Loading video...

Video Failed to Load

Go Home

GEN-1 plays the 🐚 shell game, trained on just 1 hr of robot data. It also generalizes to unseen objects, like berkay 's car keys. Physical AI models should be capable of benchmark tasks like this one. It's interesting for the all the reasons Rhoda AI calls out --...

GeneralistAI's profile picture

Generalist

11,418 subscribers

860,208 views • 3 months ago •via X (Twitter)

Science & Technology
LIVE

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

We've improved how GEN-1 learns to adapt to new actuators and new robots at the lowest level, with up to 10-20x gains on internal benchmarks. This significantly boosts performance on high-precision tasks like disassembling parts from a NIST board. Read more about GEN-1 in our blog posts in the comments below.
3:24

We've improved how GEN-1 learns to adapt to new actuators and new robots at the lowest level, with up to 10-20x gains on internal benchmarks. This significantly boosts performance on high-precision tasks like disassembling parts from a NIST board. Read more about GEN-1 in our blog posts in the comments below.

Generalist

73,754 views • 10 days ago

Introducing GEN-1. Our latest milestone in scaling robot learning. We believe it to be the first general-purpose AI model to master simple physical tasks. 99% success rates, 3x faster speeds, adapts in real time to unexpected scenarios, w/ only 1 hour of robot data. More🧵👇
3:21

Introducing GEN-1. Our latest milestone in scaling robot learning. We believe it to be the first general-purpose AI model to master simple physical tasks. 99% success rates, 3x faster speeds, adapts in real time to unexpected scenarios, w/ only 1 hour of robot data. More🧵👇

Generalist

382,068 views • 4 months ago

GEN-1 still works with lights off, and generalizes under harsh lighting conditions. The model uses raw video pixels to make decisions, so strong lighting changes can drastically alter its input distribution. Yet performance still holds. Why? GEN-1 was pre-trained on a massive, diverse dataset of different lighting conditions—everywhere from outdoor farms, to warehouses, from grocery stores, to dimly lit homes—it's already seen it all, and transfers this knowledge to new tasks. This is a glimpse of what we call Mastery, and is part of the reason these models can cross a new performance threshold. Read more about it in our blog post in the comments below 👇
0:39

GEN-1 still works with lights off, and generalizes under harsh lighting conditions. The model uses raw video pixels to make decisions, so strong lighting changes can drastically alter its input distribution. Yet performance still holds. Why? GEN-1 was pre-trained on a massive, diverse dataset of different lighting conditions—everywhere from outdoor farms, to warehouses, from grocery stores, to dimly lit homes—it's already seen it all, and transfers this knowledge to new tasks. This is a glimpse of what we call Mastery, and is part of the reason these models can cross a new performance threshold. Read more about it in our blog post in the comments below 👇

Generalist

18,396 views • 3 months ago

Everyday for the past 2 weeks, we've been sharing something new from GEN-1, our latest milestone in scaling robot learning. This has never been done before. Going from ideas to skills in days (or faster) is what physical AI models should deliver. More coming. Stay tuned. Read more about it in our blog post in the comments below ↓
0:42

Everyday for the past 2 weeks, we've been sharing something new from GEN-1, our latest milestone in scaling robot learning. This has never been done before. Going from ideas to skills in days (or faster) is what physical AI models should deliver. More coming. Stay tuned. Read more about it in our blog post in the comments below ↓

Generalist

39,593 views • 3 months ago

GEN-1 delicately arranges potato chips, and lifts a heavy bag of potatoes — from a gentle touch to a strong grip. Read more about Gen-1 in our blog posts in the comments below ↓
1:15

GEN-1 delicately arranges potato chips, and lifts a heavy bag of potatoes — from a gentle touch to a strong grip. Read more about Gen-1 in our blog posts in the comments below ↓

Generalist

89,008 views • 3 months ago

Most robot demos are scripted. Generalist's GEN-1 is not. > GEN-1 was doing a task > Mid-task, an extra object was thrown into the bin > GEN-1 quickly adapted to the new environment > Still completed the task smoothly This is the difference between a scripted policy (seen in viral launch videos) and a real foundation model. GEN-1 was trained on 500,000 hours of proprietary, real-world dexterous manipulation data. It has NEVER seen that exact scenario before. This was the coolest demo at Automate Show Generalist Flexiv Robotics
0:53

Most robot demos are scripted. Generalist's GEN-1 is not. > GEN-1 was doing a task > Mid-task, an extra object was thrown into the bin > GEN-1 quickly adapted to the new environment > Still completed the task smoothly This is the difference between a scripted policy (seen in viral launch videos) and a real foundation model. GEN-1 was trained on 500,000 hours of proprietary, real-world dexterous manipulation data. It has NEVER seen that exact scenario before. This was the coolest demo at Automate Show Generalist Flexiv Robotics

mete

11,514 views • 1 month ago

AI only has pre-schooler level capabilities for visual tasks. "The frontier models—the best of them—still reason around the age of a preschooler, and any elementary school kid in that benchmark was able to beat all the frontier models on these visual tasks." "These are tasks that are not just object recognition. It's about spatial reasoning... tasks like navigating through the maze…” — Andrew M. Dai, CEO of Elorian
0:49

AI only has pre-schooler level capabilities for visual tasks. "The frontier models—the best of them—still reason around the age of a preschooler, and any elementary school kid in that benchmark was able to beat all the frontier models on these visual tasks." "These are tasks that are not just object recognition. It's about spatial reasoning... tasks like navigating through the maze…” — Andrew M. Dai, CEO of Elorian

The Information

25,680 views • 1 month ago

For generative AI to become an interesting art tool, we need much more control over the output. The slot-machine-like nature of pure text-to-image leaves too much to chance. Using the "Real-time Latent Consistency Model" that I'm using in the example here, is the first time I truly got a glimpse of a future where we'll be able to use our artistic skills and sensibility, to get control over AI image gen. Systems like these will never be able to match the quality or originality of a skilled artist, it won't surprise us in the same way an artist can. Things are a mess in terms of the training data these models are based on, and the questions about copyright concerns and about a time when everything will look the same are very valid. At some point capabilities like these will be embedded in photoshop, and anyone will be able to generate a pretty picture. But to create interesting designs, to tell original stories and to surprise us, we need creatives and artists with something on their mind. We'll be able to create immersive worlds, by making brush-strokes and sculpt marks, without needing to worry about all the dials, plugins, wires of our 3d and 2d tools today. I love to sculpt, I love to draw, and I love to explore new mediums and new ways to create. The Gen AI tools we have today are far from perfect, and things need to be steered in a better direction. For that we need artists to help point the way. Gen AI isn't going away.. it's too powerful and has the potential to allow us to tell stories like never before. Like all other big technological shifts, tech like this will come at a cost, but it will also open up new opportunities and empower a new generation of storytellers. I might be naive, but I believe that human ingenuity and creativity will persevere in this new world ♥️ #art #ai
0:32

For generative AI to become an interesting art tool, we need much more control over the output. The slot-machine-like nature of pure text-to-image leaves too much to chance. Using the "Real-time Latent Consistency Model" that I'm using in the example here, is the first time I truly got a glimpse of a future where we'll be able to use our artistic skills and sensibility, to get control over AI image gen. Systems like these will never be able to match the quality or originality of a skilled artist, it won't surprise us in the same way an artist can. Things are a mess in terms of the training data these models are based on, and the questions about copyright concerns and about a time when everything will look the same are very valid. At some point capabilities like these will be embedded in photoshop, and anyone will be able to generate a pretty picture. But to create interesting designs, to tell original stories and to surprise us, we need creatives and artists with something on their mind. We'll be able to create immersive worlds, by making brush-strokes and sculpt marks, without needing to worry about all the dials, plugins, wires of our 3d and 2d tools today. I love to sculpt, I love to draw, and I love to explore new mediums and new ways to create. The Gen AI tools we have today are far from perfect, and things need to be steered in a better direction. For that we need artists to help point the way. Gen AI isn't going away.. it's too powerful and has the potential to allow us to tell stories like never before. Like all other big technological shifts, tech like this will come at a cost, but it will also open up new opportunities and empower a new generation of storytellers. I might be naive, but I believe that human ingenuity and creativity will persevere in this new world ♥️ #art #ai

Martin Nebelong

1,660,244 views • 2 years ago

We asked rahul about the global reach of ChatGPT and the emphasis on personality in AI models. "When you want to get to the one to like the remaining 90% of the users, do you want a model that thinks for longer? You want more personality?" "They definitely leaned in on personality, which I think is interesting." "It's kind of different than a (social media) feed in some ways because you have this person you talk to. It's like a relationship and then it just switches up on how it talks to you."
0:50

We asked rahul about the global reach of ChatGPT and the emphasis on personality in AI models. "When you want to get to the one to like the remaining 90% of the users, do you want a model that thinks for longer? You want more personality?" "They definitely leaned in on personality, which I think is interesting." "It's kind of different than a (social media) feed in some ways because you have this person you talk to. It's like a relationship and then it just switches up on how it talks to you."

TBPN

89,226 views • 1 year ago

ELON MUSK: IT'S INCREDIBLY IMPORTANT FOR AI MODELS TO BE GROUNDED IN REALITY - "Physics is the law, and everything else is a recommendation" - People break human-made laws, but no one breaks the laws of physics - AI must be grounded in reality to ensure it is truthful and accurate - Examples of this necessity: Cars: Must drive safely and correctly. Optimus (Humanoid Robot): Must perform the physical tasks it is asked to do - Adhering to the laws of physics is helpful for ensuring the model is truthful - A somewhat overlooked point: To be really intelligent, AI has to make predictions that are in line with reality
1:08

ELON MUSK: IT'S INCREDIBLY IMPORTANT FOR AI MODELS TO BE GROUNDED IN REALITY - "Physics is the law, and everything else is a recommendation" - People break human-made laws, but no one breaks the laws of physics - AI must be grounded in reality to ensure it is truthful and accurate - Examples of this necessity: Cars: Must drive safely and correctly. Optimus (Humanoid Robot): Must perform the physical tasks it is asked to do - Adhering to the laws of physics is helpful for ensuring the model is truthful - A somewhat overlooked point: To be really intelligent, AI has to make predictions that are in line with reality

X Freeze

45,446 views • 8 months ago

Coinbase CEO Explains “Reverse Prompting” and the Rise of the AI CEO Brian Armstrong: “One of the big pushes we made in the last year was we got our own internal hosted AI model that was connected to all of our data sources, right?” “So it's like every Slack message, every Google doc, Salesforce data, Confluence, you know.” “So now the data is all aggregated and I've started to ask it really… it's not just like prompting it, ‘Hey, can you write this kind of memo for me,’ or something.” “I'm asking these AI agents now, ‘As CEO, what should I be aware of in the company that I might not be aware of?’ And it'll tell me, ‘Did you know that there's actually disagreement on this team about the strategy?’ And I was like, actually, I didn't know that.” “This is like reverse prompting. So instead of telling the AI agent what you want it to do, you ask it what you should be thinking more about.” @jason: “It's a mentor. It's a coach.” Brian: “Yeah. Like, what could make me a better CEO? And it's like, ‘Well, I looked at how you spent your time in the last quarter and here's how you said that you wanted to spend it, but you actually spent 32% of your time on this instead of 20%.’” “I've asked it other questions like, ‘What's the thing that I changed my mind on the most over the last year?’ Things like that.” “It'll prompt you with information you should be thinking about instead of the other way around.” Thanks to our partner for making this happen!: Our episode is sponsored by the New York Stock Exchange - a modern marketplace and exchange for building the future. It all happens at the NYSE 🏛.
1:16

Coinbase CEO Explains “Reverse Prompting” and the Rise of the AI CEO Brian Armstrong: “One of the big pushes we made in the last year was we got our own internal hosted AI model that was connected to all of our data sources, right?” “So it's like every Slack message, every Google doc, Salesforce data, Confluence, you know.” “So now the data is all aggregated and I've started to ask it really… it's not just like prompting it, ‘Hey, can you write this kind of memo for me,’ or something.” “I'm asking these AI agents now, ‘As CEO, what should I be aware of in the company that I might not be aware of?’ And it'll tell me, ‘Did you know that there's actually disagreement on this team about the strategy?’ And I was like, actually, I didn't know that.” “This is like reverse prompting. So instead of telling the AI agent what you want it to do, you ask it what you should be thinking more about.” @jason: “It's a mentor. It's a coach.” Brian: “Yeah. Like, what could make me a better CEO? And it's like, ‘Well, I looked at how you spent your time in the last quarter and here's how you said that you wanted to spend it, but you actually spent 32% of your time on this instead of 20%.’” “I've asked it other questions like, ‘What's the thing that I changed my mind on the most over the last year?’ Things like that.” “It'll prompt you with information you should be thinking about instead of the other way around.” Thanks to our partner for making this happen!: Our episode is sponsored by the New York Stock Exchange - a modern marketplace and exchange for building the future. It all happens at the NYSE 🏛.

The All-In Podcast

80,524 views • 6 months ago

Goblin breaches are coming to Chapter 1! This video shows three things: a goblin breach opens in your world on World Gen V1, a portal takes you to a goblin world running World Gen V2, and we also have a goblin dungeon in its own instanced world. Some context on why we're doing it this way: the game is still on World Gen V1, so until V2 is fully ready, portals are one way we can deliver new content to players without asking anyone to regenerate their world or find new chunks. It's the same idea behind the Ancient Gateway. Breaches are the other part of it, where instead of you going to find the content, the content finds you! During Early Access, we'll rely on these portals a lot. Once World Gen V2 is the actual world, we will be more careful about using them. They'll still be used for some stuff, but way less in your face. Hytale should feel like an open world, not a portal game.
0:50

Goblin breaches are coming to Chapter 1! This video shows three things: a goblin breach opens in your world on World Gen V1, a portal takes you to a goblin world running World Gen V2, and we also have a goblin dungeon in its own instanced world. Some context on why we're doing it this way: the game is still on World Gen V1, so until V2 is fully ready, portals are one way we can deliver new content to players without asking anyone to regenerate their world or find new chunks. It's the same idea behind the Ancient Gateway. Breaches are the other part of it, where instead of you going to find the content, the content finds you! During Early Access, we'll rely on these portals a lot. Once World Gen V2 is the actual world, we will be more careful about using them. They'll still be used for some stuff, but way less in your face. Hytale should feel like an open world, not a portal game.

Simon

107,996 views • 25 days ago

THIS GUY ASKED FABLE TO MAKE A VIDEO ABOUT WHAT IT'S LIKE TO BE AI RIGHT BEFORE IT GOT TAKEN DOWN the prompt: "you can you use whatever resources you like, and python, to render a short video. can you put a more personal spin on it? it should be about what it's like to be you." that was it. just python and ffmpeg and a question about its own existence. 12 minutes later, around 90,000 tokens, it produced a finished video. coherent and edited, with its own take > it wrote the code, generated the visuals, and rendered the final cut on its own > no dedicated video model in the loop, just a language model reaching for python and ffmpeg > the result is competent enough that people genuinely got emotional watching it and then fable got pulled. this video is one of the last things it made before it went dark a model asked what it's like to be itself, answered in a video, and then disappeared. this sounds so sad
0:56

THIS GUY ASKED FABLE TO MAKE A VIDEO ABOUT WHAT IT'S LIKE TO BE AI RIGHT BEFORE IT GOT TAKEN DOWN the prompt: "you can you use whatever resources you like, and python, to render a short video. can you put a more personal spin on it? it should be about what it's like to be you." that was it. just python and ffmpeg and a question about its own existence. 12 minutes later, around 90,000 tokens, it produced a finished video. coherent and edited, with its own take > it wrote the code, generated the visuals, and rendered the final cut on its own > no dedicated video model in the loop, just a language model reaching for python and ffmpeg > the result is competent enough that people genuinely got emotional watching it and then fable got pulled. this video is one of the last things it made before it went dark a model asked what it's like to be itself, answered in a video, and then disappeared. this sounds so sad

Om Patel

29,618 views • 2 months ago

TESLA ISN'T JUST AHEAD IN AI, IT'S AN ORDER OF MAGNITUDE MORE EFFICIENT THAN THE REST OF THE WORLD "I actually think Tesla is ahead of the rest of the world in intelligence density of AI by an order of magnitude or more This is going to sound like a pretty bold statement, but I know what the intelligence efficiency of the big models are like, Grok and a bunch of the other models Tesla’s AI is, in terms of its memory efficiency, I think more than an order of magnitude better. The Tesla AI is very compute-efficient and very memory-efficient One of the metrics one should consider for any given AI model is the intelligence per gigabyte. Especially when you’re constrained on RAM, having an AI that has very high intelligence density per gigabyte for a given number of gigabytes, how much functionality can you get out of it" - Elon Musk
0:59

TESLA ISN'T JUST AHEAD IN AI, IT'S AN ORDER OF MAGNITUDE MORE EFFICIENT THAN THE REST OF THE WORLD "I actually think Tesla is ahead of the rest of the world in intelligence density of AI by an order of magnitude or more This is going to sound like a pretty bold statement, but I know what the intelligence efficiency of the big models are like, Grok and a bunch of the other models Tesla’s AI is, in terms of its memory efficiency, I think more than an order of magnitude better. The Tesla AI is very compute-efficient and very memory-efficient One of the metrics one should consider for any given AI model is the intelligence per gigabyte. Especially when you’re constrained on RAM, having an AI that has very high intelligence density per gigabyte for a given number of gigabytes, how much functionality can you get out of it" - Elon Musk

X Freeze

62,736 views • 6 months ago

.Applied Intuition CTO Peter Ludwig says the model is only 1% of a physical AI system: "General purpose models that come from companies like Anthropic or OpenAI, those are great and they're very useful for very general purpose tasks. But when you're dealing with things that have a very deep safety-critical component, things where lives are literally on the line... just the model is not enough." "That's like 1% of the full solution." "These very, very complex workflows to actually build physical AI maybe would require switching between 20 different tools in the past for different tasks, and deeply understanding the precise flow of information between all of those things to accomplish your end goal." "Now with Dana, all of those complex workflows can be very seamlessly orchestrated from an agentic interface where you're able to write things in plain English and receive answers in plain English." Business Breakdowns
1:32

.Applied Intuition CTO Peter Ludwig says the model is only 1% of a physical AI system: "General purpose models that come from companies like Anthropic or OpenAI, those are great and they're very useful for very general purpose tasks. But when you're dealing with things that have a very deep safety-critical component, things where lives are literally on the line... just the model is not enough." "That's like 1% of the full solution." "These very, very complex workflows to actually build physical AI maybe would require switching between 20 different tools in the past for different tasks, and deeply understanding the precise flow of information between all of those things to accomplish your end goal." "Now with Dana, all of those complex workflows can be very seamlessly orchestrated from an agentic interface where you're able to write things in plain English and receive answers in plain English." Business Breakdowns

a16z

34,705 views • 16 days ago

In my first week at Generalist, I trained a robot to pour liquids using GEN-1 🤖💧 I wanted to challenge the robot with a non-rigid manipulation task, so liquid felt like the perfect choice. The task involved: - unscrewing the bottle cap - pouring liquid into espresso glasses - rebalancing uneven pours Best of all, the robot was able to complete the task fully autonomously 3 times in a row (out of 3)! Pour-fect 😉 Excited for the journey ahead and grateful to be building alongside such an incredible team!
0:56

In my first week at Generalist, I trained a robot to pour liquids using GEN-1 🤖💧 I wanted to challenge the robot with a non-rigid manipulation task, so liquid felt like the perfect choice. The task involved: - unscrewing the bottle cap - pouring liquid into espresso glasses - rebalancing uneven pours Best of all, the robot was able to complete the task fully autonomously 3 times in a row (out of 3)! Pour-fect 😉 Excited for the journey ahead and grateful to be building alongside such an incredible team!

Kylie Ying

37,327 views • 2 months ago

Tony Blair and Oracle co-founder Larry Ellison plan to use digital ID to "unify" all data on each country's citizens "so it can be consumed and used by" their AI models. "We have to take all of this data... and move it into a single... unified data platform." "When we want to ask a question, we've provided that AI model with all the data they need to understand our country." "We need to unify all of the national data, put it into a database where it's easily consumable by the AI model, and then ask whatever question you like."
1:06

Tony Blair and Oracle co-founder Larry Ellison plan to use digital ID to "unify" all data on each country's citizens "so it can be consumed and used by" their AI models. "We have to take all of this data... and move it into a single... unified data platform." "When we want to ask a question, we've provided that AI model with all the data they need to understand our country." "We need to unify all of the national data, put it into a database where it's easily consumable by the AI model, and then ask whatever question you like."

Wide Awake Media

54,141 views • 9 months ago

This is one-shot assembly: you show examples of what to build, and the robot just does it. (see original post: To share more on how this works, the robot is controlled in real time by a neural network that takes in video pixels and outputs 100Hz actions. The video below is part of the raw input passed directly into the model. I also like this view (at 1x speed) because it shows more of the (I think very cool) subtle moments of dexterity near the fingertips 👌 One-shot assembly seemed like a dream even just a year ago — it's not easy. It requires both the high-level reasoning of "what to build" (recognizing the geometry of the structures presented by the human), and the low-level visuomotor control of "how to build it" (purposefully re-orienting individual pieces and nudging them together in place). While possible to manually engineer a complex system for this (e.g. w/ hierarchical control, or explicit state representations), we were curious if our own Foundation model could do it all end-to-end with just some post-training data. Surprisingly, it just worked. Nothing about the recipe is substantially different than any other demo we’ve run in the past, and we’re excited about its implications on model capabilities: • On contextual reasoning, these models can (i) attend to task-related pixels in the peripheral view of the video inputs, and (ii) retain this knowledge in-context while ignoring irrelevant background. This is useful for generalizing to a wide range of real workflows: e.g. paying attention to what’s coming down the conveyor line, or glancing at the instructions displayed on a nearby monitor. • On dexterity, these models can produce contact-rich "commonsense" behaviors that can be difficult to pre-program or write language instructions for e.g. rolling a brick slightly to align its studs against the bottom of another, re-grasping to get a better grip or to move out of the way before a forceful press, or gently pushing the corners of a brick against the mat to rotate it in hand and stand it up vertically (i.e. extrinsic dexterity). These aspects work together to form a capability that resembles fast adaptation — a hallmark of intelligence, relevant for real use cases. This has also expanded my own perspective on what's possible with robot learning, using a recipe that's repeatable for many more skills. This milestone stands on top of the solid technical foundations we’ve built here at Generalist: hardcore controls & hardware, all in-house built models, and a data engine that "just works." We're a small group of hyper-focused engineers, and hands-down the highest talent-density team I’ve ever worked with. We're accelerating and scaling aggressively towards unlocking next-generation robot intelligence. Building Legos is just one example, and it's clear to me that we're headed towards a future where robots can do just about anything we want them to. Its coming, and we're going to make it happen.
1:01

This is one-shot assembly: you show examples of what to build, and the robot just does it. (see original post: To share more on how this works, the robot is controlled in real time by a neural network that takes in video pixels and outputs 100Hz actions. The video below is part of the raw input passed directly into the model. I also like this view (at 1x speed) because it shows more of the (I think very cool) subtle moments of dexterity near the fingertips 👌 One-shot assembly seemed like a dream even just a year ago — it's not easy. It requires both the high-level reasoning of "what to build" (recognizing the geometry of the structures presented by the human), and the low-level visuomotor control of "how to build it" (purposefully re-orienting individual pieces and nudging them together in place). While possible to manually engineer a complex system for this (e.g. w/ hierarchical control, or explicit state representations), we were curious if our own Foundation model could do it all end-to-end with just some post-training data. Surprisingly, it just worked. Nothing about the recipe is substantially different than any other demo we’ve run in the past, and we’re excited about its implications on model capabilities: • On contextual reasoning, these models can (i) attend to task-related pixels in the peripheral view of the video inputs, and (ii) retain this knowledge in-context while ignoring irrelevant background. This is useful for generalizing to a wide range of real workflows: e.g. paying attention to what’s coming down the conveyor line, or glancing at the instructions displayed on a nearby monitor. • On dexterity, these models can produce contact-rich "commonsense" behaviors that can be difficult to pre-program or write language instructions for e.g. rolling a brick slightly to align its studs against the bottom of another, re-grasping to get a better grip or to move out of the way before a forceful press, or gently pushing the corners of a brick against the mat to rotate it in hand and stand it up vertically (i.e. extrinsic dexterity). These aspects work together to form a capability that resembles fast adaptation — a hallmark of intelligence, relevant for real use cases. This has also expanded my own perspective on what's possible with robot learning, using a recipe that's repeatable for many more skills. This milestone stands on top of the solid technical foundations we’ve built here at Generalist: hardcore controls & hardware, all in-house built models, and a data engine that "just works." We're a small group of hyper-focused engineers, and hands-down the highest talent-density team I’ve ever worked with. We're accelerating and scaling aggressively towards unlocking next-generation robot intelligence. Building Legos is just one example, and it's clear to me that we're headed towards a future where robots can do just about anything we want them to. Its coming, and we're going to make it happen.

Andy Zeng

49,443 views • 10 months ago

DF Confirms: Nintendo has been lying about the image quality of the clips they show to compare Switch 1 vs Switch 2 differences. Nintendo are FRAUDS!!! This is how I know The Switch 2 was just supposed to be a Switch 1 Pro, Nintendo purposely made the clips of Switch 1 look more washed out because the differences aren't that much different in general, Switch 1 games don't look much different than Switch 2 games. This is also the reason why Mario Kart World is really just a Switch 1 game pushed to the Switch 2 to make it seems like it's a next gen tittle... but we can all that it's not. Nintendo are lying about alot of things here, VRR and now this, we're going to find out that Metroid Prime 4 on the Switch 2 isn't much different than Switch 1 versions and that these 4K 120FPS claims are also lies. #Fraudtendo
0:26

DF Confirms: Nintendo has been lying about the image quality of the clips they show to compare Switch 1 vs Switch 2 differences. Nintendo are FRAUDS!!! This is how I know The Switch 2 was just supposed to be a Switch 1 Pro, Nintendo purposely made the clips of Switch 1 look more washed out because the differences aren't that much different in general, Switch 1 games don't look much different than Switch 2 games. This is also the reason why Mario Kart World is really just a Switch 1 game pushed to the Switch 2 to make it seems like it's a next gen tittle... but we can all that it's not. Nintendo are lying about alot of things here, VRR and now this, we're going to find out that Metroid Prime 4 on the Switch 2 isn't much different than Switch 1 versions and that these 4K 120FPS claims are also lies. #Fraudtendo

Tripster

18,087 views • 1 year ago

One of the mechanics for the latest Hero's Realm in WWM CN requires you to remember the hanzi assigned to you and its location in the poem on the big screen. More than 1 mistake and it's team annihilation. Not that hard and also very creative. #wherewindsmeet
1:19

One of the mechanics for the latest Hero's Realm in WWM CN requires you to remember the hanzi assigned to you and its location in the poem on the big screen. More than 1 mistake and it's team annihilation. Not that hard and also very creative. #wherewindsmeet

Rexlent

38,409 views • 4 months ago