Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

We’ve open-sourced the UI infrastructure for Blocks/Artifacts/Canvas-like AI chat experiences. BYO model. I see huge potential in bringing this UX paradigm to new verticals: productivity, finance, travel, health, legal, logistics… Happy hacking 🤘

Guillermo Rauch

735,068 subscribers

234,210 views • 1 year ago •via X (Twitter)

Science & Technology Health & Wellness Finance

Anya Rossi• Live Now

Private livecam show

9 Comments

Guillermo Rauch1 year ago

We have more announcements coming and a big vision for this project. Stay tuned!

Omar ☥1 year ago

The philosophy of Vercel is remarkable.

Guillermo Rauch1 year ago

🫡

Mike Chong (WildCat) 🏔️⭐️1 year ago

love it! even with next auth!

Seve1 year ago

Using @tscircuit you could also build electronics this way!

Ayman1 year ago

Do you mean this can be run completely locally using Llama or Gemini Models?

Guillermo Rauch1 year ago

Yes

Philip Vollet1 year ago

@aestheticedwar1

Parker Rex1 year ago

YESSSS

Related Videos

Latency has been the main UX concern we've heard for Flutter teams building GenUI/A2UI experiences. The new Jev model from TypeSafe AI solves this and unlocks a ton of new potential. All the UI generated in this video was picked by Jev - it felt like everything was running locally!

Latency has been the main UX concern we've heard for Flutter teams building GenUI/A2UI experiences. The new Jev model from TypeSafe AI solves this and unlocks a ton of new potential. All the UI generated in this video was picked by Jev - it felt like everything was running locally!

Abdallah Shaban

11,177 views • 23 days ago

The State of Claude Artifacts - August 2024 With Artifacts generally available across Claude dot ai and our mobile apps, I figured now is a good time to record a walkthrough of everything that you can do with Artifacts today. Artifacts has so much potential as a new UX paradigm - we're just scratching the surface of what it will eventually become. I have a feeling that when we look back at this video in the not-too-distant future, we'll be amazed at how quickly Artifacts evolved.

The State of Claude Artifacts - August 2024 With Artifacts generally available across Claude dot ai and our mobile apps, I figured now is a good time to record a walkthrough of everything that you can do with Artifacts today. Artifacts has so much potential as a new UX paradigm - we're just scratching the surface of what it will eventually become. I have a feeling that when we look back at this video in the not-too-distant future, we'll be amazed at how quickly Artifacts evolved.

Alex Albert

83,686 views • 2 years ago

🚨 ALIBABA JUST OPEN-SOURCED THE MOST POWERFUL MODEL IN THE WORLD - Qwen3.5 plus And it changes the AI race forever. This is not another “bigger model” release. This is a complete architectural reset. 397B parameters → only 17B active at inference, Qwen3.5 plus delivers performance on par with GPT-5.2 and Gemini-3-Pro. A new Hybrid Sparse MoE + Linear Attention architecture + Multi-Token Prediction = near-instant long-context responses and massive speed gains. Built as a true native multimodal model, it doesn’t just chat it builds. With a single prompt: “Use the React framework to create a visually impressive Mapbox demo featuring locations in Beijing and Shanghai” it generates a live interactive 3D map UI, structured components, real layouts, and production-ready frontend logic. This is real agentic, multimodal coding not a static code block. MMLU-Pro: 87.8 GPQA: 88.4 IFBench: 76.5 ⚡ up to 8× faster inference And it’s fully open-sourced under Apache-2.0. No locked APIs. No restricted tiers. 201 languages supported bringing frontier AI to developers, startups, and researchers worldwide. The race is no longer about the biggest model. It’s about the most usable intelligence. Open source just entered the top tier.

🚨 ALIBABA JUST OPEN-SOURCED THE MOST POWERFUL MODEL IN THE WORLD - Qwen3.5 plus And it changes the AI race forever. This is not another “bigger model” release. This is a complete architectural reset. 397B parameters → only 17B active at inference, Qwen3.5 plus delivers performance on par with GPT-5.2 and Gemini-3-Pro. A new Hybrid Sparse MoE + Linear Attention architecture + Multi-Token Prediction = near-instant long-context responses and massive speed gains. Built as a true native multimodal model, it doesn’t just chat it builds. With a single prompt: “Use the React framework to create a visually impressive Mapbox demo featuring locations in Beijing and Shanghai” it generates a live interactive 3D map UI, structured components, real layouts, and production-ready frontend logic. This is real agentic, multimodal coding not a static code block. MMLU-Pro: 87.8 GPQA: 88.4 IFBench: 76.5 ⚡ up to 8× faster inference And it’s fully open-sourced under Apache-2.0. No locked APIs. No restricted tiers. 201 languages supported bringing frontier AI to developers, startups, and researchers worldwide. The race is no longer about the biggest model. It’s about the most usable intelligence. Open source just entered the top tier.

RAVI KUMAR SAHU

380,400 views • 7 months ago

Here’s a cool project I worked on today: Recreating Meta’s new AR glasses on my Quest. 😎 This project reproduces the new wristband using microgestures to navigate through the UI. I also built my own hand tracking implementation for the pinch and twist mechanism, which controls the volume just like in the keynote. This was pretty fun to do, but also helped me think about how future experiences could be designed for this new device! Next steps: taking picture 📷 and contextual AI ✨!

Here’s a cool project I worked on today: Recreating Meta’s new AR glasses on my Quest. 😎 This project reproduces the new wristband using microgestures to navigate through the UI. I also built my own hand tracking implementation for the pinch and twist mechanism, which controls the volume just like in the keynote. This was pretty fun to do, but also helped me think about how future experiences could be designed for this new device! Next steps: taking picture 📷 and contextual AI ✨!

Quentin Valembois

29,316 views • 1 year ago

I believe AI has a major role to play in our future crypto infrastructure. Although there have been some scares recently, long term access to the RWA supercycle on Robinhood Chain will require creative new interfacing and execution technologies. I have been experimenting and laying the foundation for a product I call a DAIO (Decentralized Autonomous Intelligent Organization), which could, two to three years down the line, operate a fully decentralized product line like Clutch Markets developments. I call the base of this infrastructure, this potential AI CEO in training, "L.A.U.R.A": Layered Autonomous Unified Reasoning Agents. Although LAURA isn't ready to run a company yet, it is time to begin developing the core infrastructure required for this super agent, and that process will be an open source one, which we can all study together.

I believe AI has a major role to play in our future crypto infrastructure. Although there have been some scares recently, long term access to the RWA supercycle on Robinhood Chain will require creative new interfacing and execution technologies. I have been experimenting and laying the foundation for a product I call a DAIO (Decentralized Autonomous Intelligent Organization), which could, two to three years down the line, operate a fully decentralized product line like Clutch Markets developments. I call the base of this infrastructure, this potential AI CEO in training, "L.A.U.R.A": Layered Autonomous Unified Reasoning Agents. Although LAURA isn't ready to run a company yet, it is time to begin developing the core infrastructure required for this super agent, and that process will be an open source one, which we can all study together.

SimpleFarmer

84,027 views • 1 month ago

Proactive AI Agents Today’s AI products are reactive. You give the model a prompt, it responds with an answer. These are useful, but I’m excited about products that take this further and shift the paradigm from “ask → answer” to “observe → act." These agents will continuously monitor context in the background across all of your connected tools and data, predict what matters, and take action before being asked to do so – much like a human does. So instead of you prompting the model, the model will start prompting you. Examples here could include agents that remind you about tasks you forgot to complete, resolve customer issues before support tickets are filed, or debug and ship code fixes automatically. This shift represents a new paradigm where AI products behave more like humans and less like tools. We’re already starting to see this dynamic with products like OpenClaw, Poke, and more - and we’ve only scratched the surface of capabilities here. We’re accepting applications for the next cohort of a16z speedrun – If you’re building the next generation of AI products, apply online.

Proactive AI Agents Today’s AI products are reactive. You give the model a prompt, it responds with an answer. These are useful, but I’m excited about products that take this further and shift the paradigm from “ask → answer” to “observe → act." These agents will continuously monitor context in the background across all of your connected tools and data, predict what matters, and take action before being asked to do so – much like a human does. So instead of you prompting the model, the model will start prompting you. Examples here could include agents that remind you about tasks you forgot to complete, resolve customer issues before support tickets are filed, or debug and ship code fixes automatically. This shift represents a new paradigm where AI products behave more like humans and less like tools. We’re already starting to see this dynamic with products like OpenClaw, Poke, and more - and we’ve only scratched the surface of capabilities here. We’re accepting applications for the next cohort of a16z speedrun – If you’re building the next generation of AI products, apply online.

Kenan Saleh

136,785 views • 5 months ago

YoYo - New 'Git' for Vibe coders This open source extension allows me to do version control of AI generated codes in Cursor/Claude Code, So we can iterate different UI/UX flow much easier ----- It's interesting how basic things like Git can be rethink in the era of AI coding, Especially when we have 2~3 background agents working on the same code base simultaneously, We prob need new type of Git experience that allow agents to collab & resolve merge conflicts easily, We are just scratching the surface

YoYo - New 'Git' for Vibe coders This open source extension allows me to do version control of AI generated codes in Cursor/Claude Code, So we can iterate different UI/UX flow much easier ----- It's interesting how basic things like Git can be rethink in the era of AI coding, Especially when we have 2~3 background agents working on the same code base simultaneously, We prob need new type of Git experience that allow agents to collab & resolve merge conflicts easily, We are just scratching the surface

Jason Zhou

41,969 views • 1 year ago

🚀Launching CoAgents Public Beta 🪁: Everything you need to build Agent-Native applications, powered by LangGraph & CopilotKit. CoAgents enables in-app agents with: - Agenetic generative UI ✨ - Shared state (between agent application) - Streaming intermediate agent state - Human-in-the-Loop (Human approval & planning) 👨‍🦰 - Frontend Actions 💪 --- We’ve recently seen the emergence of Agent-Native applications that actually work: powered by vertical AI agents + agentic UX + deep in-app integration. (Apps like the Replit AI Agent, OpenAI Canvas, etc, but for any domain) With CoAgents + LangGraph, you can build this next generation of agent apps, for any vertical, with a clean and easy to use infrastructure (in hours). 💻Get started: 🗞️Blog: 🕹️Demo: 🗓️Webinar (Build an Agent-Native Apps with CoAgents & LangGraph): CC: Atai Barkai, , Harrison Chase

🚀Launching CoAgents Public Beta 🪁: Everything you need to build Agent-Native applications, powered by LangGraph & CopilotKit. CoAgents enables in-app agents with: - Agenetic generative UI ✨ - Shared state (between agent application) - Streaming intermediate agent state - Human-in-the-Loop (Human approval & planning) 👨‍🦰 - Frontend Actions 💪 --- We’ve recently seen the emergence of Agent-Native applications that actually work: powered by vertical AI agents + agentic UX + deep in-app integration. (Apps like the Replit AI Agent, OpenAI Canvas, etc, but for any domain) With CoAgents + LangGraph, you can build this next generation of agent apps, for any vertical, with a clean and easy to use infrastructure (in hours). 💻Get started: 🗞️Blog: 🕹️Demo: 🗓️Webinar (Build an Agent-Native Apps with CoAgents & LangGraph): CC: Atai Barkai, , Harrison Chase

CopilotKit🪁

51,685 views • 2 years ago

For the first time we are fundamentally changing how humans can collaborate with ChatGPT since it launched two years ago. We’re introducing canvas, a new interface for working with ChatGPT on writing and coding projects that go beyond simple chat. Product and model features: 1/ Ask for in-line feedback. With canvas, ChatGPT can better understand the context of what you’re trying to accomplish. You can highlight specific sections to indicate exactly what you want ChatGPT to focus on. Like a copy editor or code reviewer, it can give in-line feedback and suggestions with the entire project in mind. 2/ Directly edit the model's output and select a specific area for targeted editing. You control your creative work on canvas. You can directly edit text or code. 3/ Menu of shortcuts. There’s a menu of shortcuts for you to ask ChatGPT to adjust writing length, debug your code, and quickly perform other useful actions. You can also restore previous versions of your work by using the back button in canvas. 4/ Use search with canvas for research writing! As we are moving towards the new paradigm of reasoning we are fundamentally evolving the chat interface into a more collaborative human-AI interaction. Today you can say “browse / use browsing to find XYZ on the internet and write a report in canvas”

For the first time we are fundamentally changing how humans can collaborate with ChatGPT since it launched two years ago. We’re introducing canvas, a new interface for working with ChatGPT on writing and coding projects that go beyond simple chat. Product and model features: 1/ Ask for in-line feedback. With canvas, ChatGPT can better understand the context of what you’re trying to accomplish. You can highlight specific sections to indicate exactly what you want ChatGPT to focus on. Like a copy editor or code reviewer, it can give in-line feedback and suggestions with the entire project in mind. 2/ Directly edit the model's output and select a specific area for targeted editing. You control your creative work on canvas. You can directly edit text or code. 3/ Menu of shortcuts. There’s a menu of shortcuts for you to ask ChatGPT to adjust writing length, debug your code, and quickly perform other useful actions. You can also restore previous versions of your work by using the back button in canvas. 4/ Use search with canvas for research writing! As we are moving towards the new paradigm of reasoning we are fundamentally evolving the chat interface into a more collaborative human-AI interaction. Today you can say “browse / use browsing to find XYZ on the internet and write a report in canvas”

Karina

846,131 views • 2 years ago

[New] from a16z speedrun: The Outsider founders are winning the AI B2B race we used to say founder x market fit was everything but in this AI wave, Outsider founders who are AI native are outmaneuvering their industry veteran competitors just look at Tennr (healthcare), (finance), (logistics), Eve (legal) & Decagon (customer support)..... these are all Outsider founders w/ no prior experience in the verticals they now sell into so why is this? #1 - right now, AI technical excellency trumps industry expertise --> These founders combine AI fluency w/ exceptional problem solving skills to quickly map the inner workings of a vertical from an Outsiders perspective and how AI can automate that work --> many will bring on a "board of Insider advisors" early to help validate their approaches and leverage their rolodex #2 - new verticals w/out a scaled SaaS player are now in play --> Agentic AI products don’t just help workers become more efficient, they can take action and complete work autonomously --> Therefore companies can spend way more for these products as they don't just tap into IT budgets, they actually displace labor spend --> That means that offline verticals w/ historically small IT budgets and no vertical SaaS winners are now ripe for a venture scaled vertical AI disruptor = from food distribution to car dealerships to home services to agriculture, it will be Outsiders who will come in and automate the backoffice functions for these verticals #3 - the barriers for Outsiders to sell into the enterprise have dropped dramatically --> most companies have a tops-down mandate and dedicated budget to explore AI initiatives. They’re actively pulling new solutions into their organizations and open to experimenting with pilot partners --> for some products, where PLG and bottoms-up adoption makes sense, Outsiders can let their product and marketing strategies be their ticket into their buyer so, today, founder - market fit should be based on required skillsets to win in a space, not simply on tenure & domain expertise in a vertical here at speedrun we’ve invested in teams of exceptional Outsiders such as Artifact AI (accounting), Anchr (food distribution), Bead (SOX compliance) + many more if you’re a team of Outsiders primed to disrupt a boring business, head to sr [dot] a16z [dot] com to apply for speedrun

[New] from a16z speedrun: The Outsider founders are winning the AI B2B race we used to say founder x market fit was everything but in this AI wave, Outsider founders who are AI native are outmaneuvering their industry veteran competitors just look at Tennr (healthcare), (finance), (logistics), Eve (legal) & Decagon (customer support)..... these are all Outsider founders w/ no prior experience in the verticals they now sell into so why is this? #1 - right now, AI technical excellency trumps industry expertise --> These founders combine AI fluency w/ exceptional problem solving skills to quickly map the inner workings of a vertical from an Outsiders perspective and how AI can automate that work --> many will bring on a "board of Insider advisors" early to help validate their approaches and leverage their rolodex #2 - new verticals w/out a scaled SaaS player are now in play --> Agentic AI products don’t just help workers become more efficient, they can take action and complete work autonomously --> Therefore companies can spend way more for these products as they don't just tap into IT budgets, they actually displace labor spend --> That means that offline verticals w/ historically small IT budgets and no vertical SaaS winners are now ripe for a venture scaled vertical AI disruptor = from food distribution to car dealerships to home services to agriculture, it will be Outsiders who will come in and automate the backoffice functions for these verticals #3 - the barriers for Outsiders to sell into the enterprise have dropped dramatically --> most companies have a tops-down mandate and dedicated budget to explore AI initiatives. They’re actively pulling new solutions into their organizations and open to experimenting with pilot partners --> for some products, where PLG and bottoms-up adoption makes sense, Outsiders can let their product and marketing strategies be their ticket into their buyer so, today, founder - market fit should be based on required skillsets to win in a space, not simply on tenure & domain expertise in a vertical here at speedrun we’ve invested in teams of exceptional Outsiders such as Artifact AI (accounting), Anchr (food distribution), Bead (SOX compliance) + many more if you’re a team of Outsiders primed to disrupt a boring business, head to sr [dot] a16z [dot] com to apply for speedrun

Troy Kirwin

81,913 views • 1 year ago

The DEPRESSING reality of AI adoption curves Advanced AI just broke into it's third major paradigm since launching. Paradigm One was the simple autocomplete engine. GPT-2 and original GPT-3 were glorified autocomplete tools, just "next token predictors." Paradigm 1.5 was when we added "instruct-aligned" GPT-3, which set the stage for Paradigm Two. Paradigm Two was the chatbot era, with ChatGPT being the front-runner. Paradigm 2.5 was when we started adding reasoning, tool use, and RAG, which set the stage for agentic abilities (Paradigm 3). OpenClaw just blew the lid off Paradigm 3, and we're just at the beginning of this new ramp-up in capabilities. The thing is... each of these paradigm shifts creates fundamentally different UX and technical affordances. While most Fortune 500 companies are still struggling to figure out the cyber security, legal, and financial risks of chatbots, the industry is going whole hog into autonomous agents. And many people will try to graft their understanding of chatbots onto agents. But that's like trying to compare the electric lightbulb to the electric motor.... ...yes they both ran on electricity, but their uses, affordances, limitations, and risks were fundamentally different. Yes, autocomplete, chatbots, and agents all run on "next token prediction" but that's like saying "it all runs on electrons." My goal today is to help give you a better intuition as to why adoption is so slow and to give you a new reference frame to understand that agents are a phase change from chatbots. But also... the state and big companies are going to move depressingly slow on all of this...

The DEPRESSING reality of AI adoption curves Advanced AI just broke into it's third major paradigm since launching. Paradigm One was the simple autocomplete engine. GPT-2 and original GPT-3 were glorified autocomplete tools, just "next token predictors." Paradigm 1.5 was when we added "instruct-aligned" GPT-3, which set the stage for Paradigm Two. Paradigm Two was the chatbot era, with ChatGPT being the front-runner. Paradigm 2.5 was when we started adding reasoning, tool use, and RAG, which set the stage for agentic abilities (Paradigm 3). OpenClaw just blew the lid off Paradigm 3, and we're just at the beginning of this new ramp-up in capabilities. The thing is... each of these paradigm shifts creates fundamentally different UX and technical affordances. While most Fortune 500 companies are still struggling to figure out the cyber security, legal, and financial risks of chatbots, the industry is going whole hog into autonomous agents. And many people will try to graft their understanding of chatbots onto agents. But that's like trying to compare the electric lightbulb to the electric motor.... ...yes they both ran on electricity, but their uses, affordances, limitations, and risks were fundamentally different. Yes, autocomplete, chatbots, and agents all run on "next token prediction" but that's like saying "it all runs on electrons." My goal today is to help give you a better intuition as to why adoption is so slow and to give you a new reference frame to understand that agents are a phase change from chatbots. But also... the state and big companies are going to move depressingly slow on all of this...

David Shapiro (L/0)

17,266 views • 8 months ago

AI video creation is starting to feel less like prompting and more like directing. Dreamina AI has updated Web experience and canvas, bringing together many of proven Workflows and Skills based on high-performing content across film, brand ads, viral social media content scenes and more. I tested the updated Dreamina AI Skills & AI Agent, and the biggest difference for me is how much more control you get over the entire creative process. I wanted to see what I could do with a very ordinary campus moment ,a normal day with friends and turn it into something that looks like a movie. For this one, I wanted to bring in the coastal summer-adventure feeling of Outer Banks S5, combined with the kind of continuous, follow-camera movement you’d see in a cinematic coming-of-age film.

Antonio Costa | IA

19,887 views • 20 days ago

Jason Calacanis: The outcome of NYT v. OpenAI could change everything for content creators On E215, @jason discussed the downstream impacts from a huge recent legal decision in the AI space: "Thomson Reuters just won the first major US AI copyright case." "This has huge implications for AI companies here in the United States." "I think that there is a non-zero chance, it might be 5 or 10%, that OpenAI is going to lose the The New York Times lawsuit, and they're going to lose it hard." "I think The New York Times realizes this is their payday." " The settlement might be that these language models, especially the closed ones, are going to have to pay some percentage of their revenue to the content holders." " And this could make the content industry have a massive resurgence." " I think the New York Times will make more money from licenses from LLMs than they will make from advertising or subscriptions, eventually." " This will renew the model."

Jason Calacanis: The outcome of NYT v. OpenAI could change everything for content creators On E215, @jason discussed the downstream impacts from a huge recent legal decision in the AI space: "Thomson Reuters just won the first major US AI copyright case." "This has huge implications for AI companies here in the United States." "I think that there is a non-zero chance, it might be 5 or 10%, that OpenAI is going to lose the The New York Times lawsuit, and they're going to lose it hard." "I think The New York Times realizes this is their payday." " The settlement might be that these language models, especially the closed ones, are going to have to pay some percentage of their revenue to the content holders." " And this could make the content industry have a massive resurgence." " I think the New York Times will make more money from licenses from LLMs than they will make from advertising or subscriptions, eventually." " This will renew the model."

The All-In Podcast

69,778 views • 1 year ago

🚁 We’ve taught drones to see what engineers once had to climb for. This fascinates me — combining drones and 3D modeling is transforming how we inspect and maintain the world’s infrastructure. Instead of slow, risky manual checks, drones now scan entire bridges in minutes — spotting even the tiniest cracks or signs of wear invisible to the naked eye. The result: faster insights, safer inspections, and data-driven maintenance that helps prevent failures before they happen. To me, this is what progress looks like — when technology doesn’t replace human expertise, but extends it. Could AI-powered drones become the new guardians of our cities’ infrastructure? #AI #Innovation #Technology #Drones #Engineering #Infrastructure #Automation #SmartCities #Data #FutureOfWork Credits: Marc Theermann

🚁 We’ve taught drones to see what engineers once had to climb for. This fascinates me — combining drones and 3D modeling is transforming how we inspect and maintain the world’s infrastructure. Instead of slow, risky manual checks, drones now scan entire bridges in minutes — spotting even the tiniest cracks or signs of wear invisible to the naked eye. The result: faster insights, safer inspections, and data-driven maintenance that helps prevent failures before they happen. To me, this is what progress looks like — when technology doesn’t replace human expertise, but extends it. Could AI-powered drones become the new guardians of our cities’ infrastructure? #AI #Innovation #Technology #Drones #Engineering #Infrastructure #Automation #SmartCities #Data #FutureOfWork Credits: Marc Theermann

Pascal Bornet

239,088 views • 9 months ago

I made an AI data science team with Python. I'm excited to share my new project, the AI Data Science Team, a Python package for creating AI co-pilots and agents to assist in common data science tasks. I walk you through the GitHub page, showcasing the progress and potential of this project. I'll explain how these agents can streamline tasks like code generation and modeling pipeline creation, making data science projects more efficient. Introduction to AI Data Science Team Project Integrating Human-in-the-Loop Example: Accessing Agents for Feature Engineering Setting up Feature Engineering Agent Code On GitHub: Python AI-Tips Newsletter (stay updated as I build this project in the open):

I made an AI data science team with Python. I'm excited to share my new project, the AI Data Science Team, a Python package for creating AI co-pilots and agents to assist in common data science tasks. I walk you through the GitHub page, showcasing the progress and potential of this project. I'll explain how these agents can streamline tasks like code generation and modeling pipeline creation, making data science projects more efficient. Introduction to AI Data Science Team Project Integrating Human-in-the-Loop Example: Accessing Agents for Feature Engineering Setting up Feature Engineering Agent Code On GitHub: Python AI-Tips Newsletter (stay updated as I build this project in the open):

Matt Dancho (Business Science)

11,799 views • 1 year ago

Introducing open source, real-time demo for Comfy Deploy, streaming in any params (text, number, images). Workflow by Julien Blanchon 🇺🇦 🫶 Running serverless gpu on Modal Drawing UI tldraw Inspired by Krea, fal 's real-time demo! Huge respects! Demo on GitHub. For context: tbh this is just a technical demo, truly meant to showcase how powerfully Comfy UI is when you get to expose the params. I'm also very grateful to even get the chance to meet with the team behind krea and fal in SF, these are very creative people and I have huge respect. Everything you see here is running behind a workflow done by julien when I asked for help in the discord, for which I am really really grateful. I'm looking toward a future where real-time generative AI becomes more accessible, and by contributing to open source, a lot more people can build creative AI apps and let their imaginations go wild. benny

Introducing open source, real-time demo for Comfy Deploy, streaming in any params (text, number, images). Workflow by Julien Blanchon 🇺🇦 🫶 Running serverless gpu on Modal Drawing UI tldraw Inspired by Krea, fal 's real-time demo! Huge respects! Demo on GitHub. For context: tbh this is just a technical demo, truly meant to showcase how powerfully Comfy UI is when you get to expose the params. I'm also very grateful to even get the chance to meet with the team behind krea and fal in SF, these are very creative people and I have huge respect. Everything you see here is running behind a workflow done by julien when I asked for help in the discord, for which I am really really grateful. I'm looking toward a future where real-time generative AI becomes more accessible, and by contributing to open source, a lot more people can build creative AI apps and let their imaginations go wild. benny

BennyKok

165,591 views • 2 years ago

Ai Day last week was a blast! I had a great time presenting alongside many colleagues from InstaDeep and BioNTech SE, some of our ongoing Bio+AI initiatives. We made several announcements, introducing Kyber, our new supercomputing cluster (more on this soon), as well as BFN, our new paradigm for generative AI, with competitive results on protein and antibody generation. During Ai Day we also delivered two live demos of Laila, our new AI agent, including one from BioNTech's TechLab in Mainz, Germany! To our knowledge, this was one of the first demos of its kind featuring a GPT-4 level AI agent acting in the Lab, interacting with scientists and technicians and helping them enhance their productivity! Ai Day was a significant moment for all of us, and it was exciting to see both the event and Laila featured by several publications including Financial Times ( Curious to see it in action? Check out the recording of the Laila in the Lab demo below, worth watching and don't miss the end! 😉

Ai Day last week was a blast! I had a great time presenting alongside many colleagues from InstaDeep and BioNTech SE, some of our ongoing Bio+AI initiatives. We made several announcements, introducing Kyber, our new supercomputing cluster (more on this soon), as well as BFN, our new paradigm for generative AI, with competitive results on protein and antibody generation. During Ai Day we also delivered two live demos of Laila, our new AI agent, including one from BioNTech's TechLab in Mainz, Germany! To our knowledge, this was one of the first demos of its kind featuring a GPT-4 level AI agent acting in the Lab, interacting with scientists and technicians and helping them enhance their productivity! Ai Day was a significant moment for all of us, and it was exciting to see both the event and Laila featured by several publications including Financial Times ( Curious to see it in action? Check out the recording of the Laila in the Lab demo below, worth watching and don't miss the end! 😉

Karim Beguir

16,565 views • 2 years ago

Day 2 of recreating Meta’s new AR glasses on my Quest. After adding microgestures to navigate the UI. Today, I built a simple camera that can zoom using Pinch and Twist. It felt really cool to scroll through all the pictures I took in the carousel. But the best addition is definitely the contextual AI that lets me send whatever I’m looking at to an AI and instantly get more information. This project is getting more and more challenging but I'm having a lot of fun and it's cool to see the potential of these new AR glasses. And yes, the complete Unity project will be available very soon on my Patreon if you’d like to see how I did it 👉

Day 2 of recreating Meta’s new AR glasses on my Quest. After adding microgestures to navigate the UI. Today, I built a simple camera that can zoom using Pinch and Twist. It felt really cool to scroll through all the pictures I took in the carousel. But the best addition is definitely the contextual AI that lets me send whatever I’m looking at to an AI and instantly get more information. This project is getting more and more challenging but I'm having a lot of fun and it's cool to see the potential of these new AR glasses. And yes, the complete Unity project will be available very soon on my Patreon if you’d like to see how I did it 👉

Quentin Valembois

36,677 views • 1 year ago

What happens when an AI world reacts to you in real time? That’s what makes PixVerse R2 interesting. You can move through generated worlds with WASD. You can explore freely. You can type what should happen next. And the world responds while you’re still inside it. It starts to feel less like watching AI video and more like interacting with a world that keeps evolving around your input. Still early, but I can already see the potential for game ideation, rapid prototyping, interactive stories, and new kinds of playable experiences. Try it: PixVerse #PixVerseWorldModel

What happens when an AI world reacts to you in real time? That’s what makes PixVerse R2 interesting. You can move through generated worlds with WASD. You can explore freely. You can type what should happen next. And the world responds while you’re still inside it. It starts to feel less like watching AI video and more like interacting with a world that keeps evolving around your input. Still early, but I can already see the potential for game ideation, rapid prototyping, interactive stories, and new kinds of playable experiences. Try it: PixVerse #PixVerseWorldModel

Alamin

90,708 views • 18 days ago