Ramzi RAMZI

[Ramzi RAMZI @RhR11216]

@JeffDean We are hiring if interested

[Ramzi RAMZI @RhR11216]

@bryan_johnson Hell no . We look up to u to make it you’ve been bold enough , keep the work and sorry for your disease i hipe u get better , ❤️

[Ramzi RAMZI @RhR11216]

@GeneralistAI Can startups use your models on there robots ??

[Ramzi RAMZI @RhR11216]

@t_blom @ycombinator Insightful

[Ramzi RAMZI @RhR11216]

@steipete Love u dude

[Ramzi RAMZI @RhR11216]

@H0meMadeGarbage Really cool and fun

[Ramzi RAMZI @RhR11216]

@REM__BEN Absolute means : considering the reference of interest is center of planet earth

[Ramzi RAMZI @RhR11216]

@REM__BEN It’s reacting againg a car moving 60 km/h , relative speed (refrence car ) is 0km/h , absolute speed 60km/h

[Ramzi RAMZI @RhR11216]

@michelleefang Where r the physical AI ones

[NVIDIA AI @NVIDIAAI]

This #CVPR2026 paper from our research team is trending #1 on @HuggingFace 🤗

Meet LocateAnything: a vision-language detection model that rethinks bounding box prediction. For AI agents and robots, “seeing” is only useful if a model can pinpoint where something is fast enough to act.

Trained on 138M high-quality samples, LocateAnything decodes bounding boxes in parallel instead of one coordinate at a time, improving localization accuracy while dramatically increasing throughput for visual grounding and detection.

Project page: https://t.co/O7JMe8tzFM

[Ramzi RAMZI @RhR11216]

@michelleefang Can u do more on physical AI please

[Jim Fan @DrJimFan]

I promise this will be the best 20 min you spend today! Robotics: Endgame, the sequel to my last year's Sequoia AI Ascent talk, "Physical Turing Test". I laid out the roadmap for solving Physical AGI as a simple parallel to the LLM success story. Be a good scientist, copy homework ;)

And stay till the end, more easter eggs and predictions for your polymarket!

00:30 DGX-1 origin story at OpenAI, I was there in 2016 signing with Jensen and Elon. Heading to the Computer History Museum!
01:42 The Great Parallel
03:31 Robotics, the Endgame
03:39 Why VLAs fall short
04:32 Video world models as the 2nd pretraining paradigm
06:09 World Action Models (WAM)
07:46 Strategies for robot data collection and the FSD equivalent to physical data flywheel for robot manipulation
11:06 EgoScale and the Dexterity Scaling Law we discovered recently
14:00 Physical RL: bridging the last mile
15:39 DreamDojo: an end-to-end neural physics engine for scaling RL in silico
17:00 Civilizational Technology Tree and my predictions for the near future. Spoiler: it's closer than you think.

Thanks to my friends at Sequoia for inviting me back to AI Ascent this year! I had a blast! Last year's talk is attached in the thread if you missed it.

[Ramzi RAMZI @RhR11216]

@DrJimFan I love you

[Ramzi RAMZI @RhR11216]

@saranormous Can i pitch in your add ??

[Ramzi RAMZI @RhR11216]

@norma_core_dev Is the open arm for sell ??

[Ramzi RAMZI @RhR11216]

Wuji hands , @gs_ai_

[Ramzi RAMZI @RhR11216]

https://t.co/vRohFhgyNM

[Ramzi RAMZI @RhR11216]

@brivael @gs_ai_ This is amazing work , i wanna use it fory startup

[Ramzi RAMZI @RhR11216]

https://t.co/LBbf5aSCPd

[Ramzi RAMZI @RhR11216]

@quxiaoyin I think this is amazing insights after all the nonsense around the AI , would love to hear the first principles or experiences that lead to this conclusions.