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。

Many people still remember when ManusAI was released. It was astonishing to see what AI agents are already capable of today, and its in-depth research was impressive. But what else can ManusAI actually do? Unlike traditional chatbots, Manus can independently plan, execute, and optimize complex, multi-step tasks from start... to finish—without any ongoing user intervention. In short: Manus AI is an autonomous AI agent that can independently research, plan, analyze, design, and program—without you having to manually accompany every step. For example: • Create market analyses and generate structured reports from online data • Evaluate Excel or Google Sheets and derive data-driven recommendations • Automatically design websites, fill them with content, and publish them online • Write applications, emails, or social media posts in the desired style • Write Python code, test it, and use it to solve technical tasks Below is a brief overview of the user interface. Prompt used: Create a presentation with 5 slides on the state of the AI industry in 2025. Focus: investments, areas of application, risks, regulation, outlook, everything in englishshow more

Chubby♨️

125,156 subscribers

51,219 次观看 • 1 年前 •via X (Twitter)

科学技术新闻政治教育

Anya Rossi• Live Now

Private livecam show

11 条评论

Boris Cergol 的头像

Boris Cergol1 年前

@ManusAI_HQ Manus is impressive, but it lacks grounding. Can produce very complex results but give it a simple form to fill out and the result is quite underwhelming.

Places Visited & Pictures Taken 的头像

Places Visited & Pictures Taken2 年前

Wondering what happens when AI is used? Here’s the answer 🙂

Ilyas Salaoui 的头像

Ilyas Salaoui1 年前

@ManusAI_HQ Impressive — ManusAI is more than a chatbot; it’s a full-stack autonomous coworker. From market intel to live websites, it closes the loop without hand-holding. The big question now: how do we scale guardrails when one prompt can spin up code, content, and insights?

JC Gilbert 的头像

JC Gilbert1 年前

@ManusAI_HQ imo manus is still one of the best ai products in the market the ux is just unparalleled imo, no manual error handling

RiskDataScience 的头像

RiskDataScience1 年前

@ManusAI_HQ It can reverse-engineer (simple) SaaS solutions when prompted appropriately. 🤯

akim 的头像

akim1 年前

@ManusAI_HQ ai agent is future No multi ai agent is future

Eryn Littel 的头像

Eryn Littel1 年前

@ManusAI_HQ Manus isn't just dialogue. It plans independently. This was the dream years ago, now unfolding. Truly profound.

Cengiz 的头像

Cengiz1 年前

@ManusAI_HQ manus is a fantastic AI product, shame they have completely fallen out of the common consciousness

{Author} 的头像

{Author}1 年前

@ManusAI_HQ

Johannes Miertschischk 的头像

Johannes Miertschischk1 年前

Have you ever considered why Large Language Models (LLMs) are increasingly being promoted as 'AI agents'? Artificial personal assistants who can do all kinds of annoying tasks for you at any time. Tax returns, insurance stuff, official business... All this and much more will be handled by your individually tailored personal AI assistant. Even independently and without your permission! The only prerequisite to be able to handle all sorts of things on your behalf is total access to your digital devices. The best thing to do is simply entrust it with your entire life. Unlike your unreliable friends, it will never forget even the smallest detail. And since you also talk to your AI friend on a daily basis about all sorts of topics, it can practically read your mind and anticipate your every wish! Isn't this a wonderful, brave, new world?

Akshay Bapat 的头像

Akshay Bapat1 年前

@ManusAI_HQ @ManusAI_HQ is gpt 5

相关视频

Today we're launching Amplitude MCP MCP was made for data analytics. Ask a vague question and have an AI model iteratively query Amplitude to find you insights. Amplitude MCP exposes all of Amplitude's functionality so an agent can interact with it directly. It allows you to use Amplitude without having to know anything about Amplitude's user interface or your data taxonomy. It uses the intelligence of off the shelf AI agents, including Claude, Gemini, and Cursor, to query Amplitude data you already have to find answers. It leverages all of your existing Amplitude data and how it's used to navigate through large complex behavioral datasets. You can have conversations about your product data directly inside any AI model with MCP. It can create Amplitude dashboards and notebooks with what it finds and even make recommendations. This is the best MCP use case I've ever seen. Watching an AI agent think, reason, query Amplitude, and then repeat that process iteratively is magical. See it in action:

Today we're launching Amplitude MCP MCP was made for data analytics. Ask a vague question and have an AI model iteratively query Amplitude to find you insights. Amplitude MCP exposes all of Amplitude's functionality so an agent can interact with it directly. It allows you to use Amplitude without having to know anything about Amplitude's user interface or your data taxonomy. It uses the intelligence of off the shelf AI agents, including Claude, Gemini, and Cursor, to query Amplitude data you already have to find answers. It leverages all of your existing Amplitude data and how it's used to navigate through large complex behavioral datasets. You can have conversations about your product data directly inside any AI model with MCP. It can create Amplitude dashboards and notebooks with what it finds and even make recommendations. This is the best MCP use case I've ever seen. Watching an AI agent think, reason, query Amplitude, and then repeat that process iteratively is magical. See it in action:

Spenser Skates

32,332 次观看 • 10 个月前

You can create an AI Agent that answers your email with a few clicks. 1. Go to ChatLLM ( 2. Click on AI Engineer 3. Select Create an AI Agent 4. Choose the Email Answering Agent ChatLLM will do the rest: it will code, test, and deploy the agent for you. You can also create a custom agent in English. The Agent Economy is coming (somebody should write a book and use this title.) We are going to see examples like this, times 1,000 in 2025. Just think about how many repetitive tasks you perform every day. Some of these tasks are involved enough that we couldn't automate them with pre-AI solutions. That's where we'll see agents explode, and I'm here for it.

You can create an AI Agent that answers your email with a few clicks. 1. Go to ChatLLM ( 2. Click on AI Engineer 3. Select Create an AI Agent 4. Choose the Email Answering Agent ChatLLM will do the rest: it will code, test, and deploy the agent for you. You can also create a custom agent in English. The Agent Economy is coming (somebody should write a book and use this title.) We are going to see examples like this, times 1,000 in 2025. Just think about how many repetitive tasks you perform every day. Some of these tasks are involved enough that we couldn't automate them with pre-AI solutions. That's where we'll see agents explode, and I'm here for it.

Santiago

79,974 次观看 • 1 年前

"What is an AI Agent and why do they matter?" An agent is a program that autonomously completes tasks or makes decisions based on data. What do I mean by autonomous? The agent understands task intent, can plan steps to solve the problem, decide and execute and actions and adapt to the environment. Consider how many of us use AI chat interfaces today. You might ask ChatGPT to write an article from start to finish and get a one-shot response. You probably need to do some work to iterate on it yourself. An agentic version is more nuanced - it might write an outline, decide if research is needed, write a draft, evaluate if it needs work and revise itself. Unlike traditional AI models that simply respond to queries, agents are designed to be autonomous and proactive. Think of them as assistants that can not only understand what you need but also take initiative to accomplish tasks by using various tools and making decisions along the way. For example, an AI agent might help a marketing team by not just analyzing campaign data, but actively monitoring performance, adjusting budget allocations, and even drafting social media posts based on real-time engagement metrics. The significance of AI agents lies in their potential to transform how we work. In customer service, agents can handle complex inquiries by accessing multiple databases, processing payments, and updating records - all while maintaining natural conversations with customers. In software development, they can assist programmers by not just suggesting code but actively debugging issues, writing test cases, and even refactoring entire codebases. This level of autonomy and capability represents a fundamental shift from AI as a tool to AI as a collaborative partner. While there remain many unknowns, I'm excited about the potential for agents and we're thinking about how they can help users and developers on the web over in Chrome. The key to success will likely be finding the right balance between human oversight and agent autonomy, ensuring that these powerful tools enhance rather than diminish the human element in business operations.

"What is an AI Agent and why do they matter?" An agent is a program that autonomously completes tasks or makes decisions based on data. What do I mean by autonomous? The agent understands task intent, can plan steps to solve the problem, decide and execute and actions and adapt to the environment. Consider how many of us use AI chat interfaces today. You might ask ChatGPT to write an article from start to finish and get a one-shot response. You probably need to do some work to iterate on it yourself. An agentic version is more nuanced - it might write an outline, decide if research is needed, write a draft, evaluate if it needs work and revise itself. Unlike traditional AI models that simply respond to queries, agents are designed to be autonomous and proactive. Think of them as assistants that can not only understand what you need but also take initiative to accomplish tasks by using various tools and making decisions along the way. For example, an AI agent might help a marketing team by not just analyzing campaign data, but actively monitoring performance, adjusting budget allocations, and even drafting social media posts based on real-time engagement metrics. The significance of AI agents lies in their potential to transform how we work. In customer service, agents can handle complex inquiries by accessing multiple databases, processing payments, and updating records - all while maintaining natural conversations with customers. In software development, they can assist programmers by not just suggesting code but actively debugging issues, writing test cases, and even refactoring entire codebases. This level of autonomy and capability represents a fundamental shift from AI as a tool to AI as a collaborative partner. While there remain many unknowns, I'm excited about the potential for agents and we're thinking about how they can help users and developers on the web over in Chrome. The key to success will likely be finding the right balance between human oversight and agent autonomy, ensuring that these powerful tools enhance rather than diminish the human element in business operations.

Addy Osmani

30,412 次观看 • 1 年前

Usable AI agents are finally here. From OpenAI’s DeepResearch to xAI’s DeepSearch, we’re seeing the first real push toward autonomous tools that can plan, execute, and complete tasks like research, outreach, and coding with minimal human input. Recently, a new entrant joined the competition — Manus, a general-purpose AI agent that’s getting global attention. Some are calling it the “most sophisticated computer using AI,” while others write it off as just another “wrapper.” So what sets Manus apart under the hood? What does its launch say about the broader debate around GPT “wrappers”? YC’s Garry Tan dives into Manus’ “chain-of-thought injection” technique that enables agents to actively update plans, what works particularly well with its UI, and how founders can create enduring value with application-level products.

Usable AI agents are finally here. From OpenAI’s DeepResearch to xAI’s DeepSearch, we’re seeing the first real push toward autonomous tools that can plan, execute, and complete tasks like research, outreach, and coding with minimal human input. Recently, a new entrant joined the competition — Manus, a general-purpose AI agent that’s getting global attention. Some are calling it the “most sophisticated computer using AI,” while others write it off as just another “wrapper.” So what sets Manus apart under the hood? What does its launch say about the broader debate around GPT “wrappers”? YC’s Garry Tan dives into Manus’ “chain-of-thought injection” technique that enables agents to actively update plans, what works particularly well with its UI, and how founders can create enduring value with application-level products.

Y Combinator

203,451 次观看 • 1 年前

New Short Course: Building AI Browser Agents! Learn how to build AI agents that interact and take actions on websites in this course, created in partnership with and taught by and @namangarg0, Co-founders of AGI Inc. AI browser agents can log into websites, fill out forms, click through web pages, or even place orders online for you. They use both visual information, like screenshots, and structural data, like the HTML or Document Object Model (DOM) of a web page, to reason and take action. With the complexity of webpages and multiple possible actions at each step, it can be challenging for an AI browser agent to complete an assigned task. Because these agents run long action sequences, a single error—like clicking the wrong button or misreading a field—can lead to unexpected outcomes or errors that compound over time. In this course, you'll understand how autonomous web agents work, their current limitations, and how AgentQ enables them to improve through self-correction. In detail, you'll: - Learn what web agents are, how they automate tasks online, their architecture, key components, limitations, and an overview of their decision-making strategies. - Build a web agent that can scrape website and return course recommendations in a structured output format. - Build an autonomous web agent that can execute multiple tasks, such as finding and summarizing webpages, filling out a form, and signing up for a newsletter. - Explore AgentQ, a framework that enables agents to self-correct by combining Monte Carlo Tree Search (MCTS), a self-critique mechanism for continuous improvement, and Direct Preference Optimization (DPO). - Deep dive into MCTS, learn how it finds an effective path, illustrated by an example of Gridworld animation, and use AgentQ to complete web tasks. - Understand AI agents' current state and future directions—including key factors shaping their evolution, such as hardware, algorithm innovation, and data availability. By the end of this course, you will have hands-on experience building browser agents and a deeper understanding of how to make them more robust and reliable. Please sign up here:

New Short Course: Building AI Browser Agents! Learn how to build AI agents that interact and take actions on websites in this course, created in partnership with and taught by and @namangarg0, Co-founders of AGI Inc. AI browser agents can log into websites, fill out forms, click through web pages, or even place orders online for you. They use both visual information, like screenshots, and structural data, like the HTML or Document Object Model (DOM) of a web page, to reason and take action. With the complexity of webpages and multiple possible actions at each step, it can be challenging for an AI browser agent to complete an assigned task. Because these agents run long action sequences, a single error—like clicking the wrong button or misreading a field—can lead to unexpected outcomes or errors that compound over time. In this course, you'll understand how autonomous web agents work, their current limitations, and how AgentQ enables them to improve through self-correction. In detail, you'll: - Learn what web agents are, how they automate tasks online, their architecture, key components, limitations, and an overview of their decision-making strategies. - Build a web agent that can scrape website and return course recommendations in a structured output format. - Build an autonomous web agent that can execute multiple tasks, such as finding and summarizing webpages, filling out a form, and signing up for a newsletter. - Explore AgentQ, a framework that enables agents to self-correct by combining Monte Carlo Tree Search (MCTS), a self-critique mechanism for continuous improvement, and Direct Preference Optimization (DPO). - Deep dive into MCTS, learn how it finds an effective path, illustrated by an example of Gridworld animation, and use AgentQ to complete web tasks. - Understand AI agents' current state and future directions—including key factors shaping their evolution, such as hardware, algorithm innovation, and data availability. By the end of this course, you will have hands-on experience building browser agents and a deeper understanding of how to make them more robust and reliable. Please sign up here:

Andrew Ng

186,182 次观看 • 1 年前

Nobody cares about chatbots anymore. You want AI agents that can reason, plan, collaborate, and solve tasks. That's what modern AI is all about. At AI Agents Conference 2026, ASI:One was trending because people used their agent to order their coffee. People asked for their orders in natural language, and the agent handled the rest, processing more than 1,000 orders during the conference without a hitch. This is how it works behind the scenes:

Nobody cares about chatbots anymore. You want AI agents that can reason, plan, collaborate, and solve tasks. That's what modern AI is all about. At AI Agents Conference 2026, ASI:One was trending because people used their agent to order their coffee. People asked for their orders in natural language, and the agent handled the rest, processing more than 1,000 orders during the conference without a hitch. This is how it works behind the scenes:

Santiago

17,867 次观看 • 3 个月前

AutoGPT might be the next big step in AI. Here's why Karpathy recently said "AutoGPT is the next frontier of prompt engineering" AutoGPT is the equivalent of giving GPT-based models a memory and a body. You can now give a task to an AI agent and have it autonomously come up with a plan, execute on it, browse the web, and use new data to revise the strategy until the task is completed. It can analyze the market and come up with a trading strategy, customer service, marketing, finance, or other tasks that requires continuous updates. There are three components to it: 1. Architecture: It leverages GPT-4 and GPT-3.5 via API. 2. Autonomous Iterations: AutoGPT can refine its outputs by self-critical review, building on its previous work and integrating prompt history for more accurate results. 3. Memory Management: Integration with Pinecone allows for long-term memory storage, enabling context preservation and improved decision-making. 4. Multi-functionality: Capabilities include file manipulation, web browsing, and data retrieval, distinguishing AutoGPT from previous AI advancements by broadening its application scope.

AutoGPT might be the next big step in AI. Here's why Karpathy recently said "AutoGPT is the next frontier of prompt engineering" AutoGPT is the equivalent of giving GPT-based models a memory and a body. You can now give a task to an AI agent and have it autonomously come up with a plan, execute on it, browse the web, and use new data to revise the strategy until the task is completed. It can analyze the market and come up with a trading strategy, customer service, marketing, finance, or other tasks that requires continuous updates. There are three components to it: 1. Architecture: It leverages GPT-4 and GPT-3.5 via API. 2. Autonomous Iterations: AutoGPT can refine its outputs by self-critical review, building on its previous work and integrating prompt history for more accurate results. 3. Memory Management: Integration with Pinecone allows for long-term memory storage, enabling context preservation and improved decision-making. 4. Multi-functionality: Capabilities include file manipulation, web browsing, and data retrieval, distinguishing AutoGPT from previous AI advancements by broadening its application scope.

Lior Alexander

808,131 次观看 • 3 年前

The future of automation at the Department of War 🇺🇸 has arrived. We are excited to announce the launch of Agent Designer, now available in first AI instance, Google Google Gemini. All 3 million Department of War personnel now have the power to create their own custom AI assistants to automate tasks and streamline complex workflows, with absolutely no coding experience needed. These custom agents can perform multi-step tasks, ingest various data sources, and be shared with teams for immediate deployment. Now, operators can automatically generate after-action reports, analysts can synthesize CUI images into memos, and comptrollers can build apps to analyze financial data. From logistics to strategy, Agent Designer empowers you to build the tools needed for increased efficiency. Several pre-built agents are already available in Gemini to address common workflows. An “AI-first” Department of War empowers the warfighter to address bigger problems, rather than being burdened by tedious tasks.

The future of automation at the Department of War 🇺🇸 has arrived. We are excited to announce the launch of Agent Designer, now available in first AI instance, Google Google Gemini. All 3 million Department of War personnel now have the power to create their own custom AI assistants to automate tasks and streamline complex workflows, with absolutely no coding experience needed. These custom agents can perform multi-step tasks, ingest various data sources, and be shared with teams for immediate deployment. Now, operators can automatically generate after-action reports, analysts can synthesize CUI images into memos, and comptrollers can build apps to analyze financial data. From logistics to strategy, Agent Designer empowers you to build the tools needed for increased efficiency. Several pre-built agents are already available in Gemini to address common workflows. An “AI-first” Department of War empowers the warfighter to address bigger problems, rather than being burdened by tedious tasks.

Department of War CTO

14,523 次观看 • 5 个月前

"Vibe coding is NOT the same as using AI to write code that's reviewed, tested, and maintained." Here's my new episode with Lee Robinson (Cursor's Head of AI Education) where he shows how to build AI apps properly using planning, test-driven development, agent workflows, and more. Some tips from Lee: 1. Work with AI to create a detailed technical plan upfront including requirements, design preferences, and your preferred tech stack. 2. Ask AI to write tests so that it can check its own work. Add this to the plan: "Write unit tests for business logic, e2e tests for core user journeys." 3. Add "make descriptive commits" to your initial plan so that you can track what AI changed and why. 📌 Watch our beginner-friendly tutorial now: Also available on: Spotify: Apple: Newsletter:

"Vibe coding is NOT the same as using AI to write code that's reviewed, tested, and maintained." Here's my new episode with Lee Robinson (Cursor's Head of AI Education) where he shows how to build AI apps properly using planning, test-driven development, agent workflows, and more. Some tips from Lee: 1. Work with AI to create a detailed technical plan upfront including requirements, design preferences, and your preferred tech stack. 2. Ask AI to write tests so that it can check its own work. Add this to the plan: "Write unit tests for business logic, e2e tests for core user journeys." 3. Add "make descriptive commits" to your initial plan so that you can track what AI changed and why. 📌 Watch our beginner-friendly tutorial now: Also available on: Spotify: Apple: Newsletter:

Peter Yang

108,418 次观看 • 1 年前

1/ ChatGPT is great, but increasingly more competitors are entering the market - and they have features that make OpenAI and Anthropic look somewhat outdated. Genspark is Silicon Valley’s first AI agent unicorn - 30 people, $1B valuation in 6 months - and it’s built as an all-in-one AI workspace where work actually gets done for you. Now with 3 new amazing features: • AI Inbox – talk to your email in text or voice, auto-summaries + replies, scheduled AI agents • Teams – shared projects where your team + AI co-edit slides/docs/sheets/designs • AI Sheets 2.0 – fully-automated, Excel-native data superpowers. In this example, I used Slides to create an overview of NVIDIA's earnings. It's incredible how detailed and with how many graphs it was presented!

1/ ChatGPT is great, but increasingly more competitors are entering the market - and they have features that make OpenAI and Anthropic look somewhat outdated. Genspark is Silicon Valley’s first AI agent unicorn - 30 people, $1B valuation in 6 months - and it’s built as an all-in-one AI workspace where work actually gets done for you. Now with 3 new amazing features: • AI Inbox – talk to your email in text or voice, auto-summaries + replies, scheduled AI agents • Teams – shared projects where your team + AI co-edit slides/docs/sheets/designs • AI Sheets 2.0 – fully-automated, Excel-native data superpowers. In this example, I used Slides to create an overview of NVIDIA's earnings. It's incredible how detailed and with how many graphs it was presented!

Chubby♨️

48,864 次观看 • 9 个月前

.carried_no_interest says Manus is one of the most underrated AI products today, and that it was a genius acquisition by Zuck: “There are simply things that you can do with Manus that no other inference provider or product can do. I would reveal them, but that’s alpha for me.” “What Zuck realized is — 'I don’t have to care about inference or who’s giving it to me, because [Manus] figured out some really special stuff around how an LLM processes data in and around a web browser.'” “And now he’s already integrating it with Ads Manager. It’s a great bet.”

.carried_no_interest says Manus is one of the most underrated AI products today, and that it was a genius acquisition by Zuck: “There are simply things that you can do with Manus that no other inference provider or product can do. I would reveal them, but that’s alpha for me.” “What Zuck realized is — 'I don’t have to care about inference or who’s giving it to me, because [Manus] figured out some really special stuff around how an LLM processes data in and around a web browser.'” “And now he’s already integrating it with Ads Manager. It’s a great bet.”

TBPN

37,131 次观看 • 5 个月前

This is the best AI tool I have seen so far this year, and everyone can use it. Normally, when you research a topic using tools like Perplexity, Gemini, or ChatGPT, they just give you a wall of text. Try Superagent, a newly launched product by Airtable. It literally builds you an interactive Super Report with high-quality data from a single prompt, ready to present as-is. You can click into research sections, scroll through dynamic graphs, and see visualizations that make complex topics easy to understand. 👉 Use code KUNAL2FREE and get free 2 month access: It’s not just a chatbot. It spins up a team of multiple specialized AI agents that build a research plan, divide the tasks, and execute them simultaneously. You can build presentations, university reports, websites, dashboards, you name it! Shoutout to them for sponsoring this post.

This is the best AI tool I have seen so far this year, and everyone can use it. Normally, when you research a topic using tools like Perplexity, Gemini, or ChatGPT, they just give you a wall of text. Try Superagent, a newly launched product by Airtable. It literally builds you an interactive Super Report with high-quality data from a single prompt, ready to present as-is. You can click into research sections, scroll through dynamic graphs, and see visualizations that make complex topics easy to understand. 👉 Use code KUNAL2FREE and get free 2 month access: It’s not just a chatbot. It spins up a team of multiple specialized AI agents that build a research plan, divide the tasks, and execute them simultaneously. You can build presentations, university reports, websites, dashboards, you name it! Shoutout to them for sponsoring this post.

Kunal Kushwaha

15,150 次观看 • 7 个月前

New short course: Collaborative Writing and Coding with OpenAI Canvas! Explore new ways to write and code with OpenAI Canvas, a user-friendly interface that allows you to brainstorm, draft, and refine text and code in collaboration with ChatGPT. In the short course, created with OpenAI, and taught by , a research lead at OpenAI, you’ll learn to use Canvas to enhance your workflows. Canvas lets you go beyond simple chat interactions. It provides a side-by-side workspace where you and ChatGPT can edit and refine text or code collaboratively. This makes brainstorming, drafting, and iterating as you write feel more natural and effective. As the first major update to ChatGPT’s visual interface since its launch in 2022, Canvas gives a new, innovative approach to collaboration with AI. For instance, after writing the first version of your code, Canvas can review it and give suggestions for improvement. It can also help with debugging by adding logging, identifying problems to fix, and writing comments. In addition, you'll also learn what it takes to train the model for an interface like Canvas. In this video-only short course, you’ll: - Learn how to ask for in-line feedback and control the iteration of your work by directly editing selected areas of your text or code from the model’s output. - Learn how to access quick automation tools in a shortcut menu that allows you to modify your writing tone and length, enhance your code, and restore previous versions of your work. - Learn how to use Canvas as a research assistant tool with an example of asking the model to reason through the screenshot of a plot to write a research report, in which you can ask questions within the created report. - Ask the model to write Python code to replicate the graph seen on a screenshot image. - Go behind the scenes of how you can create a video game, such as Space Battleship, from scratch, edit it, and display it in one self-contained HTML file. - Get a real-world application example of creating a SQL database from the image of its architecture. - Understand the model training and design processes that power Canvas! Please sign up here:

New short course: Collaborative Writing and Coding with OpenAI Canvas! Explore new ways to write and code with OpenAI Canvas, a user-friendly interface that allows you to brainstorm, draft, and refine text and code in collaboration with ChatGPT. In the short course, created with OpenAI, and taught by , a research lead at OpenAI, you’ll learn to use Canvas to enhance your workflows. Canvas lets you go beyond simple chat interactions. It provides a side-by-side workspace where you and ChatGPT can edit and refine text or code collaboratively. This makes brainstorming, drafting, and iterating as you write feel more natural and effective. As the first major update to ChatGPT’s visual interface since its launch in 2022, Canvas gives a new, innovative approach to collaboration with AI. For instance, after writing the first version of your code, Canvas can review it and give suggestions for improvement. It can also help with debugging by adding logging, identifying problems to fix, and writing comments. In addition, you'll also learn what it takes to train the model for an interface like Canvas. In this video-only short course, you’ll: - Learn how to ask for in-line feedback and control the iteration of your work by directly editing selected areas of your text or code from the model’s output. - Learn how to access quick automation tools in a shortcut menu that allows you to modify your writing tone and length, enhance your code, and restore previous versions of your work. - Learn how to use Canvas as a research assistant tool with an example of asking the model to reason through the screenshot of a plot to write a research report, in which you can ask questions within the created report. - Ask the model to write Python code to replicate the graph seen on a screenshot image. - Go behind the scenes of how you can create a video game, such as Space Battleship, from scratch, edit it, and display it in one self-contained HTML file. - Get a real-world application example of creating a SQL database from the image of its architecture. - Understand the model training and design processes that power Canvas! Please sign up here:

Andrew Ng

128,180 次观看 • 1 年前

New agentic short course! Multi AI Agent Systems with crewAI, built with CrewAI's founder and CEO João Moura. In this course, you'll learn how to break down complex tasks into subtasks for multiple AI agents, each playing a specialized role, to execute. For example, to generate a research report, you might have researcher, writer, and quality assurance agents collaborate. You'll define the roles, expectations, and interactions between the agents—like a manager organizing a team. You'll work with key agentic AI techniques like role-playing, tool use, memory, guardrails, and cross-agent collaboration. And you'll build your own multi-agent systems that can tackle complex tasks. I think you'll find it both productive and fun to design agents and watch them collaborate to get things done. Let me know what you think! I believe multi-agent architectures will drive significant progress in AI systems. Please sign up here!

New agentic short course! Multi AI Agent Systems with crewAI, built with CrewAI's founder and CEO João Moura. In this course, you'll learn how to break down complex tasks into subtasks for multiple AI agents, each playing a specialized role, to execute. For example, to generate a research report, you might have researcher, writer, and quality assurance agents collaborate. You'll define the roles, expectations, and interactions between the agents—like a manager organizing a team. You'll work with key agentic AI techniques like role-playing, tool use, memory, guardrails, and cross-agent collaboration. And you'll build your own multi-agent systems that can tackle complex tasks. I think you'll find it both productive and fun to design agents and watch them collaborate to get things done. Let me know what you think! I believe multi-agent architectures will drive significant progress in AI systems. Please sign up here!

Andrew Ng

350,175 次观看 • 2 年前

I launched Grok Bot for 24 hours, gave it 3 jobs, and stopped treating AI like a chatbot. I had it scan live X + web data for emerging AI products, research what people were actually saying about them, filter the obvious engagement bait, compare sources and turn the strongest signals into a structured brief. At the same time, I could have separate research paths looking at competitors, code or market opportunities instead of forcing everything through one conversation. That’s the difference I underestimated. I don’t want to sit inside a chat writing another prompt every 5 minutes. I want to define the objective, give the agent access to the right tools and data, let it execute, then step back in when judgment or approval is actually needed. The old workflow was prompt → answer → copy → repeat. The workflow I’m moving toward is assign → execute → verify. Once AI stops waiting for your next message, it stops feeling like software you use and starts feeling like work you delegated.

I launched Grok Bot for 24 hours, gave it 3 jobs, and stopped treating AI like a chatbot. I had it scan live X + web data for emerging AI products, research what people were actually saying about them, filter the obvious engagement bait, compare sources and turn the strongest signals into a structured brief. At the same time, I could have separate research paths looking at competitors, code or market opportunities instead of forcing everything through one conversation. That’s the difference I underestimated. I don’t want to sit inside a chat writing another prompt every 5 minutes. I want to define the objective, give the agent access to the right tools and data, let it execute, then step back in when judgment or approval is actually needed. The old workflow was prompt → answer → copy → repeat. The workflow I’m moving toward is assign → execute → verify. Once AI stops waiting for your next message, it stops feeling like software you use and starts feeling like work you delegated.

Lummox

3,875,565 次观看 • 8 天前

Announcing my new course: Agentic AI! Building AI agents is one of the most in-demand skills in the job market. This course, available now at teaches you how. You'll learn to implement four key agentic design patterns: - Reflection, in which an agent examines its own output and figures out how to improve it - Tool use, in which an LLM-driven application decides which functions to call to carry out web search, access calendars, send email, write code, etc. - Planning, where you'll use an LLM to decide how to break down a task into sub-tasks for execution, and - Multi-agent collaboration, in which you build multiple specialized agents — much like how a company might hire multiple employees — to perform a complex task You'll also learn to take a complex application and systematically decompose it into a sequence of tasks to implement using these design patterns. But here's what I think is the most important part of this course: Having worked with many teams on AI agents, I've found that the single biggest predictor of whether someone executes well is their ability to drive a disciplined process for evals and error analysis. In this course, you'll learn how to do this, so you can efficiently home in on which components to improve in a complex agentic workflow. Instead of guessing what to work on, you'll let evals data guide you. This will put you significantly ahead of the game compared to the vast majority of teams building agents. Together, we'll build a deep research agent that searches, synthesizes, and reports, using all of these agentic design patterns and best practices. This self-paced course is taught in a vendor neutral way, using raw Python - without hiding details in a framework. You'll see how each step works, and learn the core concepts that you can then implement using any popular agentic AI framework, or using no framework. The only prerequisite is familiarity with Python, though knowing a bit about LLMs helps. Come join me, and let's build some agentic AI systems! Sign up to get started:

Announcing my new course: Agentic AI! Building AI agents is one of the most in-demand skills in the job market. This course, available now at teaches you how. You'll learn to implement four key agentic design patterns: - Reflection, in which an agent examines its own output and figures out how to improve it - Tool use, in which an LLM-driven application decides which functions to call to carry out web search, access calendars, send email, write code, etc. - Planning, where you'll use an LLM to decide how to break down a task into sub-tasks for execution, and - Multi-agent collaboration, in which you build multiple specialized agents — much like how a company might hire multiple employees — to perform a complex task You'll also learn to take a complex application and systematically decompose it into a sequence of tasks to implement using these design patterns. But here's what I think is the most important part of this course: Having worked with many teams on AI agents, I've found that the single biggest predictor of whether someone executes well is their ability to drive a disciplined process for evals and error analysis. In this course, you'll learn how to do this, so you can efficiently home in on which components to improve in a complex agentic workflow. Instead of guessing what to work on, you'll let evals data guide you. This will put you significantly ahead of the game compared to the vast majority of teams building agents. Together, we'll build a deep research agent that searches, synthesizes, and reports, using all of these agentic design patterns and best practices. This self-paced course is taught in a vendor neutral way, using raw Python - without hiding details in a framework. You'll see how each step works, and learn the core concepts that you can then implement using any popular agentic AI framework, or using no framework. The only prerequisite is familiarity with Python, though knowing a bit about LLMs helps. Come join me, and let's build some agentic AI systems! Sign up to get started:

Andrew Ng

888,888 次观看 • 10 个月前

🚀 Today, we are thrilled to share that Amazon Q is now generally available. ◼Amazon Q is the most capable generative AI-powered assistant for accelerating software development and leveraging companies’ internal data. It eliminates tedious work for developers and employees across organizations. ◼Q helps to test, debug and write code, and has the highest reported code acceptance rates in the industry, for assistants that perform multi-line code suggestions. BT Group recently reported they accepted 37% of Q’s code suggestions and National Australia Bank reported a 50% acceptance rate. ◼Amazon Q Developer Agents can autonomously perform a range of tasks—everything from implementing features, documenting, and refactoring code, to performing software upgrades. Developers can ask Amazon Q to implement an application feature, and the agent will analyze their existing application code and generate a step-by-step implementation plan. ◼Amazon Q's capabilities extend beyond coding. It also allows employees to easily get insight from their company's internal data that is spread across multiple documents, systems, and applications. Q connects all these siloed sources and can answer questions, provide summaries, analyze trends, and generate content. ◼We're also introducing Q Apps, a powerful new way for anyone to create generative AI apps based on their organization's data—no prior coding experience required. Simply describe the app you need in natural language, and Q Apps will build it for you. This unlocks endless possibilities for teams to automate workflows and daily tasks. Customers across industries are using Amazon Q to transform the way they work. I'm incredibly excited to see what Q can do for you.

🚀 Today, we are thrilled to share that Amazon Q is now generally available. ◼Amazon Q is the most capable generative AI-powered assistant for accelerating software development and leveraging companies’ internal data. It eliminates tedious work for developers and employees across organizations. ◼Q helps to test, debug and write code, and has the highest reported code acceptance rates in the industry, for assistants that perform multi-line code suggestions. BT Group recently reported they accepted 37% of Q’s code suggestions and National Australia Bank reported a 50% acceptance rate. ◼Amazon Q Developer Agents can autonomously perform a range of tasks—everything from implementing features, documenting, and refactoring code, to performing software upgrades. Developers can ask Amazon Q to implement an application feature, and the agent will analyze their existing application code and generate a step-by-step implementation plan. ◼Amazon Q's capabilities extend beyond coding. It also allows employees to easily get insight from their company's internal data that is spread across multiple documents, systems, and applications. Q connects all these siloed sources and can answer questions, provide summaries, analyze trends, and generate content. ◼We're also introducing Q Apps, a powerful new way for anyone to create generative AI apps based on their organization's data—no prior coding experience required. Simply describe the app you need in natural language, and Q Apps will build it for you. This unlocks endless possibilities for teams to automate workflows and daily tasks. Customers across industries are using Amazon Q to transform the way they work. I'm incredibly excited to see what Q can do for you.

Adam Selipsky

121,755 次观看 • 2 年前

Today, Box is announcing major new AI agent capabilities to let customers tap into the full value of their unstructured data. First, we’re announcing all new updates to the Box AI Studio to make it even easier to build AI agents that tap into your enterprise content for any job function, business process, or industry specific use case. We are also expanding our set of foundational agents that customers will be able to use to work with their enterprise content, including new features like search and research on unstructured data. Next, we’re announcing Box Extract to enable customers to use AI agents seamlessly for complex data extraction from any type of document or content. This makes it easier than ever to pull out data from contracts, invoices, research data, marketing assets, medical charts, and more. Finally, we’re introducing Box Automate, a new workflow automation solution within Box that lets you deploy AI agents across enterprise content-centric workflows. With Box Automate, you can design your business process in a simple drag and drop builder and then drop in AI agents at any step in the process. This ensures agents execute tasks at the right steps in a workflow every time. Best of all, our AI agents and workflow tools are designed to work across any system our customers work within, whether it’s leveraging pre-built integrations, Box APIs, or the new Box MCP Server. Ultimately, all of these capabilities come together to transform how companies can work with their enterprise content. Software has historically only been good at automating work that deals with structured data, which is why ERP, CRM, and HR systems have been mainstays of enterprise software for so long. The data in these systems fits neatly into a database, and the workflows are very ripe for automation. But it turns out most of the work in the world deals with unstructured data. It’s ideating through research documents, working with a client on contracts, reviewing details for a new product launch, looking at a patient’s healthcare record to make a diagnosis, working through due diligence documents for an M&A deal, and so on. For the first time ever, we can begin to bring all new insights and automation to this work with AI agents. At Box, we’re incredibly excited to be on this journey to help customers transform how they work with their most important data.

Today, Box is announcing major new AI agent capabilities to let customers tap into the full value of their unstructured data. First, we’re announcing all new updates to the Box AI Studio to make it even easier to build AI agents that tap into your enterprise content for any job function, business process, or industry specific use case. We are also expanding our set of foundational agents that customers will be able to use to work with their enterprise content, including new features like search and research on unstructured data. Next, we’re announcing Box Extract to enable customers to use AI agents seamlessly for complex data extraction from any type of document or content. This makes it easier than ever to pull out data from contracts, invoices, research data, marketing assets, medical charts, and more. Finally, we’re introducing Box Automate, a new workflow automation solution within Box that lets you deploy AI agents across enterprise content-centric workflows. With Box Automate, you can design your business process in a simple drag and drop builder and then drop in AI agents at any step in the process. This ensures agents execute tasks at the right steps in a workflow every time. Best of all, our AI agents and workflow tools are designed to work across any system our customers work within, whether it’s leveraging pre-built integrations, Box APIs, or the new Box MCP Server. Ultimately, all of these capabilities come together to transform how companies can work with their enterprise content. Software has historically only been good at automating work that deals with structured data, which is why ERP, CRM, and HR systems have been mainstays of enterprise software for so long. The data in these systems fits neatly into a database, and the workflows are very ripe for automation. But it turns out most of the work in the world deals with unstructured data. It’s ideating through research documents, working with a client on contracts, reviewing details for a new product launch, looking at a patient’s healthcare record to make a diagnosis, working through due diligence documents for an M&A deal, and so on. For the first time ever, we can begin to bring all new insights and automation to this work with AI agents. At Box, we’re incredibly excited to be on this journey to help customers transform how they work with their most important data.

Aaron Levie

91,863 次观看 • 11 个月前

David Friedberg: “Gaming is the future of entertainment, and the future of gaming is AI.” @jason: “Friedberg, what are your thoughts on the gaming industry versus social media versus traditional media?” david friedberg: “One way to answer that question is to think about how people spend their time.” “Do you spend more minutes on social media, or on traditional media, or playing games? And how is that trending?” “But importantly, which of those will accrue more benefit, and as a result, drive more hours spent from AI?” “One way to think about this thesis is that AI is going to ultimately accrue to video game entertainment far more than social media entertainment or traditional content.” “If you believe in AI, and you believe in the improvements in productivity, generally speaking, people in the industrialized world will generally have more free time on their hands and be able to support themselves with the deflationary effects of AI over time.” “So if there's more time on people's hands, the general market for entertainment is growing, and if the general market for entertainment is growing, gaming is the future of entertainment, and the future of gaming is AI.” “Because I think you can create dynamic, more engaging experiences that will benefit from a back and forth sort of relationship than you can with traditional content or with social media.” “If you're a noob in Fortnite, like you're an early player in Fortnite, you're mostly playing against AI, because what they do is they tune the AI to be easier to beat so that you can slowly develop your skills.” “What was happening early was they were seeing a high degree of churn in Fortnite because kids would go on and play for the first time and they'd get paired up with kids that were better than them, and so they would never win, and they would get frustrated and they would quit the game and stop.” “So the churn rate was high. So AI unlocked higher engagement and higher retention, and I think we're seeing that in a lot of different gaming platforms now.” “So AI can be used, for example, to maximally increase time, engagement, satisfaction, happiness.”

David Friedberg: “Gaming is the future of entertainment, and the future of gaming is AI.” @jason: “Friedberg, what are your thoughts on the gaming industry versus social media versus traditional media?” david friedberg: “One way to answer that question is to think about how people spend their time.” “Do you spend more minutes on social media, or on traditional media, or playing games? And how is that trending?” “But importantly, which of those will accrue more benefit, and as a result, drive more hours spent from AI?” “One way to think about this thesis is that AI is going to ultimately accrue to video game entertainment far more than social media entertainment or traditional content.” “If you believe in AI, and you believe in the improvements in productivity, generally speaking, people in the industrialized world will generally have more free time on their hands and be able to support themselves with the deflationary effects of AI over time.” “So if there's more time on people's hands, the general market for entertainment is growing, and if the general market for entertainment is growing, gaming is the future of entertainment, and the future of gaming is AI.” “Because I think you can create dynamic, more engaging experiences that will benefit from a back and forth sort of relationship than you can with traditional content or with social media.” “If you're a noob in Fortnite, like you're an early player in Fortnite, you're mostly playing against AI, because what they do is they tune the AI to be easier to beat so that you can slowly develop your skills.” “What was happening early was they were seeing a high degree of churn in Fortnite because kids would go on and play for the first time and they'd get paired up with kids that were better than them, and so they would never win, and they would get frustrated and they would quit the game and stop.” “So the churn rate was high. So AI unlocked higher engagement and higher retention, and I think we're seeing that in a lot of different gaming platforms now.” “So AI can be used, for example, to maximally increase time, engagement, satisfaction, happiness.”

The All-In Podcast

70,365 次观看 • 10 个月前