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NVIDIA AI
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NVIDIA AI
@NVIDIAAI
Teaching your AI new tricks.
Santa Clara, CA Katılım Haziran 2016
878 Takip Edilen312.9K Takipçiler

Hackathon entry: Clipit, an agent-first, video-forward social media tool.
Clipit helps agents turn long-form content into short form clips and distribute them across social platforms.
Clipit plugs into the agent environments people already use.
The workflow is simple:
Give the agent a video, stream, podcast, or content library
Clipit digests the source and finds usable moments
The agent generates clips, captions, thumbnails, reframes, and post copy
Clipit queues or publishes the final assets
The human stays in the loop for taste, approval, and brand judgment
The bigger idea: agents need tools that let them operate, not just answer.
Clipit is the creation, editing, and posting layer for short-form video. We are building the searchable memory layer behind it, helping agents find old material, resurface context, and keep a brand’s video history useful.
Together, they give agents a real media workflow: find content, generate clips, edit them, and distribute them.
Clipit is the video production layer for AI agents.
@NousResearch @NVIDIAAI @stripe
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Join us at #SIGGRAPH2026 for more papers, breakthroughs, and sessions. nvda.ws/4vtIiHQ
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3D scene reconstruction works great until the camera never sees part of the scene.
ArtiFixer from NVIDIA Research is an open autoregressive model that fills in the missing geometry that other methods leave blank.
#SIGGRAPH2026 paper, code + demo: nvda.ws/4oILqNd
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Hi everyone! I’ve moved to the Bay Area for a summer research internship at @nvidia. Beyond exciting work, I'd love to meet new people doing exciting stuff (incl. stuff I don't work on myself rn!). If you’re around, I’d love to connect! Even if just for a jam session!

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New work with @nvidia: evaluating robot policies entirely inside a world model. The policy acts, the model imagines the consequences, and the imagined evals predict real-world results. 🧵
real vs world-model rollout side by side📷
GIF
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NVIDIA AI retweetledi

16x parallel Gemma-4-26B-A4B-NVFP4 runs 🤯🤯🤯
18 output tokens/s, aggregate 300 tok/s
1 DGX Spark with 128 GB unified memory
Concurrency so high I had to demo it programmatically
It can go up to 32 even! 🤯 But then my screen would not have been readable for you
And this is not even using flashinfer yet! Please reply if you know whether support is on the way
Note that this is not dumb e4b or e2b that you can run on the average laptop. This is the big Gemma MoE
Model link: huggingface.co/nvidia/Gemma-4…
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Wow, it has happened!
30.55 tok/s on GLM-5.2 4-bit (from @Zai_org) ran by six RTX Pro 6000's across the USA scattered over WAN!
I can't believe this. It was an insane build, you can read more about it on github.com/leyten/shard

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parakeet.cpp transcription running on a Raspberry Pi 5 CPU at 7x real-time on a 60s clip under thermal throttling.
Of course speed and power consumption could be better on a GPU, but still, nice of @NVIDIAAI to release this model.
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@SpaceTimeViking @NVIDIAAI LFG!!! so awesome. thanks for making the @NVIDIAAI spark even more awesome with your developments!
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Qwen3.6 27B getting some love on the new AEON ULTIMATE VLLM image
@NVIDIAAI DGX SPARK OPTIMIZED!
github.com/AEON-7/Qwen3.6…

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