
Scenario
4.7K posts

Scenario
@Scenario_gg
The creative AI engine studios actually use in production, at scale. Build custom pipelines & workflows across Images · Video · 3D · Audio. Your brand, your AI.












P-Video-Animate is now the fastest model for top-quality image-to-video motion transfer. - $0.03/s at 720p and $0.06/s at 1080p — a fraction of typical motion-transfer pricing (~$0.07–$0.35/s at 720p in our benchmarks) - 5.24 s generation time per second of output video at 720p — roughly 7× faster than typical motion-transfer alternatives in the same benchmarks - Upload one still plus a driver video to preserve motion, timing, camera movement, and scene structure, with optional source audio and clips up to ~2 minutes at 720p Ideal for UGC ads, gaming cinematics, film casting, and product demos when you already have a winning take and want a new hero still to follow it. Try it in the playground: …-playground-production.up.railway.app/p-video-animate We’re launching with 70% discounted usage for the coming two days with our launch partners. @eachlabs, eachlabs.ai/pruna/p-video/… @replicate, replicate.com/prunaai/p-vide… @runware, runware.ai/models/prunaai… @scenario_gg, scenario.com/models/p-video… @wavespeed_ai, wavespeed.ai/models/pruna-a… @wiroai, wiro.ai/models/pruna-p… @TellersAI, buff.ly/RBmFEco Soon also on @togethercompute We’d love your feedback as you try it. 📚 Model docs & API examples: docs.pruna.ai/en/stable/docs… 📚 API quickstart: docs.api.pruna.ai/guides/quickst… ⭐️ OSS: github.com/PrunaAI/pruna 🌐 pruna.ai






everyone is talking about self-optimizing loops in software & agents. but what does that actually mean? in my mind, it's a system that observes it's own outputs, evaluates them, and uses that signal to improve itself in the future. the reason why it has become so popular now, is because the evaluation step is finally reliable with llms. this wasn't really the case a year ago. this is why i'm so bullish on langsmith engine. we've incorporated a ton of different concepts that allow developers to invest in this self-optimizing loop that makes the improvement flywheel spin faster & faster. some examples of this include > feedback you leave on traces are automatically triaged > every fix that we suggest has an online evaluator, so you never regress > we create offline evals that you can add to your test suite > we continually learn on your preferences, and tune our evaluation & fix step based on this and we're seeing crazy adoption, and lots of growth across our customers. it is truly something that just gets better the more time you spend on it.

