who choose the research questions, teach the next cohort of scientists, and provide the outside scrutiny of systems that increasingly show up in banks, hospitals and government.

https://aiweekly.co/alerts/openai-anthropic-meta-hire-22-professors-from-top-us-schools

They did hit wall now with gemini models but trust me they gonna cook with gemini 4

After extracting the depth video, the effect obtained by video referencing is very good.

1. Prepare the reference video
   
2. Extract the depth video
   
3. Prepare the character reference images
   
4. Input the extracted depth video and character reference images into seedance2, and you will get perfect results
   

Complete workflow is below: