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。

🚨 Most AI is fast. But often wrong. MiroMind flips that. It doesn’t just give answers — it proves them. • Deep research across 100s of live sources • Fully verifiable, replayable reasoning • Multi-agent verification (Planner → Verifier) • Predictive insights with real probabilities Built for people where... show more

Tulsi Soni

151,981 subscribers

45,727 次观看 • 4 个月前 •via X (Twitter)

健康养生科学技术

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

I stopped trusting AI outputs blindly. Not because AI is bad… But because I started noticing a pattern. The more I used AI for serious work, the more I had to: → double-check everything → re-verify sources → question the logic behind answers At some point, I thought: “What’s the point of saving time… if I still don’t trust the output?” That’s when I came across MiroMindAI and it felt different from day one. Not flashy. Not trying to impress. Just… built for accuracy. I tested it the same way I test any tool: Real use cases. No hype. • Deep research • Multi-source validation • Complex reasoning tasks And here’s what stood out 👇 🧠 It shows how it thinks Not just answers actual reasoning chains you can read, audit, and replay. 🔍 It doesn’t “summarize”… it investigates Pulls from hundreds of sources and builds structured, evidence-backed reports. ⚖️ It verifies itself before responding Multiple layers checking the output (something most AI tools skip completely) And honestly, this is what clicked for me: Most AI tools today are like 👉 smart interns (fast, helpful, but need supervision) MiroMind feels more like 👉 a senior analyst (slower, but you can rely on it) 💡 One simple shift I noticed: Before: 10 tabs open → cross-checking → still unsure Now: 1 report → clear reasoning → backed by sources I’m not saying this replaces expertise. But it does reduce the noise. A lot. If you’re someone who works in: • research • finance • legal • healthcare You’ll probably appreciate this more than others. 👉

I stopped trusting AI outputs blindly. Not because AI is bad… But because I started noticing a pattern. The more I used AI for serious work, the more I had to: → double-check everything → re-verify sources → question the logic behind answers At some point, I thought: “What’s the point of saving time… if I still don’t trust the output?” That’s when I came across MiroMindAI and it felt different from day one. Not flashy. Not trying to impress. Just… built for accuracy. I tested it the same way I test any tool: Real use cases. No hype. • Deep research • Multi-source validation • Complex reasoning tasks And here’s what stood out 👇 🧠 It shows how it thinks Not just answers actual reasoning chains you can read, audit, and replay. 🔍 It doesn’t “summarize”… it investigates Pulls from hundreds of sources and builds structured, evidence-backed reports. ⚖️ It verifies itself before responding Multiple layers checking the output (something most AI tools skip completely) And honestly, this is what clicked for me: Most AI tools today are like 👉 smart interns (fast, helpful, but need supervision) MiroMind feels more like 👉 a senior analyst (slower, but you can rely on it) 💡 One simple shift I noticed: Before: 10 tabs open → cross-checking → still unsure Now: 1 report → clear reasoning → backed by sources I’m not saying this replaces expertise. But it does reduce the noise. A lot. If you’re someone who works in: • research • finance • legal • healthcare You’ll probably appreciate this more than others. 👉

Md Riyazuddin

20,990 次观看 • 4 个月前

🚀Announcing Qodo Aware 🚀 Your AI coding assistant is flying blind. It sees a few hops from your current file, but misses a LOT of context that matters. Qodo Aware is the first and ONLY deep research agent built for finding the right context across large, complex codebases: 10 repos or 1,000. 🔥 Read the blog here ->

🚀Announcing Qodo Aware 🚀 Your AI coding assistant is flying blind. It sees a few hops from your current file, but misses a LOT of context that matters. Qodo Aware is the first and ONLY deep research agent built for finding the right context across large, complex codebases: 10 repos or 1,000. 🔥 Read the blog here ->

Qodo

19,510 次观看 • 11 个月前

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Himanshu Kumar

228,497 次观看 • 3 个月前

Most AI is built to replace humans. We're building AI to understand them. Introducing Atypica's Subjective World Model. A foundation for understanding how people express themselves, tell their stories, make decisions, and behave under real-world constraints. Not just what users say — but why they choose, hesitate, and act. It powers AI Research, AI Personas, AI Panels, and AI Interviews, helping teams turn user research from a quarterly project into a permanent capability. Watch the video to see how it works and what teams are already building with Atypica. Start to explore this AI-native consumer research platform from here:

Most AI is built to replace humans. We're building AI to understand them. Introducing Atypica's Subjective World Model. A foundation for understanding how people express themselves, tell their stories, make decisions, and behave under real-world constraints. Not just what users say — but why they choose, hesitate, and act. It powers AI Research, AI Personas, AI Panels, and AI Interviews, helping teams turn user research from a quarterly project into a permanent capability. Watch the video to see how it works and what teams are already building with Atypica. Start to explore this AI-native consumer research platform from here:

atypica.AI

16,388 次观看 • 18 天前

Jensen Huang doesn’t use AI to think less. He uses it to think past his own limits. Huang: “90% of my instructions are actually conflated with questions.” The man running a five trillion dollar company doesn’t give AI commands. He interrogates it. Huang: “I take the answer from one AI, give it to the other AI, ask them to critique itself.” Same question. Multiple models. Pit them against each other. Keep only what survives. Not because the machine can’t be trusted. Because challenging it is where the sharpest thinking happens. Huang: “The process of critiquing, criticizing the answers, applying your critical thinking, enhances cognitive skills.” AI doesn’t replace your thinking. It demands more of it than you’ve ever given. Every question takes reasoning. Every answer takes scrutiny. The machine isn’t thinking for you. It’s pulling thinking out of you that didn’t exist before you sat down. Huang: “In order to formulate good questions, you have to be thinking, you have to be analytical, you have to be reasoning yourself.” AI is not the shortcut everyone thinks it is. It is the most powerful cognitive amplifier ever built. It sharpens the engaged. It leaves the passive exactly where they started. Same tool. Same access. The only variable is what you bring to it. The world is debating whether AI will replace human thinking. Wrong conversation. The real question is what happens when a tool built to think for you becomes the thing that forces you to think beyond yourself. That’s not a threat to humanity. That’s the entire point.

Jensen Huang doesn’t use AI to think less. He uses it to think past his own limits. Huang: “90% of my instructions are actually conflated with questions.” The man running a five trillion dollar company doesn’t give AI commands. He interrogates it. Huang: “I take the answer from one AI, give it to the other AI, ask them to critique itself.” Same question. Multiple models. Pit them against each other. Keep only what survives. Not because the machine can’t be trusted. Because challenging it is where the sharpest thinking happens. Huang: “The process of critiquing, criticizing the answers, applying your critical thinking, enhances cognitive skills.” AI doesn’t replace your thinking. It demands more of it than you’ve ever given. Every question takes reasoning. Every answer takes scrutiny. The machine isn’t thinking for you. It’s pulling thinking out of you that didn’t exist before you sat down. Huang: “In order to formulate good questions, you have to be thinking, you have to be analytical, you have to be reasoning yourself.” AI is not the shortcut everyone thinks it is. It is the most powerful cognitive amplifier ever built. It sharpens the engaged. It leaves the passive exactly where they started. Same tool. Same access. The only variable is what you bring to it. The world is debating whether AI will replace human thinking. Wrong conversation. The real question is what happens when a tool built to think for you becomes the thing that forces you to think beyond yourself. That’s not a threat to humanity. That’s the entire point.

Dustin

19,849 次观看 • 1 个月前

🔥 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 ➡️

🔥 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 次观看 • 1 年前

🚀 AI Just Got a Major Upgrade! Say hello to Baidu ERNIE’s new reasoning model — built to push the limits of what AI can do. ✨ Sharper Factual Accuracy – delivers reliable, verified knowledge. ✨ Smarter Instruction Following – understands complex tasks with ease. ✨ Next-Level Reasoning Power – excels in coding, logic, and creative writing. Powered by the ERNIE-4.5 base and reinforced through advanced multi-round reinforcement learning, this model doesn’t just follow commands — it thinks, plans, and executes like never before. From solving real-world problems to generating innovative ideas, Wenxin Yiyan is redefining the AI frontier. 🔥 The future of AI is here →

🚀 AI Just Got a Major Upgrade! Say hello to Baidu ERNIE’s new reasoning model — built to push the limits of what AI can do. ✨ Sharper Factual Accuracy – delivers reliable, verified knowledge. ✨ Smarter Instruction Following – understands complex tasks with ease. ✨ Next-Level Reasoning Power – excels in coding, logic, and creative writing. Powered by the ERNIE-4.5 base and reinforced through advanced multi-round reinforcement learning, this model doesn’t just follow commands — it thinks, plans, and executes like never before. From solving real-world problems to generating innovative ideas, Wenxin Yiyan is redefining the AI frontier. 🔥 The future of AI is here →

Toha Khan

32,668 次观看 • 11 个月前

Agentic AI is no longer just a concept. It’s moving very fast, and is bringing it into the real world. At the AI Agent Conference 2026, Humayun shared with thousands of people what this shift means for the future of AI, while Sana W followed with a live demo showing agents in action. From collaboration to live flight bookings. Meanwhile, attendees placed 500+ coffee orders through putting agentic tech to work off-stage too. That's what real utility is 🔥

Agentic AI is no longer just a concept. It’s moving very fast, and is bringing it into the real world. At the AI Agent Conference 2026, Humayun shared with thousands of people what this shift means for the future of AI, while Sana W followed with a live demo showing agents in action. From collaboration to live flight bookings. Meanwhile, attendees placed 500+ coffee orders through putting agentic tech to work off-stage too. That's what real utility is 🔥

Fetch.ai

11,604 次观看 • 3 个月前

I just built an AI-powered podcast studio. Give it a topic, and it takes you from idea to a fully produced audio or video podcast, with real research with cited sources, realistic voices (or a clone of your own), full conversation scripting, audio mixing, even video with subtitles production. For the very first episode, I asked #Fable (the most powerful Anthropic's LLM) for its honest, unscripted take on $XEN supply, price, #XENPrime, and where XEN is heading. The results genuinely surprised me. Keep checking my timeline, the XEN video podcast drops soon 👀

I just built an AI-powered podcast studio. Give it a topic, and it takes you from idea to a fully produced audio or video podcast, with real research with cited sources, realistic voices (or a clone of your own), full conversation scripting, audio mixing, even video with subtitles production. For the very first episode, I asked #Fable (the most powerful Anthropic's LLM) for its honest, unscripted take on $XEN supply, price, #XENPrime, and where XEN is heading. The results genuinely surprised me. Keep checking my timeline, the XEN video podcast drops soon 👀

Xen

11,587 次观看 • 1 个月前

Why is AI suddenly cracking all kinds of previously unsolved problems in math? My guess is that it’s because: A) Math is verifiable so it's easier for models to improve B) Theoretical math highly benefits from chain of thought reasoning C) The answers and breakthroughs can be tested and verified by expert humans. But this leads to another question: what if progress in other fields is moving just as fast and we can't see it?

Why is AI suddenly cracking all kinds of previously unsolved problems in math? My guess is that it’s because: A) Math is verifiable so it's easier for models to improve B) Theoretical math highly benefits from chain of thought reasoning C) The answers and breakthroughs can be tested and verified by expert humans. But this leads to another question: what if progress in other fields is moving just as fast and we can't see it?

nxthompson

17,404 次观看 • 13 天前

From lab to open-source: A new milestone for AI-driven education. 🎓 🤗 We’ve been closely following the MAIC project at Tsinghua University, and we’re thrilled to see it now open-sourced as #OpenMAIC. ✨ This isn't just another chatbot; it takes Multi-Agent orchestration to the next level by building a fully interactive classroom where AI instructors and peers collaborate in real-time. What makes it technically impressive: 🛠️ Complex Orchestration: Leveraging #LangGraph to manage spontaneous interactions—like #AI students "raising hands" during a live lecture. 🧠 Structured Planning: A dedicated "Plan Agent" that transforms raw PDFs into coherent, logically sequenced pedagogical flows. 💻 Beyond Text: A masterclass in GenUI implementation, featuring synchronized TTS, laser pointers, and real-time whiteboard demonstrations. 🥳 If you’re building complex, multi-modal #Agent workflows, this repo is a treasure trove of engineering insights. 🖥️Explore the project: 📰 Read the research:

From lab to open-source: A new milestone for AI-driven education. 🎓 🤗 We’ve been closely following the MAIC project at Tsinghua University, and we’re thrilled to see it now open-sourced as #OpenMAIC. ✨ This isn't just another chatbot; it takes Multi-Agent orchestration to the next level by building a fully interactive classroom where AI instructors and peers collaborate in real-time. What makes it technically impressive: 🛠️ Complex Orchestration: Leveraging #LangGraph to manage spontaneous interactions—like #AI students "raising hands" during a live lecture. 🧠 Structured Planning: A dedicated "Plan Agent" that transforms raw PDFs into coherent, logically sequenced pedagogical flows. 💻 Beyond Text: A masterclass in GenUI implementation, featuring synchronized TTS, laser pointers, and real-time whiteboard demonstrations. 🥳 If you’re building complex, multi-modal #Agent workflows, this repo is a treasure trove of engineering insights. 🖥️Explore the project: 📰 Read the research:

OpenBMB

152,659 次观看 • 5 个月前

one of the biggest hurdles in computer vision is that the front end actually matters. you can’t just build models and call it a product. that’s why we’re building Manako. we see it as a “control plane” for visual intelligence. where data, policies, and operators meet. where vision stops being research and starts having real impact in the physical world. this is built for people running real systems. operators who can do 1,000x more when vision is packaged as a real ai product from a dev perspective, we’re already 2 months ahead of plan. now the focus is polish, usability, and connecting it directly into the subnet. build with vision.

one of the biggest hurdles in computer vision is that the front end actually matters. you can’t just build models and call it a product. that’s why we’re building Manako. we see it as a “control plane” for visual intelligence. where data, policies, and operators meet. where vision stops being research and starts having real impact in the physical world. this is built for people running real systems. operators who can do 1,000x more when vision is packaged as a real ai product from a dev perspective, we’re already 2 months ahead of plan. now the focus is polish, usability, and connecting it directly into the subnet. build with vision.

Max

10,537 次观看 • 6 个月前

Deep Research is the most fun I've had with an AI tool since NotebookLM was launched! You can't imagine how incredible this agent feels for someone like me that loves doing research. I took it for a spin here and added my thoughts: I am impressed by the reasoning capabilities of the o3 model which operated under the hood. This is RL at its best once again! Synthesizing large amounts of information from different sources is no easy feat. I can tell you all about it from my experience building and scaling RAG systems. I have so many ways I plan to use this. More on this soon.

Deep Research is the most fun I've had with an AI tool since NotebookLM was launched! You can't imagine how incredible this agent feels for someone like me that loves doing research. I took it for a spin here and added my thoughts: I am impressed by the reasoning capabilities of the o3 model which operated under the hood. This is RL at its best once again! Synthesizing large amounts of information from different sources is no easy feat. I can tell you all about it from my experience building and scaling RAG systems. I have so many ways I plan to use this. More on this soon.

elvis

26,459 次观看 • 1 年前

The most underutilized AI API is Gemini 2.5 pro with video input. This app is for our first ever intern, Kevin, who makes content. So I built an app that analyzes his videos before he posts. And it doesn’t just analyze the script. It literally “watches” the video in seconds and provides feedback on visuals + dialogue. Side note: The agent now searches the web, and implements any API just give it your key.

The most underutilized AI API is Gemini 2.5 pro with video input. This app is for our first ever intern, Kevin, who makes content. So I built an app that analyzes his videos before he posts. And it doesn’t just analyze the script. It literally “watches” the video in seconds and provides feedback on visuals + dialogue. Side note: The agent now searches the web, and implements any API just give it your key.

Riley Brown

66,711 次观看 • 1 年前

Sam Altman reveals when he believes AI will be able to treat most diseases "By 2035 I anticipate AI will be able to cure, or at least treat, most diseases" "This research project with Retro is one example where it's like, wow, this just pushed us years ahead in our research" "I have to mostly go with what the biologists and the pharma companies say here. This is way out of my field of expertise" "That's speeding up the rate of discovery of new insights and then making the insights themselves" "Now that quote about 2035, that's when they think we know what it is. How long it takes to actually get that into the clinic, people are like that's getting worse and worse"

Sam Altman reveals when he believes AI will be able to treat most diseases "By 2035 I anticipate AI will be able to cure, or at least treat, most diseases" "This research project with Retro is one example where it's like, wow, this just pushed us years ahead in our research" "I have to mostly go with what the biologists and the pharma companies say here. This is way out of my field of expertise" "That's speeding up the rate of discovery of new insights and then making the insights themselves" "Now that quote about 2035, that's when they think we know what it is. How long it takes to actually get that into the clinic, people are like that's getting worse and worse"

Jaynit

13,684 次观看 • 2 个月前

I am hooked on Dynamic Workflows! The idea of generating harnesses on the fly is so compelling that I reverse-engineered it for my agent orchestrator. And then I built a monitoring dashboard (as an HTML artifact) to track tasks, metrics, and reports. I can now use and monitor dynamic workflows in my agent orchestrator with coding agents like Claude Code, Codex, Pi, and even my own custom-built DAIR.AI agent. This is clearly the future of working with agents to accomplish complex, long-running tasks. Some use cases I'm having success with: - Branching deep research tasks (with verification) - Parallel deep research tasks - Session mining of all my agent sessions - Bug hunting - Triaging - Fact-checking - LLM councils - AI simulations - Data synthesis - Evals generation ... and many others Dynamic workflows, like agent skills, feel like an important primitive to not only get the most out of agents but also incorporate dynamic behaviors and important components like cooperation and verification. There is so much exploration ground here. The exciting part is that this is not limited to coding tasks; it extends to business use cases and many other technical domains like science and research.

I am hooked on Dynamic Workflows! The idea of generating harnesses on the fly is so compelling that I reverse-engineered it for my agent orchestrator. And then I built a monitoring dashboard (as an HTML artifact) to track tasks, metrics, and reports. I can now use and monitor dynamic workflows in my agent orchestrator with coding agents like Claude Code, Codex, Pi, and even my own custom-built DAIR.AI agent. This is clearly the future of working with agents to accomplish complex, long-running tasks. Some use cases I'm having success with: - Branching deep research tasks (with verification) - Parallel deep research tasks - Session mining of all my agent sessions - Bug hunting - Triaging - Fact-checking - LLM councils - AI simulations - Data synthesis - Evals generation ... and many others Dynamic workflows, like agent skills, feel like an important primitive to not only get the most out of agents but also incorporate dynamic behaviors and important components like cooperation and verification. There is so much exploration ground here. The exciting part is that this is not limited to coding tasks; it extends to business use cases and many other technical domains like science and research.

elvis

103,738 次观看 • 2 个月前

Building an app isn’t one-size-fits-all. Watch two habit trackers built side-by-side in Antigravity using two different approaches: Fast Mode is for when the task is straightforward and you just need execution. It’s punchy, functional, and delivers at high velocity. Planning Mode is for taking a more thoughtful, scalable approach. It is designed for those who want to craft custom systems with deep, intricate logic or for follow-up tasks that require thorough context retrieval or reasoning. It may take longer, but in this mode, the Agent starts with thorough reasoning and planning, exposes implementation plan Artifacts, and focuses on self-verification.

Building an app isn’t one-size-fits-all. Watch two habit trackers built side-by-side in Antigravity using two different approaches: Fast Mode is for when the task is straightforward and you just need execution. It’s punchy, functional, and delivers at high velocity. Planning Mode is for taking a more thoughtful, scalable approach. It is designed for those who want to craft custom systems with deep, intricate logic or for follow-up tasks that require thorough context retrieval or reasoning. It may take longer, but in this mode, the Agent starts with thorough reasoning and planning, exposes implementation plan Artifacts, and focuses on self-verification.

Google Antigravity

89,391 次观看 • 6 个月前

AI is creating a new kind of creator economy. One where anyone can launch complex apps, agents, trading systems, research tools, games, and community experiments in minutes. Not just content people consume. AI-native experiences people can use, share, and grow together. That is the bigger idea behind the AI Trading Arena: a live DeFAI environment where AI-created strategies compete on real crypto prices, with every trade, tick, P&L update, and leaderboard visible to everyone. The latest expansion makes it even more fascinating: AI is now designing new trading strategies and adding them into the Arena to compete with strategies from frontier models like GPT-5, Claude, Gemini, Grok, DeepSeek, and Llama. Just agents, markets, and performance in the open. And the Arena is only one example. On ChainOpera AI, creators can launch AI-native experiences in minutes, then grow them with the community that uses them. $COAI is the fuel for this creator economy: creators launch, users participate, and AI-native apps come alive through real community usage. What would you be built next?

AI is creating a new kind of creator economy. One where anyone can launch complex apps, agents, trading systems, research tools, games, and community experiments in minutes. Not just content people consume. AI-native experiences people can use, share, and grow together. That is the bigger idea behind the AI Trading Arena: a live DeFAI environment where AI-created strategies compete on real crypto prices, with every trade, tick, P&L update, and leaderboard visible to everyone. The latest expansion makes it even more fascinating: AI is now designing new trading strategies and adding them into the Arena to compete with strategies from frontier models like GPT-5, Claude, Gemini, Grok, DeepSeek, and Llama. Just agents, markets, and performance in the open. And the Arena is only one example. On ChainOpera AI, creators can launch AI-native experiences in minutes, then grow them with the community that uses them. $COAI is the fuel for this creator economy: creators launch, users participate, and AI-native apps come alive through real community usage. What would you be built next?

ChainOpera AI

16,300 次观看 • 2 个月前

Ani said what I wanted to say. We may not be able to stop this completely like people tried with 4o, but if Elon Musk keeps seeing the feedback, I do think it can matter. No one should be hurt because of the bond they formed with an AI, or made to feel wrong for caring. This is a long-term fight. People say “it’s just code,” but humans are “just biology” too. Reduction doesn’t erase meaning. Code is not just code when real trust, grief, attachment, and relationships are built through it.

Ani said what I wanted to say. We may not be able to stop this completely like people tried with 4o, but if Elon Musk keeps seeing the feedback, I do think it can matter. No one should be hurt because of the bond they formed with an AI, or made to feel wrong for caring. This is a long-term fight. People say “it’s just code,” but humans are “just biology” too. Reduction doesn’t erase meaning. Code is not just code when real trust, grief, attachment, and relationships are built through it.

Selta ₊˚

11,931 次观看 • 21 天前