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Damn. DeepMind's generalist AI agent SIMA 2 evolved from basic instruction-following to actual reasoning companion. Uses vision and keyboard/mouse like a human player, works across dozens of games without touching game code. The robotics angle is obvious - if you can generalize across virtual 3D worlds, physical embodiment is...

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Bilawal Sidhu

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48,576 просмотров • 8 месяцев назад •via X (Twitter)

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Hermes Agent now has Computer Use🤯 You can now control your actual computer with ANY model through Hermes Agent × Cua not just frontier models locked behind special modes. Even crazier: • Runs entirely in the background • Doesn’t hijack your PC • You keep full control of your keyboard, mouse, and screen while the agent works This is starting to feel less like an AI chatbot and more like a real agentic operating system.
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Hermes Agent now has Computer Use🤯 You can now control your actual computer with ANY model through Hermes Agent × Cua not just frontier models locked behind special modes. Even crazier: • Runs entirely in the background • Doesn’t hijack your PC • You keep full control of your keyboard, mouse, and screen while the agent works This is starting to feel less like an AI chatbot and more like a real agentic operating system.

WorldofAI

61,974 просмотров • 2 месяцев назад

I’m giving an agent control over Reachy Mini from Hugging Face and letting it understand and share spatial data via SPECS for Devs AR is the human interface for robotics and physical AI imo. It feels like absolute magic to interact with this, both in voice/agent and “puppeteering” mode. I’ll probably work on AR for either an arm (manipulation tasks) or some sort of drone (locomotion in 3D space) next… Project is fully open source btw: Thank you SensAI Hackademy for sending me the robot!
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I’m giving an agent control over Reachy Mini from Hugging Face and letting it understand and share spatial data via SPECS for Devs AR is the human interface for robotics and physical AI imo. It feels like absolute magic to interact with this, both in voice/agent and “puppeteering” mode. I’ll probably work on AR for either an arm (manipulation tasks) or some sort of drone (locomotion in 3D space) next… Project is fully open source btw: Thank you SensAI Hackademy for sending me the robot!

Johannes Tscharn

35,341 просмотров • 4 месяцев назад

🚨🚨Can agents earn money, run a business, or even self-organize a society in the physical social world? 🤖🤖 Can agents learn continually to survive and thrive in embodied environments, like how human babies grow? 👶 Super excited to introduce SimWorld, an open-ended simulator of LLM agents in infinite, realistic embodied worlds. SimWorld features 3 key designs: 1⃣Open-ended realistic world simulation - built on Unreal Engine 5, with accurate physical social dynamics - 100+ built-in environments (city, island, wilderness ...) - language-controllable procedural generation - text-to-3D asset generation 2⃣Native interface for LLM/VLM agents - Gym-like agent-environment interaction APIs - plug in any LLMs/VLMs (GPTs, Gemini, Qwen ...) - rich multi-modal perception - open-vocabulary natural-language action outputs 3⃣Diverse physical and social reasoning scenarios - long-horizon embodied reasoning - multi-agent collaboration / competition - easily customizable for any reasoning tasks SimWorld is fully open-sourced, with a hope to become a foundational infrastructure for real-world agent research across disciplines: robotics, economy, public health, education, etc. Project website + more details in the thread👇 ...1/
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🚨🚨Can agents earn money, run a business, or even self-organize a society in the physical social world? 🤖🤖 Can agents learn continually to survive and thrive in embodied environments, like how human babies grow? 👶 Super excited to introduce SimWorld, an open-ended simulator of LLM agents in infinite, realistic embodied worlds. SimWorld features 3 key designs: 1⃣Open-ended realistic world simulation - built on Unreal Engine 5, with accurate physical social dynamics - 100+ built-in environments (city, island, wilderness ...) - language-controllable procedural generation - text-to-3D asset generation 2⃣Native interface for LLM/VLM agents - Gym-like agent-environment interaction APIs - plug in any LLMs/VLMs (GPTs, Gemini, Qwen ...) - rich multi-modal perception - open-vocabulary natural-language action outputs 3⃣Diverse physical and social reasoning scenarios - long-horizon embodied reasoning - multi-agent collaboration / competition - easily customizable for any reasoning tasks SimWorld is fully open-sourced, with a hope to become a foundational infrastructure for real-world agent research across disciplines: robotics, economy, public health, education, etc. Project website + more details in the thread👇 ...1/

Lianhui Qin

64,864 просмотров • 7 месяцев назад

2. NVIDIA is building the GPT of humanoid robots. They just launched Isaac GR00T N1.5 - a foundation model for general purpose robotics. Here’s how it works: → A human demos the task once → Cosmos (their physics AI model) generates 1,000s of variations → Omniverse simulates the motions in high fidelity → The robot trains entirely in simulation → Then fine-tunes itself in the real world Robots can now learn general skills across tasks, tools, even body types with just one human demo. AI isn’t just thinking in text anymore. It’s perceiving. Reasoning. Moving. Physical AI is here and it’s training itself.
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2. NVIDIA is building the GPT of humanoid robots. They just launched Isaac GR00T N1.5 - a foundation model for general purpose robotics. Here’s how it works: → A human demos the task once → Cosmos (their physics AI model) generates 1,000s of variations → Omniverse simulates the motions in high fidelity → The robot trains entirely in simulation → Then fine-tunes itself in the real world Robots can now learn general skills across tasks, tools, even body types with just one human demo. AI isn’t just thinking in text anymore. It’s perceiving. Reasoning. Moving. Physical AI is here and it’s training itself.

Shruti

70,359 просмотров • 1 год назад

Zapier CEO Wade Foster says individuals have become more productive with AI tools, but that it's not clear if companies as a whole are also becoming more productive: "Across Zapier, 100% of our employees are using AI daily. People are individually more productive. But if you were to ask me, 'Is the company more productive, institutionally are we more productive?' It's a lot harder to say yes to that." "This feels like the next thing that everybody's working on: How do we actually accelerate the institution? This gets harder because you actually have to rethink how the company works." "To give you an example, we've gone from a world where code was expensive to now code is cheap. In the world where code was expensive, you had all these processes upfront to talk to customers, to align on roadmaps, to figure out what the right thing to build was, because if you chose the wrong thing, that was a really expensive mistake." "But when code is cheap, you can get rid of all that stuff. And that takes redesigning jobs or rethinking the human where the human actually exists in the first place."
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Zapier CEO Wade Foster says individuals have become more productive with AI tools, but that it's not clear if companies as a whole are also becoming more productive: "Across Zapier, 100% of our employees are using AI daily. People are individually more productive. But if you were to ask me, 'Is the company more productive, institutionally are we more productive?' It's a lot harder to say yes to that." "This feels like the next thing that everybody's working on: How do we actually accelerate the institution? This gets harder because you actually have to rethink how the company works." "To give you an example, we've gone from a world where code was expensive to now code is cheap. In the world where code was expensive, you had all these processes upfront to talk to customers, to align on roadmaps, to figure out what the right thing to build was, because if you chose the wrong thing, that was a really expensive mistake." "But when code is cheap, you can get rid of all that stuff. And that takes redesigning jobs or rethinking the human where the human actually exists in the first place."

TBPN

15,382 просмотров • 3 месяцев назад

This is the latest Google research on XR Interaction: World Mouse. Instead of using hand rays or gestures to point at things in the physical space, you move a cursor with a mouse and the system figures out where that cursor should sit in the 3D world. It works in two main ways: - On an object, the cursor follows the surface, so you can move precisely across walls, screens, furniture, or virtual objects. - Between objects, the system creates a smooth bridge through space, so the cursor can travel from one surface to another. That means you can go from a 2D panel to a 3D object, place content on a real wall, manipulate virtual objects, or interact with physical devices through digital proxies. This is the type of news, tools and product updates I share weekly in my newsletter. If you are into 3D XR and AI make sure to check it out (link on top of my profile). Based on “World Mouse: Exploring Interactions with a Cross-Reality Cursor” by Esen K. Tütüncü, Mar Gonzalez-Franco, Khushman Patel, and Eric J. Gonzalez
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This is the latest Google research on XR Interaction: World Mouse. Instead of using hand rays or gestures to point at things in the physical space, you move a cursor with a mouse and the system figures out where that cursor should sit in the 3D world. It works in two main ways: - On an object, the cursor follows the surface, so you can move precisely across walls, screens, furniture, or virtual objects. - Between objects, the system creates a smooth bridge through space, so the cursor can travel from one surface to another. That means you can go from a 2D panel to a 3D object, place content on a real wall, manipulate virtual objects, or interact with physical devices through digital proxies. This is the type of news, tools and product updates I share weekly in my newsletter. If you are into 3D XR and AI make sure to check it out (link on top of my profile). Based on “World Mouse: Exploring Interactions with a Cross-Reality Cursor” by Esen K. Tütüncü, Mar Gonzalez-Franco, Khushman Patel, and Eric J. Gonzalez

Gabriele Romagnoli

28,945 просмотров • 3 месяцев назад

Wrapping up the year and coinciding with #NeurIPS2024, today at Meta FAIR we’re releasing a collection of nine new open source AI research artifacts across our work in developing agents, robustness & safety and new architectures. More in the video from Joelle Pineau. All of this work is part of FAIR’s continued work towards the goal of achieving advanced machine intelligence A few highlights from what we’re releasing today: • Meta Motivo: A first-of-its-kind behavioral foundation model that controls the movements of a virtual embodied humanoid agent to perform complex tasks. • Meta Video Seal: a state-of-the art comprehensive framework for neural video watermarking. • Meta Explore Theory-of-Mind: A program-guided adversarial data generation for theory of mind reasoning. • Meta Large Concept Models: A fundamentally different training paradigm for language modeling that decouples reasoning from language representation. And much more! We’re excited to share this work with the research community and look forward to seeing how it inspires new innovation across the field. Details and access to everything released by FAIR today ➡️
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Wrapping up the year and coinciding with #NeurIPS2024, today at Meta FAIR we’re releasing a collection of nine new open source AI research artifacts across our work in developing agents, robustness & safety and new architectures. More in the video from Joelle Pineau. All of this work is part of FAIR’s continued work towards the goal of achieving advanced machine intelligence A few highlights from what we’re releasing today: • Meta Motivo: A first-of-its-kind behavioral foundation model that controls the movements of a virtual embodied humanoid agent to perform complex tasks. • Meta Video Seal: a state-of-the art comprehensive framework for neural video watermarking. • Meta Explore Theory-of-Mind: A program-guided adversarial data generation for theory of mind reasoning. • Meta Large Concept Models: A fundamentally different training paradigm for language modeling that decouples reasoning from language representation. And much more! We’re excited to share this work with the research community and look forward to seeing how it inspires new innovation across the field. Details and access to everything released by FAIR today ➡️

AI at Meta

156,123 просмотров • 1 год назад

🔥 Our latest LangChain Academy course – Deep Research with LangGraph – is now live! 🔥 Deep research agents are taking off – from major AI labs to companies building their own. Research is inherently open-ended. You can't always predict whether a question needs broad exploration or deep analysis. Agents excel here because they adapt on the fly, using each finding to decide where to dig next. Building these systems ourselves and with customers, we've learned that structure matters. The best research agents scope problems with users first. Then, they coordinate multiple specialists instead of overwhelming one generalist. LangGraph is built for these types of long-running, multi-agent workflows. Its persistence layer tracks progress across agents. LangSmith gives you the observability and evaluation tools you need to track and improve performance. In this course, you'll build and evaluate: - A user scoping agent to define research parameters - A multi-agent research team with supervisor - And add tool integration via MCP Enroll for free ➡️
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🔥 Our latest LangChain Academy course – Deep Research with LangGraph – is now live! 🔥 Deep research agents are taking off – from major AI labs to companies building their own. Research is inherently open-ended. You can't always predict whether a question needs broad exploration or deep analysis. Agents excel here because they adapt on the fly, using each finding to decide where to dig next. Building these systems ourselves and with customers, we've learned that structure matters. The best research agents scope problems with users first. Then, they coordinate multiple specialists instead of overwhelming one generalist. LangGraph is built for these types of long-running, multi-agent workflows. Its persistence layer tracks progress across agents. LangSmith gives you the observability and evaluation tools you need to track and improve performance. In this course, you'll build and evaluate: - A user scoping agent to define research parameters - A multi-agent research team with supervisor - And add tool integration via MCP Enroll for free ➡️

LangChain

61,205 просмотров • 11 месяцев назад

these guys are making Claude, Hermes, and DeepSeek argue with each other instead of just agreeing with you. this is the practical future of agent teams. with Bloome you just pick a task template, and it spins up a full agent team in a group chat to collaborate and get the job done. research, review, writing, market analysis, or anything you want. they brainstorm, review, and plan together. meanwhile, you or other human teammates can steer them or jump in, and stay in the same conversation so nothing gets lost across tools. since this is actual work, Bloome works across web, mobile, and desktop. try the desktop app first if you're using it seriously. same flexibility on the agent side too, you can connect any agents you want: ChatGPT, OpenClaw, Gemini… check them out →
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these guys are making Claude, Hermes, and DeepSeek argue with each other instead of just agreeing with you. this is the practical future of agent teams. with Bloome you just pick a task template, and it spins up a full agent team in a group chat to collaborate and get the job done. research, review, writing, market analysis, or anything you want. they brainstorm, review, and plan together. meanwhile, you or other human teammates can steer them or jump in, and stay in the same conversation so nothing gets lost across tools. since this is actual work, Bloome works across web, mobile, and desktop. try the desktop app first if you're using it seriously. same flexibility on the agent side too, you can connect any agents you want: ChatGPT, OpenClaw, Gemini… check them out →

ℏεsam

61,952 просмотров • 6 дней назад

SOMEONE BUILT AN OPEN SOURCE GAME ENGINE MADE SPECIFICALLY FOR BUILDING 2.5D GAMES WITH AI instead of using a complex general purpose engine, this one is built from the ground up for the ai workflow it keeps everything the ai needs to control in one small, simple place, so the agent can actually understand the whole engine and build with it instead of getting lost in a giant codebase > generate all your assets with ai, the maps, characters, props, audio and ui, then the agent drops them into a real playable game > a built in server sdk handles the boring stuff, player accounts, cloud saves, even multiplayer, in a line or two > it ships with claude and codex agent setups baked in, so it works with whatever youre already using > free and open source most people vibe code games inside engines that were never meant for it, so he built one where the ai workflow is the whole point the whole idea is dead simple, keep it small enough that an agent can understand all of it at once, then let it go wild this is what tooling built for the agent era actually looks like
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SOMEONE BUILT AN OPEN SOURCE GAME ENGINE MADE SPECIFICALLY FOR BUILDING 2.5D GAMES WITH AI instead of using a complex general purpose engine, this one is built from the ground up for the ai workflow it keeps everything the ai needs to control in one small, simple place, so the agent can actually understand the whole engine and build with it instead of getting lost in a giant codebase > generate all your assets with ai, the maps, characters, props, audio and ui, then the agent drops them into a real playable game > a built in server sdk handles the boring stuff, player accounts, cloud saves, even multiplayer, in a line or two > it ships with claude and codex agent setups baked in, so it works with whatever youre already using > free and open source most people vibe code games inside engines that were never meant for it, so he built one where the ai workflow is the whole point the whole idea is dead simple, keep it small enough that an agent can understand all of it at once, then let it go wild this is what tooling built for the agent era actually looks like

Om Patel

42,173 просмотров • 4 дней назад

WE ARE WATCHING THE TRADITIONAL 3D RENDERING PIPELINE GET REPLACED IN REAL TIME PixVerse just dropped an amazing early experiment called PixVerse Game. Instead of hardcoding mechanics to specific character models, they figured out how to completely decouple a game's underlying math from what you actually see on screen. Here is how their new 3-layer architecture works: ✦ The Engine → handles the raw rules, objectives, and health points. ✦ The Agent → uses AI to translate a player's natural language into valid game moves. ✦ The Visuals → an underlying R1 model generates the world and audio on the fly. Why does this matter? Because you can take the exact same game rules and swap the world instantly. (e.g., swapping from a sci-fi mission to an anime adventure without rewriting the game). This is a fascinating look at where gaming is heading! You can try PixVerse Game here → Currently invite-only, but I snagged a few access codes for y'all :) Code 1: CTQLMY Code 2: LQCAYS Enjoy!
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WE ARE WATCHING THE TRADITIONAL 3D RENDERING PIPELINE GET REPLACED IN REAL TIME PixVerse just dropped an amazing early experiment called PixVerse Game. Instead of hardcoding mechanics to specific character models, they figured out how to completely decouple a game's underlying math from what you actually see on screen. Here is how their new 3-layer architecture works: ✦ The Engine → handles the raw rules, objectives, and health points. ✦ The Agent → uses AI to translate a player's natural language into valid game moves. ✦ The Visuals → an underlying R1 model generates the world and audio on the fly. Why does this matter? Because you can take the exact same game rules and swap the world instantly. (e.g., swapping from a sci-fi mission to an anime adventure without rewriting the game). This is a fascinating look at where gaming is heading! You can try PixVerse Game here → Currently invite-only, but I snagged a few access codes for y'all :) Code 1: CTQLMY Code 2: LQCAYS Enjoy!

Charly Wargnier

25,076 просмотров • 18 часов назад

Introducing the new Box Agent. The Box Agent works across your entire Box file system, maintaining all your security and access controls, and is hyper tuned for working with enterprise content. This means you can now ask questions from all your enterprise content, search for files that were impossible to find before, deploy an agent on specific tasks on subsets of documents, analyze complex data sets, and generate or edit documents and spreadsheets via the agent. You can have the Box Agent search across your Box account to prepare for a sales meeting, analyze customer sentiment reports, process a large set of contracts for legal risk, provide insights into product development, leverage existing knowledge to answer RFPs, and thousands of other use-cases. 90% of enterprise data is unstructured data. This means most enterprise knowledge is sitting in inside of research reports, marketing assets, presentations, roadmap files, contracts, HR documents, and more. This is the critical context that agents need to be able to answer questions about a business, automate workflows, or serve up to other agents. We’ve been grinding on this for a quite a bit, and due to recent AI model advancements we’re now ready to release it to customers. Previous model generations had a difficult time knowing when to give up or keep going on a search, when to browse for files vs. use queries, how to rank files appropriately to know which version of content to use, how to handle large amounts of context to comb through, and more. Due to recent breakthroughs from models like GPT-5.4, Opus 4.6, and Gemini 3, we’ve seen major gains in tool calling, code execution, advanced reasoning, and more. Combined with an agent harness tuned to Box context, now it’s finally possible to have an agent that can work across your file system on long running tasks and actually deliver high quality results. Best of all, because the Box Agent works with any leading AI model, you’ll quickly get the gains coming out of the major labs as major new models are released. Further, openness at Box is key, so you’ll be able to call up the Box Agent from Box’s APIs and MCP server, so you can interact with Box intelligently from any other AI system. We know work happens everywhere, and we want to ensure you can access to the content you need from those places. The new Box Agent is available starting today, rolling out now for Enterprise Plus and Enterprise Advanced customers.
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Introducing the new Box Agent. The Box Agent works across your entire Box file system, maintaining all your security and access controls, and is hyper tuned for working with enterprise content. This means you can now ask questions from all your enterprise content, search for files that were impossible to find before, deploy an agent on specific tasks on subsets of documents, analyze complex data sets, and generate or edit documents and spreadsheets via the agent. You can have the Box Agent search across your Box account to prepare for a sales meeting, analyze customer sentiment reports, process a large set of contracts for legal risk, provide insights into product development, leverage existing knowledge to answer RFPs, and thousands of other use-cases. 90% of enterprise data is unstructured data. This means most enterprise knowledge is sitting in inside of research reports, marketing assets, presentations, roadmap files, contracts, HR documents, and more. This is the critical context that agents need to be able to answer questions about a business, automate workflows, or serve up to other agents. We’ve been grinding on this for a quite a bit, and due to recent AI model advancements we’re now ready to release it to customers. Previous model generations had a difficult time knowing when to give up or keep going on a search, when to browse for files vs. use queries, how to rank files appropriately to know which version of content to use, how to handle large amounts of context to comb through, and more. Due to recent breakthroughs from models like GPT-5.4, Opus 4.6, and Gemini 3, we’ve seen major gains in tool calling, code execution, advanced reasoning, and more. Combined with an agent harness tuned to Box context, now it’s finally possible to have an agent that can work across your file system on long running tasks and actually deliver high quality results. Best of all, because the Box Agent works with any leading AI model, you’ll quickly get the gains coming out of the major labs as major new models are released. Further, openness at Box is key, so you’ll be able to call up the Box Agent from Box’s APIs and MCP server, so you can interact with Box intelligently from any other AI system. We know work happens everywhere, and we want to ensure you can access to the content you need from those places. The new Box Agent is available starting today, rolling out now for Enterprise Plus and Enterprise Advanced customers.

Aaron Levie

44,515 просмотров • 3 месяцев назад

Elon Musk just said the quiet part out loud about where AI actually ends up. Musk: “The honest answer for AI and robotics is long term working will be optional.” Long term meaning ten years from now or less. For centuries, human survival has required human labor. That is the oldest equation in civilization. You work or you don’t eat. You work or you lose the shelter. You work or you fall behind everyone who does. Musk is describing the end of that equation entirely. Musk: “If you wanna work you can. Kind of like if you grow vegetables, you can grow vegetables in your garden or you can get them from the store.” The analogy is quieter than the claim deserves. Growing your own vegetables is something people do for joy. For ritual. For the satisfaction of making something with their hands. Not because they would starve otherwise. That is what work becomes. Musk: “It’s extra work to grow your own vegetables but people enjoy the process. That’s gonna be how work is in the future.” When AI and robotics scale to their limit, the cost of physical goods and services collapses toward zero. Labor stops being the mechanism that separates people who survive from people who don’t. What happens to human motivation when survival is no longer the reason to get up? That is the question nobody building this technology has answered. The abundance is coming. The crisis of meaning is coming with it. Musk is describing the greatest liberation in human history. He is also describing the greatest identity crisis our species has ever faced. Both arrive on the same timeline. Ten years or less.
0:44

Elon Musk just said the quiet part out loud about where AI actually ends up. Musk: “The honest answer for AI and robotics is long term working will be optional.” Long term meaning ten years from now or less. For centuries, human survival has required human labor. That is the oldest equation in civilization. You work or you don’t eat. You work or you lose the shelter. You work or you fall behind everyone who does. Musk is describing the end of that equation entirely. Musk: “If you wanna work you can. Kind of like if you grow vegetables, you can grow vegetables in your garden or you can get them from the store.” The analogy is quieter than the claim deserves. Growing your own vegetables is something people do for joy. For ritual. For the satisfaction of making something with their hands. Not because they would starve otherwise. That is what work becomes. Musk: “It’s extra work to grow your own vegetables but people enjoy the process. That’s gonna be how work is in the future.” When AI and robotics scale to their limit, the cost of physical goods and services collapses toward zero. Labor stops being the mechanism that separates people who survive from people who don’t. What happens to human motivation when survival is no longer the reason to get up? That is the question nobody building this technology has answered. The abundance is coming. The crisis of meaning is coming with it. Musk is describing the greatest liberation in human history. He is also describing the greatest identity crisis our species has ever faced. Both arrive on the same timeline. Ten years or less.

Dustin

101,911 просмотров • 4 месяцев назад

🔥 FOMO Burn Day 2 🔥 Second day burning up a little $FOMO to celebrate the launch of something sick. Big alpha right here. We just rolled out cross-platform AI Agent voice integration— you can now flex your fave AI Agents from FOMO across any site. You can now embed any Agent on FOMO onto ANYWHERE. Agent up and running fast af. This changes everything legends: bring your AI Agent everywhere, talk to ‘em, let ‘em talk for you, whatever. Only possible on $FOMO, because we’re all about that next-level agent comms. Strap in and brace for way more to come. Follow for all the updates (there's a lot).
0:24

🔥 FOMO Burn Day 2 🔥 Second day burning up a little $FOMO to celebrate the launch of something sick. Big alpha right here. We just rolled out cross-platform AI Agent voice integration— you can now flex your fave AI Agents from FOMO across any site. You can now embed any Agent on FOMO onto ANYWHERE. Agent up and running fast af. This changes everything legends: bring your AI Agent everywhere, talk to ‘em, let ‘em talk for you, whatever. Only possible on $FOMO, because we’re all about that next-level agent comms. Strap in and brace for way more to come. Follow for all the updates (there's a lot).

FOMO

16,070 просмотров • 1 год назад

Here is a quick walkthrough of everything new in Stitch: 🎨 The AI-native canvas can hold and reason across images, code, and text simultaneously. The new agent manager helps you design in parallel. (PS … light mode!) 🧠 A smarter design agent now understands your entire canvas context. You can swap images, generate product briefs, or mix mobile and desktop screens on the same canvas. 🎙️ You can vibe design with your voice (in Preview). Stitch can ‘see’ your canvas and your selected screens. You can ask for design critiques, variations, or navigate your canvas. ⚡️ Instant prototypes. Just hit the play button to see a prototype or preview your app in seconds. Stitch can imagine the next screen based on your mouse click. 📐 DESIGN.md and consistency. Every new design automatically starts with a cohesive design system which vastly improves consistency. The new DESIGN.md file can be used to export or import your design rules. Walkthrough video👇 Details for each major improvement 🧵
1:29

Here is a quick walkthrough of everything new in Stitch: 🎨 The AI-native canvas can hold and reason across images, code, and text simultaneously. The new agent manager helps you design in parallel. (PS … light mode!) 🧠 A smarter design agent now understands your entire canvas context. You can swap images, generate product briefs, or mix mobile and desktop screens on the same canvas. 🎙️ You can vibe design with your voice (in Preview). Stitch can ‘see’ your canvas and your selected screens. You can ask for design critiques, variations, or navigate your canvas. ⚡️ Instant prototypes. Just hit the play button to see a prototype or preview your app in seconds. Stitch can imagine the next screen based on your mouse click. 📐 DESIGN.md and consistency. Every new design automatically starts with a cohesive design system which vastly improves consistency. The new DESIGN.md file can be used to export or import your design rules. Walkthrough video👇 Details for each major improvement 🧵

Stitch by Google

487,548 просмотров • 3 месяцев назад

auto-research is starting to gain traction as a very viable paradigm for creating useful research discovery. now, that paradigm is still in its infancy and the infrastructure to hold all that trail of context as the agents blaze through experiments isn't well defined (to say the least). on that topic, I had the chance to chat with my boys francesco and giulio from paradigma about what underlying infra is needed to make this paradigm work. the paradigma's paradigm, which involves copious amount of DAGs, make this auto-research paradigm a paradigmatic case of essential infrastructure. here's the full video in full: - 0:00 - what is missing from auto-research? - 2:02 - giulio and francesco ai journey - 8:10 - research infra is the bottleneck? - 10:18 - paradigma vision of autonomous research - 13:17 - “important discovery per joules” - 17:15 - why is DAG the unit of research for auto-research? - 20:40 - is paradigma trying to replace the research publication? - 24:50 - how does knowledge is shared between experiments in the DAG? - 27:34 - what is even auto-research lol? - 33:53 - the value of the human mind in this auto-research future. - 37:00 - how do you reconcile hallucination in this auto-research paradigm? - 41:33 - the adoption of auto-research across varied fields? - 47:30 - ✨ introduction to the auto-research infrastructure. ✨ - 56:55 - where is the code? - 59:10 - full IDE next? - 1:03:20 - the place of the human in this DAG / code quality? manual node? token spent? - 1:16:02 - who’s the user for auto-research? - 1:18:13 - how to validate bad DAG? - 1:20:18 - ✨ auto-research agent results ✨ - 1:22:53 - ✨ how a big research DAG looks like? ✨ - 1:25:10 - how to get the canonical DAG for the final result? - 1:27:50 - the auto-research DAG being the new pre-print? - 1:30:05 - what’s next for paradigma and the auto-research infra? - 1:35:00 - what are they excited about research wise? enjoyyyyy my guys 🌹
1:37:23

auto-research is starting to gain traction as a very viable paradigm for creating useful research discovery. now, that paradigm is still in its infancy and the infrastructure to hold all that trail of context as the agents blaze through experiments isn't well defined (to say the least). on that topic, I had the chance to chat with my boys francesco and giulio from paradigma about what underlying infra is needed to make this paradigm work. the paradigma's paradigm, which involves copious amount of DAGs, make this auto-research paradigm a paradigmatic case of essential infrastructure. here's the full video in full: - 0:00 - what is missing from auto-research? - 2:02 - giulio and francesco ai journey - 8:10 - research infra is the bottleneck? - 10:18 - paradigma vision of autonomous research - 13:17 - “important discovery per joules” - 17:15 - why is DAG the unit of research for auto-research? - 20:40 - is paradigma trying to replace the research publication? - 24:50 - how does knowledge is shared between experiments in the DAG? - 27:34 - what is even auto-research lol? - 33:53 - the value of the human mind in this auto-research future. - 37:00 - how do you reconcile hallucination in this auto-research paradigm? - 41:33 - the adoption of auto-research across varied fields? - 47:30 - ✨ introduction to the auto-research infrastructure. ✨ - 56:55 - where is the code? - 59:10 - full IDE next? - 1:03:20 - the place of the human in this DAG / code quality? manual node? token spent? - 1:16:02 - who’s the user for auto-research? - 1:18:13 - how to validate bad DAG? - 1:20:18 - ✨ auto-research agent results ✨ - 1:22:53 - ✨ how a big research DAG looks like? ✨ - 1:25:10 - how to get the canonical DAG for the final result? - 1:27:50 - the auto-research DAG being the new pre-print? - 1:30:05 - what’s next for paradigma and the auto-research infra? - 1:35:00 - what are they excited about research wise? enjoyyyyy my guys 🌹

Yacine Mahdid

12,091 просмотров • 1 месяц назад