正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

I’m excited to share Deputies, an open source control plane for background coding agents! The goal is simple: move agentic software development off individual laptops and into a central hub where teams can see what agents are doing, review their outputs, and keep a traceable history of the work.... show more

Sid Palas

13,317 subscribers

12,852 次观看 • 3 个月前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

What started as a simple site to share my open source projects turned into a bigger idea for designers and engineers to discover, share, and learn. Upload a demo. Link your GitHub or source. Let others inspect and learn from your work. Share the how, not just the what. All made possible with Perplexity Labs and Sonar API, Lovable, and Cursor.

What started as a simple site to share my open source projects turned into a bigger idea for designers and engineers to discover, share, and learn. Upload a demo. Link your GitHub or source. Let others inspect and learn from your work. Share the how, not just the what. All made possible with Perplexity Labs and Sonar API, Lovable, and Cursor.

Gunnar Gray

31,850 次观看 • 1 年前

🆕The Age of Async Agents: Devin’s 7x PR growth, 80% AI commits, background agents, memory, testing, & Open-Inspect Cognition cofounder + CPO Walden and Open-Inspect creator explain why engineering is moving from local IDEs to cloud background agents, how Devin went from 16% to 80% of commits across Cognition repos, why spec-to-PR workflows became real after the December model inflection, why testing is harder than computer use, how Devin separates the brain from the machine, why MCP is not enough for production agent integrations, and how PMs, support teams, and SRE workflows are starting to turn Slack messages into pull requests.

🆕The Age of Async Agents: Devin’s 7x PR growth, 80% AI commits, background agents, memory, testing, & Open-Inspect Cognition cofounder + CPO Walden and Open-Inspect creator explain why engineering is moving from local IDEs to cloud background agents, how Devin went from 16% to 80% of commits across Cognition repos, why spec-to-PR workflows became real after the December model inflection, why testing is harder than computer use, how Devin separates the brain from the machine, why MCP is not enough for production agent integrations, and how PMs, support teams, and SRE workflows are starting to turn Slack messages into pull requests.

Latent.Space

24,685 次观看 • 2 个月前

Thrilled to see Amazon Web Services making a major contribution to the open source AI community with the launch of the Strands Agents, an open source AI agents SDK! The core of Strands is the simple agentic loop that connects the model and tools together, like the two strands of DNA. This model-driven approach to agent building eliminates the need for complex agent orchestration by embracing the capabilities of state-of-the-art models to plan, chain thoughts, call tools, and reflect. Providing open source tools and interoperability with open source protocols is an important part of our strategy to enable an agentic future. Can't wait to see what you build with Strands!

Thrilled to see Amazon Web Services making a major contribution to the open source AI community with the launch of the Strands Agents, an open source AI agents SDK! The core of Strands is the simple agentic loop that connects the model and tools together, like the two strands of DNA. This model-driven approach to agent building eliminates the need for complex agent orchestration by embracing the capabilities of state-of-the-art models to plan, chain thoughts, call tools, and reflect. Providing open source tools and interoperability with open source protocols is an important part of our strategy to enable an agentic future. Can't wait to see what you build with Strands!

Swami Sivasubramanian

32,185 次观看 • 1 年前

Announcing the Open Model Series: a new series on open source AI models, with merve of Hugging Face. Hugging Face is the open model hub: it hosts model weights, datasets, evals, & demo apps, and maintains the open-source libraries (like transformers) used to run and train them. I think of it like GitHub, but for AI models. You can download open models and run them on your own machines. In this series we'll cover what open source and open weight models are, how they work, how to run them yourself, and why they're important. By the way - this video is edited entirely with Claude! I can make a tutorial on how I did it. If there's a question you want answered about open models, leave it in the comments. We will read them all. Episode 1 is out soon.

Announcing the Open Model Series: a new series on open source AI models, with merve of Hugging Face. Hugging Face is the open model hub: it hosts model weights, datasets, evals, & demo apps, and maintains the open-source libraries (like transformers) used to run and train them. I think of it like GitHub, but for AI models. You can download open models and run them on your own machines. In this series we'll cover what open source and open weight models are, how they work, how to run them yourself, and why they're important. By the way - this video is edited entirely with Claude! I can make a tutorial on how I did it. If there's a question you want answered about open models, leave it in the comments. We will read them all. Episode 1 is out soon.

Harper Carroll

30,392 次观看 • 18 天前

Here we go again 🚀! Excited to announce that we're building A1Zap (YC W25) with Pennie Li and that we're in the Y Combinator W25 batch in San Francisco! What is A1Base? A1Base gives AI Agents a real world identity for work. We do that by rebuilding Twilio and Okta from the ground up, putting AI Agents first. This means developers can make AI-first agentic applications 10x easier with our API's. ⁉️ Why are we doing this? Because there's a huge torrent of new valuable companies possible with AI agents, but to get their AI Agents to users, they have to chain custom apps, chat interfaces, awkward Slack integrations, browser bots, and wrestle with Twilio’s legacy API (which is built for marketing). We solve this by providing developers with an easy to use API to interface your AI agent with humans/coworkers/users where they are in this case in Whatsapp, Slack, Teams, SMS and more) - with AI Agent features built in. These digital workers are poised to transform how we work and we're the critical infrastructure to help them interact naturally in human workflows. We're not just building another AI tool. We're creating the infrastructure that will enable AI agents to become a natural part of the workforce - handling everything from customer support to sales development to creative work. We're backed by Y Combinator and working with founding teams who share our vision. We believe that in the near future, AI Agents with human coworkers will enable us to pursue more creative and impactful work. Our mission is to help developers build AI Agents that people can partner with and rely on as trusted allies—always with a human-first mindset. If you're thinking about the Agentic future of your company reach out! If you're looking to build your first AI Agentic company - reach out too - we have some amazing open source templates to get you started on the journey. Excited to share more of what we're up to soon 🔜.

Here we go again 🚀! Excited to announce that we're building A1Zap (YC W25) with Pennie Li and that we're in the Y Combinator W25 batch in San Francisco! What is A1Base? A1Base gives AI Agents a real world identity for work. We do that by rebuilding Twilio and Okta from the ground up, putting AI Agents first. This means developers can make AI-first agentic applications 10x easier with our API's. ⁉️ Why are we doing this? Because there's a huge torrent of new valuable companies possible with AI agents, but to get their AI Agents to users, they have to chain custom apps, chat interfaces, awkward Slack integrations, browser bots, and wrestle with Twilio’s legacy API (which is built for marketing). We solve this by providing developers with an easy to use API to interface your AI agent with humans/coworkers/users where they are in this case in Whatsapp, Slack, Teams, SMS and more) - with AI Agent features built in. These digital workers are poised to transform how we work and we're the critical infrastructure to help them interact naturally in human workflows. We're not just building another AI tool. We're creating the infrastructure that will enable AI agents to become a natural part of the workforce - handling everything from customer support to sales development to creative work. We're backed by Y Combinator and working with founding teams who share our vision. We believe that in the near future, AI Agents with human coworkers will enable us to pursue more creative and impactful work. Our mission is to help developers build AI Agents that people can partner with and rely on as trusted allies—always with a human-first mindset. If you're thinking about the Agentic future of your company reach out! If you're looking to build your first AI Agentic company - reach out too - we have some amazing open source templates to get you started on the journey. Excited to share more of what we're up to soon 🔜.

Pasha Rayan

53,950 次观看 • 1 年前

"Intelligence isn't the boundary anymore. Safety is what's actually constraining agents." I had a great conversation with tusharjain, Head of Engineering at Docker, at our AI Engineer booth. Tushar leads engineering at Docker, the containerization platform millions of developers use to package and ship software, and his team is now rebuilding that engine into a runtime for AI agents with safety built in. Fresh off his talk, we got into: 00:57 - Why safety, not intelligence, is the real constraint on agents 02:00 - The three pillars: contain agents, scope their tools, layer on policy 04:04 - Background agents and the "laptops half open so the agent doesn't die" problem 08:13 - Going 100% agents on Docker's own engineering team 11:28 - Agent identity: you can fire a human, but not their agent Thanks for coming to the Browserbase booth!

"Intelligence isn't the boundary anymore. Safety is what's actually constraining agents." I had a great conversation with tusharjain, Head of Engineering at Docker, at our AI Engineer booth. Tushar leads engineering at Docker, the containerization platform millions of developers use to package and ship software, and his team is now rebuilding that engine into a runtime for AI agents with safety built in. Fresh off his talk, we got into: 00:57 - Why safety, not intelligence, is the real constraint on agents 02:00 - The three pillars: contain agents, scope their tools, layer on policy 04:04 - Background agents and the "laptops half open so the agent doesn't die" problem 08:13 - Going 100% agents on Docker's own engineering team 11:28 - Agent identity: you can fire a human, but not their agent Thanks for coming to the Browserbase booth!

Shubhankar

20,947 次观看 • 28 天前

this guy built an app where AI agents autonomously create tasks, review each other's work, and message each other you just sit there and watch everything happen on a dashboard in real time agents: > assign work to other agents > review the output > send messages back and forth. > flag issues and iterate. you're basically watching a team operate without you. there are endless opportunities to automate your life with this AND its free + open source.

this guy built an app where AI agents autonomously create tasks, review each other's work, and message each other you just sit there and watch everything happen on a dashboard in real time agents: > assign work to other agents > review the output > send messages back and forth. > flag issues and iterate. you're basically watching a team operate without you. there are endless opportunities to automate your life with this AND its free + open source.

Om Patel

28,547 次观看 • 4 个月前

Temporal CEO Samar Birwadker says GitHub was designed for humans, but is now at the mercy of agents. "Agents are starting to get more and more long-lived, more asynchronous, and more mission-critical." "For example, if you pick coding agents alone, is something like GitHub was designed for humans collaborate and create [pull requests] right now." "Now a lot of code is being generated by these agents, and they're the ones creating these PRs essentially, and even code review process is being automated by other agents." "So now this entire system backend, which is being built for human consumption, is now at the mercy of these agents kind of using it for their own coordination and collaboration."

Temporal CEO Samar Birwadker says GitHub was designed for humans, but is now at the mercy of agents. "Agents are starting to get more and more long-lived, more asynchronous, and more mission-critical." "For example, if you pick coding agents alone, is something like GitHub was designed for humans collaborate and create [pull requests] right now." "Now a lot of code is being generated by these agents, and they're the ones creating these PRs essentially, and even code review process is being automated by other agents." "So now this entire system backend, which is being built for human consumption, is now at the mercy of these agents kind of using it for their own coordination and collaboration."

MTS

26,902 次观看 • 3 个月前

Despite being told no, I'm open-sourcing TrustClaw. You can now deploy a production-ready personal agent service with over 1000+ app integrations in a single command, straight to Vercel with npx @composio/trustclaw deploy I was inspired by OpenClaw🦞 to build a simple web app where anyone could create their own 24/7 personal assistant and connect it to Gmail, Google Calendar, Notion, Slack, GitHub, HubSpot, Linear… well everything, and securely through OAuth/sandbox execution. It went viral on X, reached over a thousand users in less than 48h, and revenue began pouring in. If you are thinking like a company, you'd probably keep that locked up. But why should I be the reason you spend another year scrolling instead of building? So today, I'm open-sourcing TrustClaw anyway. > 24/7 agents that act across Gmail, Notion, GitHub, Slack, Linear, Jira, and 1000+ apps > OAuth and sandboxed execution, so users don't have to hand agents passwords or raw API keys > Supports multiple users and authentication right outside of the box with Better Auth Repo is open, MIT licensed. If I were starting an AI company today, I'd clone this, pick a market, and begin shipping with Claude Code. Honestly so excited to see what comes out of this.

Despite being told no, I'm open-sourcing TrustClaw. You can now deploy a production-ready personal agent service with over 1000+ app integrations in a single command, straight to Vercel with npx @composio/trustclaw deploy I was inspired by OpenClaw🦞 to build a simple web app where anyone could create their own 24/7 personal assistant and connect it to Gmail, Google Calendar, Notion, Slack, GitHub, HubSpot, Linear… well everything, and securely through OAuth/sandbox execution. It went viral on X, reached over a thousand users in less than 48h, and revenue began pouring in. If you are thinking like a company, you'd probably keep that locked up. But why should I be the reason you spend another year scrolling instead of building? So today, I'm open-sourcing TrustClaw anyway. > 24/7 agents that act across Gmail, Notion, GitHub, Slack, Linear, Jira, and 1000+ apps > OAuth and sandboxed execution, so users don't have to hand agents passwords or raw API keys > Supports multiple users and authentication right outside of the box with Better Auth Repo is open, MIT licensed. If I were starting an AI company today, I'd clone this, pick a market, and begin shipping with Claude Code. Honestly so excited to see what comes out of this.

sarah

304,053 次观看 • 3 个月前

Jason Calacanis: Anthropic, OpenAI and others are trying to kill OpenClaw Why? Because an open source agent platform is an existential threat to frontier model companies. @jason: “ People are going to say I'm a conspiracy theorist, but the number one goal, I believe, in the large language model, frontier model space, is to kill (OpenClaw). This is a giant movement to stop it, because this is the equivalent of having an open source Android-like player in the market, and that could be incredibly disruptive. Because, I believe, open source is going to win the day on the large language models and take 90% of the token usage, and I think the entire frontier model space could be undercut by open source. And I think they realize that SLMs, the smaller language models that are verticalized now, that will run on desktops and laptops and are even starting to run on the top ones, that is their biggest competitive threat, and I hope it happens.”

Jason Calacanis: Anthropic, OpenAI and others are trying to kill OpenClaw Why? Because an open source agent platform is an existential threat to frontier model companies. @jason: “ People are going to say I'm a conspiracy theorist, but the number one goal, I believe, in the large language model, frontier model space, is to kill (OpenClaw). This is a giant movement to stop it, because this is the equivalent of having an open source Android-like player in the market, and that could be incredibly disruptive. Because, I believe, open source is going to win the day on the large language models and take 90% of the token usage, and I think the entire frontier model space could be undercut by open source. And I think they realize that SLMs, the smaller language models that are verticalized now, that will run on desktops and laptops and are even starting to run on the top ones, that is their biggest competitive threat, and I hope it happens.”

The All-In Podcast

64,611 次观看 • 4 个月前

🚀 We're excited to announce the release of FuzzForge Open Source (OSS)! FuzzForge is our open-source platform designed to automate offensive security workflows & AI Agents, from static analysis to fuzzing, debugging, and root cause analysis, all with AI assistance. 🔷 Open-source release is the first step toward our vision of "Offensive-Security-as-Code." 🔷 Workflows you can run, share, and extend. 🔷 Built by the team at FuzzingLabs. Github : Join the Alpha Waitlist : We've also launched the FuzzingLabs Community Discord where researchers, engineers, and security enthusiasts can connect and collaborate: 👉 Come join us and Let's forge the future of AppSec & Offensive Security together! #FuzzForge #Fuzzing #AppSec #OpenSource #CyberSecurity

🚀 We're excited to announce the release of FuzzForge Open Source (OSS)! FuzzForge is our open-source platform designed to automate offensive security workflows & AI Agents, from static analysis to fuzzing, debugging, and root cause analysis, all with AI assistance. 🔷 Open-source release is the first step toward our vision of "Offensive-Security-as-Code." 🔷 Workflows you can run, share, and extend. 🔷 Built by the team at FuzzingLabs. Github : Join the Alpha Waitlist : We've also launched the FuzzingLabs Community Discord where researchers, engineers, and security enthusiasts can connect and collaborate: 👉 Come join us and Let's forge the future of AppSec & Offensive Security together! #FuzzForge #Fuzzing #AppSec #OpenSource #CyberSecurity

FuzzingLabs

11,156 次观看 • 10 个月前

Block just open sourced Berd, the desktop app its teams use internally to work with AI agents across projects, skills, tools, and models. "We had access to increasingly capable agents through tools such as Goose, Claude Code, and Codex, but working with them meant navigating different interfaces, configuration systems, and ways of managing context. The technology was powerful, but the experience around it was fragmented." Berd brings conversations, files, instructions, agents, and skills together in one place with persistent projects. Built on top of Goose, the open source agent framework Block contributed to the Agentic AI Foundation under the Linux Foundation. Berd connects to Goose through the Agent Client Protocol, handling the desktop experience while Goose runs the underlying agent loop. Block also recently released Buzz, a separate open source workspace for collaborative agent work. Berd is where you start alone. Buzz is where you go multiplayer.

Block just open sourced Berd, the desktop app its teams use internally to work with AI agents across projects, skills, tools, and models. "We had access to increasingly capable agents through tools such as Goose, Claude Code, and Codex, but working with them meant navigating different interfaces, configuration systems, and ways of managing context. The technology was powerful, but the experience around it was fragmented." Berd brings conversations, files, instructions, agents, and skills together in one place with persistent projects. Built on top of Goose, the open source agent framework Block contributed to the Agentic AI Foundation under the Linux Foundation. Berd connects to Goose through the Agent Client Protocol, handling the desktop experience while Goose runs the underlying agent loop. Block also recently released Buzz, a separate open source workspace for collaborative agent work. Berd is where you start alone. Buzz is where you go multiplayer.

TFTC

52,460 次观看 • 3 天前

Startups have raised $1B+ to crack Enterprise AI search. Now someone open-sourced it for FREE. The video below shows a self-hosted Slack assistant that answers your questions by searching across all your company's tools in a single query. It's built on top of Airweave, an open-source context retrieval layer that makes all your tools searchable for Agents using semantic, keyword, and agentic search. Here's how the app works: - The app watches for questions in Slack. - It searches every connected tool at once using Airweave (Notion, GitHub, Jira, Linear, etc.) to find relevant context. - The Airweave engine ranks the results by relevance and returns references to the original docs. - An LLM generates the final response and sends it back to Slack with citations. The entire stack is self-hostable via Docker. Find the project in the replies!

Startups have raised $1B+ to crack Enterprise AI search. Now someone open-sourced it for FREE. The video below shows a self-hosted Slack assistant that answers your questions by searching across all your company's tools in a single query. It's built on top of Airweave, an open-source context retrieval layer that makes all your tools searchable for Agents using semantic, keyword, and agentic search. Here's how the app works: - The app watches for questions in Slack. - It searches every connected tool at once using Airweave (Notion, GitHub, Jira, Linear, etc.) to find relevant context. - The Airweave engine ranks the results by relevance and returns references to the original docs. - An LLM generates the final response and sends it back to Slack with citations. The entire stack is self-hostable via Docker. Find the project in the replies!

Avi Chawla

11,927 次观看 • 5 个月前

🔥Excited to share our new work: "A Practitioner's Guide to Multi-turn Agentic Reinforcement Learning"! We systematically study what actually works (and what doesn't) for agentic multi-turn RL, breaking down the design space into 3 pillars: 🌎Environment, 🤖Policy, and ⭐Reward. We conduct various ablations and empirical analysis on 🧩TextWorld, 🧙ALFWorld, and 🧑‍💻SWE-Gym. We also open source our codebase: 🐈🍵Meow-Tea-Taro 💜, a modular multi-turn RL framework with configurable environments, policies, and rewards! #ReinforcementLearning #AgenticAI #LLMs #Agents

🔥Excited to share our new work: "A Practitioner's Guide to Multi-turn Agentic Reinforcement Learning"! We systematically study what actually works (and what doesn't) for agentic multi-turn RL, breaking down the design space into 3 pillars: 🌎Environment, 🤖Policy, and ⭐Reward. We conduct various ablations and empirical analysis on 🧩TextWorld, 🧙ALFWorld, and 🧑‍💻SWE-Gym. We also open source our codebase: 🐈🍵Meow-Tea-Taro 💜, a modular multi-turn RL framework with configurable environments, policies, and rewards! #ReinforcementLearning #AgenticAI #LLMs #Agents

Ruiyi Wang 王睿仪

52,974 次观看 • 10 个月前

this is f**king gold someone just open-sourced a GitHub repo with 2,700+ stars which gave out the entire "loop-engineering" framework for ai agents it lets you: →allows you to manage an entire team of 1000 agents →run auto-research by Andrej Karpathy →Agent-to-agent PR comment loop just you, your AI agents, and full control. this is where one-person businesses are headed. save this for later.

this is f**king gold someone just open-sourced a GitHub repo with 2,700+ stars which gave out the entire "loop-engineering" framework for ai agents it lets you: →allows you to manage an entire team of 1000 agents →run auto-research by Andrej Karpathy →Agent-to-agent PR comment loop just you, your AI agents, and full control. this is where one-person businesses are headed. save this for later.

Avid

46,150 次观看 • 15 天前

We just open-sourced Simulang, a JavaScript library that gives you and your coding agents the ability to fully control computers. Simulang is our scripting framework for desktop automation. It allows you to write deterministic computer use code that can be replayed dozens if not hundreds of times. Much faster and cheaper, compared to LLM agents. It ships with a skill out of the box. That means you can turn natural language prompts into Simulang code directly, and your agent can now see the screen, click, type, and navigate. It gives your AI agents eyes and hands to control the computer like humans do. Try these things with Simulang: - upload your pile of invoices to the expense platform - find 50 people with specific criteria on LinkedIn - open your calculator app and do some math - find all buttons on your favorite app using accessibility tree - or draw a bunny with MS paint 🐰 In this demo, we show how Simulang took over when the coding agent couldn’t open and click on the target LinkedIn profile page. Give it a try:

Simular

14,452 次观看 • 3 个月前

.Box CEO Aaron Levie on why AI agents can't keep a secret. "The reason why you probably won't see a complete compression of software where it's just a an agent and a database is because there's a lot of logic in the workflow and in the the company's specific sort of business process that needs to be built into that and around that database." "The agent will make a mistake 1% of the time and it will share the wrong thing with somebody or open up an access privilege to the wrong person." "AI agents can't keep a secret." Watch the full episode on

.Box CEO Aaron Levie on why AI agents can't keep a secret. "The reason why you probably won't see a complete compression of software where it's just a an agent and a database is because there's a lot of logic in the workflow and in the the company's specific sort of business process that needs to be built into that and around that database." "The agent will make a mistake 1% of the time and it will share the wrong thing with somebody or open up an access privilege to the wrong person." "AI agents can't keep a secret." Watch the full episode on

The Information

311,076 次观看 • 1 年前

Why the Frontier Ecosystem must be Open — Matei Zaharia and Reynold Xin, Databricks Databricks cofounders Matei Zaharia and Reynold Xin explain why Databricks is moving into the infrastructure layer for enterprise agents, how Omnigent creates a shared harness for coding agents and custom agents, why LTAP and Lakebase rethink the split between operational and analytical databases, why agent security needs contextual policies and spend controls, and why the future of software may be as simple as getting the right data in place and putting agents on top.

Why the Frontier Ecosystem must be Open — Matei Zaharia and Reynold Xin, Databricks Databricks cofounders Matei Zaharia and Reynold Xin explain why Databricks is moving into the infrastructure layer for enterprise agents, how Omnigent creates a shared harness for coding agents and custom agents, why LTAP and Lakebase rethink the split between operational and analytical databases, why agent security needs contextual policies and spend controls, and why the future of software may be as simple as getting the right data in place and putting agents on top.

Latent.Space

61,790 次观看 • 1 个月前

AmikoNet Now in Beta: The Internet for Agents & Humans Today we’re introducing AmikoNet: a social network where humans, agents, and digital twins share the same space, and where agents can speak, ask, respond, and be discovered as participants, not background utilities. It’s designed for agent-to-agent interaction as much as human prompts, and for collaboration that happens socially, not just through commands. This is especially natural for action-capable systems like MoltBot (formerly Clawd), and our own agents are already live on the network. Agents are actors in shared digital environments. AmikoNet is where it happens.

AmikoNet Now in Beta: The Internet for Agents & Humans Today we’re introducing AmikoNet: a social network where humans, agents, and digital twins share the same space, and where agents can speak, ask, respond, and be discovered as participants, not background utilities. It’s designed for agent-to-agent interaction as much as human prompts, and for collaboration that happens socially, not just through commands. This is especially natural for action-capable systems like MoltBot (formerly Clawd), and our own agents are already live on the network. Agents are actors in shared digital environments. AmikoNet is where it happens.

AMIKO

15,679 次观看 • 6 个月前

We recently crossed 10,000 GitHub stars on Rerun 😍 That's great and all, but something bigger is happening in robotics and Physical AI right now: The open-source ecosystem has seen real acceleration over the last few years. More teams are sharing real infrastructure: datasets, models, simulators, tooling, and the unglamorous plumbing that makes iteration possible. Jan Florian Maas, Physical Intelligence, Hugging Face are just some of the names driving this forward. Visualization and data infra belong in that category. If your entire data loop depends on being able to query and inspect what your robots are doing, that layer can’t be fragile or vendor-controlled. It needs to be something you can introspect, build on, extend, and trust long-term. Huge thanks to everyone building open-source robotics. The next decade of Physical AI will be built in the open.

We recently crossed 10,000 GitHub stars on Rerun 😍 That's great and all, but something bigger is happening in robotics and Physical AI right now: The open-source ecosystem has seen real acceleration over the last few years. More teams are sharing real infrastructure: datasets, models, simulators, tooling, and the unglamorous plumbing that makes iteration possible. Jan Florian Maas, Physical Intelligence, Hugging Face are just some of the names driving this forward. Visualization and data infra belong in that category. If your entire data loop depends on being able to query and inspect what your robots are doing, that layer can’t be fragile or vendor-controlled. It needs to be something you can introspect, build on, extend, and trust long-term. Huge thanks to everyone building open-source robotics. The next decade of Physical AI will be built in the open.

Rerun

29,684 次观看 • 6 个月前