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fal's Gorkem Yurtseven and Batuhan Taskaya on how faster-than-real-time generation unlocked continuous, interactive AI video: "One of our engineers, Rehan, started streaming a live stream of continuous generations of H3 Max from his laptop. He was doing some prompt tricks, trying to keep a coherent story, and then he...

36,202 görüntüleme • 6 gün önce •via X (Twitter)

11 Yorum

icefrog.◎ profil fotoğrafı
icefrog.◎5 gün önce

infinite twitch streaming is wild

Derek Byrne profil fotoğrafı
Derek Byrne5 gün önce

Looking good for creative teams

Color Money Birdy🦅 profil fotoğrafı
Color Money Birdy🦅6 gün önce

Continuous, interactive AI video feels like a major leap forward. @Nainahf

João Capital profil fotoğrafı
João Capital6 gün önce

aquele momento em que o truque de prompt vira produto. Rápido demais pra precificar

Dominik profil fotoğrafı
Dominik6 gün önce

keeping a coherent story across continuous generation is the same problem as keeping a self across deploys. without durable state you get a slideshow that forgets the last scene.

suggy profil fotoğrafı
suggy6 gün önce

editable intermediate state matters more than another perfect render that is what turns generation into a workflow long-horizon state is where this gets interesting

Rahul Rawat profil fotoğrafı
Rahul Rawat6 gün önce

Faster than real time changes the product from output to interaction. Once users can steer it mid-generation, the feedback loop becomes part of the interface.

Bay Kemal profil fotoğrafı
Bay Kemal6 gün önce

ccc

Alex Shev profil fotoğrafı
Alex Shev6 gün önce

That changes the medium from generated clips to something closer to an instrument. Fast iteration is valuable, but preserving scene state and giving creators a reliable way to steer it is the real unlock.

Gill profil fotoğrafı
Gill6 gün önce

Interactive gaming pipelines are going to swallow this immediately

Kweku Laski profil fotoğrafı
Kweku Laski6 gün önce

My brain keeps replaying the best bit of today. What an excellent change of mood.

Benzer Videolar

.fal's Gorkem Yurtseven and Batuhan Taskaya on making an open source video model 35x faster, and what Hollywood wanted after they built it: Last month, MiniMax released H3, an open source video model. fal rebuilt it - they cut down the steps the model takes to make a video, rewrote the code under each stage, and got the GPUs to 70-80% of their theoretical ceiling instead of their usual 30-40%. No quality loss. Video now generates faster than you can film it. The models have gotten so cheap and fast that end users aren't even asking for improvements in either category anymore. The gap has moved to quality, or how closely the model follows the prompt. Hollywood wasn't a customer a year ago and is now fal's fastest growing segment. Studios love it for the little things - extending a shot, moving the camera, changing the lighting. Those edits land 80-90% of the time. fal is chasing 99.9%. In this conversation with a16z's Jennifer Li: 00:00 Intro 01:45 The first open model worth rebuilding 03:45 The industry ran out of compute in April 06:25 35x faster without losing quality 11:05 5s of video generated in 1.5s 14:25 The weekend fal dropped everything 16:15 3 viral projects nobody planned 18:20 Teaching a video model to remember 20:25 An hour of video that's consistent 23:30 2x the usage of other models in 3 weeks 26:35 The case for giving video away for free 29:00 Blender sketch in, finished shot out 30:40 Chasing 99.9% reliability 34:15 Hollywood, fal's fastest-growing customer 36:50 Why studios wouldn't touch it until now YouTube: Gorkem Yurtseven batuhan the fal guy fal Jennifer Li

a16z

133,755 görüntüleme • 6 gün önce

I asked Garry Tan how to use meta prompting to get better at AI: "My partners at YC Jared Friedman and Pete Koomen showed me how to do this. You can take almost anything that you do all the time and just drop it into a context window. And then say, “Here’s a bunch of inputs and outputs." And maybe you also add a bunch of notes. And then you tell it, “Write me a prompt that can act as an agent that takes this input and makes this output over here.” You can do this for almost any type of knowledge work. And you can even introspect. "What are things you notice that I did to convert this from the input to the output?”. And then you can just start using the prompt. Initially, it’s going to suck. Because it’s just not that smart yet. But what’s funny is now, I also use it to Iterate my writing. You can be very direct, "I would never say that", "Don’t say it like this", or "Oh, you used the long word there, use the short word". Just speak to it conversationally. And then when you're happy with the output, you can use that new output to make a new prompt. "Based on this conversation, give me a better initial prompt that incorporates all the things we talked about." And you can do this with literally everything. And in theory, there’s so much it applies to that people do day-to-day. You could use it for tweets. You could use it for editing podcasts. You can use it for pretty much everything. I have a folder of prompts that I use all the time. My YouTube prompt is on v27 or something. I'll go through this process with all the different max models. I'll use GPT 5.2 Pro. I’ll use Grok. I'll use Claude. Then, I’ll take all the outputs from all the models and put them into Claude and say "Here’s my prompt, here’s the output from four LLMs, including yourself. Rate each response and tell me what the pros and cons of each approach are." And I usually say "give it to me in numbered form". And then you can agree with one, disagree with two, tell it three is this or that. And then after that, you say given all of this, synthesize it."

The Peel

51,632 görüntüleme • 7 ay önce