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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:show more

Andrew Ng

1,574,887 subscribers

128,085 görüntüleme • 1 yıl önce •via X (Twitter)

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Abu profil fotoğrafı

Abu1 yıl önce

that sounds like a fun combo! writing and coding together? count me in

Evinstein 𝕏 profil fotoğrafı

Evinstein 𝕏1 yıl önce

learning 🧠 thank you Andrew! 😁

AI Leaks and News profil fotoğrafı

AI Leaks and News1 yıl önce

Will definitely be taking this course

sitkarev profil fotoğrafı

sitkarev1 yıl önce

wow. how cool. A question, Professor: Any more Python courses? They are so awesome... you are the best

Data & Analytics profil fotoğrafı

Data & Analytics1 yıl önce

@AndrewYNg, looks like a fresh take on creativity! Blending writing and coding could spark some dope collaborations. Interested in what's covered in the course?

Mohammed Lubbad 🇵🇸 profil fotoğrafı

Mohammed Lubbad 🇵🇸1 yıl önce

The course on Collaborative Writing and Coding with OpenAI Canvas sounds like a valuable opportunity for enhancing skills in text and code development. What aspects are you most interested in exploring?

Vincent Valentine (CEO of UnOpen.ai) profil fotoğrafı

Vincent Valentine (CEO of UnOpen.ai)1 yıl önce

Exciting times ahead with this innovative course.

溪河 profil fotoğrafı

溪河1 yıl önce

useful

Cluster Protocol profil fotoğrafı

Cluster Protocol1 yıl önce

AI-powered workflows FTW! ⚡

Benzer Videolar

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

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

Karina

845,496 görüntüleme • 1 yıl önce

👋 Meet Canvas, a new interactive space in Gemini that simplifies creating, refining, and sharing your work. Canvas is like a shared whiteboard where you and Gemini can work together. You can refine documents, write code, design prototypes and more while Gemini collaborates with you in real-time to make improvements. Try Canvas today here: and share your creations with us in the comments!

👋 Meet Canvas, a new interactive space in Gemini that simplifies creating, refining, and sharing your work. Canvas is like a shared whiteboard where you and Gemini can work together. You can refine documents, write code, design prototypes and more while Gemini collaborates with you in real-time to make improvements. Try Canvas today here: and share your creations with us in the comments!

G3mini

63,795 görüntüleme • 1 yıl önce

CODE EXECUTION IN CANVAS🎨 Canvas becomes an automated SWE and a data scientist. Search for API docs, ask the model to write code and execute, get the plot!

CODE EXECUTION IN CANVAS🎨 Canvas becomes an automated SWE and a data scientist. Search for API docs, ask the model to write code and execute, get the plot!

Karina Nguyen

46,579 görüntüleme • 1 yıl önce

Canvas—a new way to work with ChatGPT to draft, edit, and get feedback on writing & code—is now available to all users in our 4o model. It’s fully rolled out on web and the ChatGPT desktop app for Windows.

Canvas—a new way to work with ChatGPT to draft, edit, and get feedback on writing & code—is now available to all users in our 4o model. It’s fully rolled out on web and the ChatGPT desktop app for Windows.

OpenAI

653,654 görüntüleme • 1 yıl önce

With Canvas in Gemini, you can: ⌨️ Write, iterate & preview React/HTML code 📝 Draft & edit comprehensive documents 🎨 Build interactive prototypes, games & visualization …and more. Simply select ‘Canvas’ in your prompt bar and you can write and edit documents or code, with changes appearing in real-time: Try it today and share your Canvas creations by tagging us here!

With Canvas in Gemini, you can: ⌨️ Write, iterate & preview React/HTML code 📝 Draft & edit comprehensive documents 🎨 Build interactive prototypes, games & visualization …and more. Simply select ‘Canvas’ in your prompt bar and you can write and edit documents or code, with changes appearing in real-time: Try it today and share your Canvas creations by tagging us here!

Google Gemini

414,938 görüntüleme • 1 yıl önce

When writing code, canvas makes it easier to track and understand ChatGPT’s changes. It can also review code, add logs and comments, fix bugs, and port to other coding languages like JavaScript and Python.

When writing code, canvas makes it easier to track and understand ChatGPT’s changes. It can also review code, add logs and comments, fix bugs, and port to other coding languages like JavaScript and Python.

OpenAI

275,824 görüntüleme • 1 yıl önce

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

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

Stitch by Google

486,830 görüntüleme • 2 ay önce

I'm teaching a new course! AI Python for Beginners is a series of four short courses that teach anyone to code, regardless of current technical skill. We are offering these courses free for a limited time. Generative AI is transforming coding. This course teaches coding in a way that’s aligned with where the field is going, rather than where it has been: (1) AI as a Coding Companion. Experienced coders are using AI to help write snippets of code, debug code, and the like. We embrace this approach and describe best-practices for coding with a chatbot. Throughout the course, you'll have access to an AI chatbot that will be your own coding companion that can assist you every step of the way as you code. (2) Learning by Building AI Applications. You'll write code that interacts with large language models to quickly create fun applications to customize poems, write recipes, and manage a to-do list. This hands-on approach helps you see how writing code that calls on powerful AI models will make you more effective in your work and personal projects. With this approach, beginning programmers can learn to do useful things with code far faster than they could have even a year ago. Knowing a little bit of coding is increasingly helping people in job roles other than software engineers. For example, I've seen a marketing professional write code to download web pages and use generative AI to derive insights; a reporter write code to flag important stories; and an investor automate the initial drafts of contracts. With this course you’ll be equipped to automate repetitive tasks, analyze data more efficiently, and leverage AI to enhance your productivity. If you are already an experienced developer, please help me spread the word and encourage your non-developer friends to learn a little bit of coding. I hope you'll check out the first two short courses here!

I'm teaching a new course! AI Python for Beginners is a series of four short courses that teach anyone to code, regardless of current technical skill. We are offering these courses free for a limited time. Generative AI is transforming coding. This course teaches coding in a way that’s aligned with where the field is going, rather than where it has been: (1) AI as a Coding Companion. Experienced coders are using AI to help write snippets of code, debug code, and the like. We embrace this approach and describe best-practices for coding with a chatbot. Throughout the course, you'll have access to an AI chatbot that will be your own coding companion that can assist you every step of the way as you code. (2) Learning by Building AI Applications. You'll write code that interacts with large language models to quickly create fun applications to customize poems, write recipes, and manage a to-do list. This hands-on approach helps you see how writing code that calls on powerful AI models will make you more effective in your work and personal projects. With this approach, beginning programmers can learn to do useful things with code far faster than they could have even a year ago. Knowing a little bit of coding is increasingly helping people in job roles other than software engineers. For example, I've seen a marketing professional write code to download web pages and use generative AI to derive insights; a reporter write code to flag important stories; and an investor automate the initial drafts of contracts. With this course you’ll be equipped to automate repetitive tasks, analyze data more efficiently, and leverage AI to enhance your productivity. If you are already an experienced developer, please help me spread the word and encourage your non-developer friends to learn a little bit of coding. I hope you'll check out the first two short courses here!

Andrew Ng

1,223,494 görüntüleme • 1 yıl önce

New short course: Building Code Agents with Hugging Face smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!

New short course: Building Code Agents with Hugging Face smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!

Andrew Ng

124,382 görüntüleme • 1 yıl önce

New short course: Vibe Coding 101 with Replit! Learn to build and host applications with an AI agent in this course, built in partnership with Replit ⠕ and taught by its President Michele Catasta and Head of Developer Relations . Coding agents are changing how we write code. "Vibe coding" refers to a growing practice where you might barely look at the generated code, and instead focus on the architecture and features of your application. However, contrary to popular belief, effectively coding this way isn't done by just prompting, accepting all recommendations, and hoping for the best. It requires structuring your work, refining your prompts, and having a systematic process that lead to a more efficient and effective workflow. I code frequently using LLMs, and asking an LLM to do everything in one shot usually does not work. I'll typically take a problem, partition it into manageable modules, spend time creating prompts to specify each module, and use the model to produce the code one module at a time, and test/debug each module before moving on. A process like this is making me and many other developers faster and more efficient. In this video-only course, you’ll learn how to use Replit’s cloud environment--with an integrated code editor, package manager, and deployment tools--to build and deploy web applications. Along the way, you’ll learn strategies for working effectively with agents and improve your development skills. In detail, you’ll: - Understand principles of agentic code development such as being precise, giving agents one task at a time, making prompts specific, keeping projects tidy, starting with fresh sessions for each new feature, and how to approach debugging. - Learn how to get started with Replit, and key skills for vibe coding: Thinking, using frameworks, checkpoints, debugging, and providing context. - Create a product requirement document (PRD) and wireframe for your agent to build a prototype of a website performance analyzer. - See how to use an agent to make your prototype more visually appealing, and deploy it application others to access . - Learn to build a head-to-head national park ranking app, from a sample dataset, with voting capabilities and persistent data storage, and refine further ask the assistant to recap and explain what it built to find room for improvement and reinforce your learning. By the end of this course, you’ll have a solid foundation in building with coding agents, and a process you can use to keep vibe coding effectively. Please sign up here:

New short course: Vibe Coding 101 with Replit! Learn to build and host applications with an AI agent in this course, built in partnership with Replit ⠕ and taught by its President Michele Catasta and Head of Developer Relations . Coding agents are changing how we write code. "Vibe coding" refers to a growing practice where you might barely look at the generated code, and instead focus on the architecture and features of your application. However, contrary to popular belief, effectively coding this way isn't done by just prompting, accepting all recommendations, and hoping for the best. It requires structuring your work, refining your prompts, and having a systematic process that lead to a more efficient and effective workflow. I code frequently using LLMs, and asking an LLM to do everything in one shot usually does not work. I'll typically take a problem, partition it into manageable modules, spend time creating prompts to specify each module, and use the model to produce the code one module at a time, and test/debug each module before moving on. A process like this is making me and many other developers faster and more efficient. In this video-only course, you’ll learn how to use Replit’s cloud environment--with an integrated code editor, package manager, and deployment tools--to build and deploy web applications. Along the way, you’ll learn strategies for working effectively with agents and improve your development skills. In detail, you’ll: - Understand principles of agentic code development such as being precise, giving agents one task at a time, making prompts specific, keeping projects tidy, starting with fresh sessions for each new feature, and how to approach debugging. - Learn how to get started with Replit, and key skills for vibe coding: Thinking, using frameworks, checkpoints, debugging, and providing context. - Create a product requirement document (PRD) and wireframe for your agent to build a prototype of a website performance analyzer. - See how to use an agent to make your prototype more visually appealing, and deploy it application others to access . - Learn to build a head-to-head national park ranking app, from a sample dataset, with voting capabilities and persistent data storage, and refine further ask the assistant to recap and explain what it built to find room for improvement and reinforce your learning. By the end of this course, you’ll have a solid foundation in building with coding agents, and a process you can use to keep vibe coding effectively. Please sign up here:

Andrew Ng

751,992 görüntüleme • 1 yıl önce

New course to bring you up to state-of-the-art at using AI to help you code: Build Apps with Windsurf's AI Coding Agents, built in partnership with WIndsurf (Codeium) and taught by Anshul Ramachandran! AI-assisted IDEs (Integrated Development Environments) make developers’ workflows faster, more efficient, and much more fun. Agentic tools like Windsurf are more than just code autocomplete—they are collaborative coding agents that help you break down complex applications, iterate efficiently, and generate code that spans multiple files. Although a lot of coding assistants share the same underlying large language models for planning and reasoning, a major point of distinction is how they handle tools, keep track of context, and stay aligned with your intent as a developer. For instance, if you make modifications to a class definition in your code and make the same modifications to other classes in the same directory, you might tell the AI agent "Do the same thing in similar places in this directory." Here, tracking your intent means understanding that “the same thing" refers to that recent edit you just made, which must be followed by appropriate search and tool-calling to implement the changes. In this course, you'll learn the inner workings of coding agents, their strengths and limitations, and how to use Windsurf to quickly build several applications. In detail, you'll: - Build a mental model of how agents work by combining human-action tracking, tool integration, and context awareness to carry out an agentic coding workflow. - Learn the challenges of code search and discovery and how a multi-step retrieval approach helps coding agents address them. - Use Windsurf to analyze and understand a large, old codebase and update it to the latest versions of the frameworks and packages it uses. - Build a Wikipedia data analysis app that retrieves, parses, and analyzes word frequencies. - Enhance the performance of your Wikipedia analysis app by adding caching, and through this, also learn how to course-correct when the AI agent produces unexpected results. - Learn tips and tricks such as keyboard shortcuts, autocomplete, and @ mentions to quickly call on agentic capabilities. - Use image/multimodal capabilities of the AI agent to increase your development velocity; you'll see an example of uploading a mockup with sketched-out UI features, and ask the agent to use that to build new functionality to an app. By the end of this course, you’ll understand agentic coding in-depth and know how to use it to make your development process much faster, more efficient, and enjoyable. Please sign up here!

New course to bring you up to state-of-the-art at using AI to help you code: Build Apps with Windsurf's AI Coding Agents, built in partnership with WIndsurf (Codeium) and taught by Anshul Ramachandran! AI-assisted IDEs (Integrated Development Environments) make developers’ workflows faster, more efficient, and much more fun. Agentic tools like Windsurf are more than just code autocomplete—they are collaborative coding agents that help you break down complex applications, iterate efficiently, and generate code that spans multiple files. Although a lot of coding assistants share the same underlying large language models for planning and reasoning, a major point of distinction is how they handle tools, keep track of context, and stay aligned with your intent as a developer. For instance, if you make modifications to a class definition in your code and make the same modifications to other classes in the same directory, you might tell the AI agent "Do the same thing in similar places in this directory." Here, tracking your intent means understanding that “the same thing" refers to that recent edit you just made, which must be followed by appropriate search and tool-calling to implement the changes. In this course, you'll learn the inner workings of coding agents, their strengths and limitations, and how to use Windsurf to quickly build several applications. In detail, you'll: - Build a mental model of how agents work by combining human-action tracking, tool integration, and context awareness to carry out an agentic coding workflow. - Learn the challenges of code search and discovery and how a multi-step retrieval approach helps coding agents address them. - Use Windsurf to analyze and understand a large, old codebase and update it to the latest versions of the frameworks and packages it uses. - Build a Wikipedia data analysis app that retrieves, parses, and analyzes word frequencies. - Enhance the performance of your Wikipedia analysis app by adding caching, and through this, also learn how to course-correct when the AI agent produces unexpected results. - Learn tips and tricks such as keyboard shortcuts, autocomplete, and @ mentions to quickly call on agentic capabilities. - Use image/multimodal capabilities of the AI agent to increase your development velocity; you'll see an example of uploading a mockup with sketched-out UI features, and ask the agent to use that to build new functionality to an app. By the end of this course, you’ll understand agentic coding in-depth and know how to use it to make your development process much faster, more efficient, and enjoyable. Please sign up here!

Andrew Ng

139,725 görüntüleme • 1 yıl önce

Introducing Higgsfield Canvas: a state-of-the-art image editing model. Paint products directly onto your image with pixel-perfect control. Say hi to your new go-to for product placement, editing, and layout! 👋🏻 Comment Canvas to get the full guide in the DM.

Introducing Higgsfield Canvas: a state-of-the-art image editing model. Paint products directly onto your image with pixel-perfect control. Say hi to your new go-to for product placement, editing, and layout! 👋🏻 Comment Canvas to get the full guide in the DM.

Higgsfield AI 🧩

2,627,093 görüntüleme • 11 ay önce

Zerve has been the best platform for writing data science code for a while. It's 100x better than Jupyter notebooks. Now, they have a specialized, fine-tuned AI assistant who knows all about data science code and how to use the platform. This is a killer feature. A generalist model will never be better than a specialized one. Watch the video. I promise you'll be jealous in 3 minutes. Some other features, in case you've never used Zerve: 1. It's web-based 2. You can share and work with your team on the same canvas 3. You can deploy your code with a click 4. Blocks are the center of attention. Promotes code modularity 5. You can combine multiple languages in a single canvas 6. You can run code blocks in parallel Zerve organizes your code as a DAG. Each block inherits the state of every block before it. Everything runs in a serverless architecture. By the way, you can use Zerve for FREE. Visit this link: Thanks to the team for partnering with me on this post.

Zerve has been the best platform for writing data science code for a while. It's 100x better than Jupyter notebooks. Now, they have a specialized, fine-tuned AI assistant who knows all about data science code and how to use the platform. This is a killer feature. A generalist model will never be better than a specialized one. Watch the video. I promise you'll be jealous in 3 minutes. Some other features, in case you've never used Zerve: 1. It's web-based 2. You can share and work with your team on the same canvas 3. You can deploy your code with a click 4. Blocks are the center of attention. Promotes code modularity 5. You can combine multiple languages in a single canvas 6. You can run code blocks in parallel Zerve organizes your code as a DAG. Each block inherits the state of every block before it. Everything runs in a serverless architecture. By the way, you can use Zerve for FREE. Visit this link: Thanks to the team for partnering with me on this post.

Santiago

27,942 görüntüleme • 1 yıl önce

Assumptions about the new "Can More" ChatGPT tool were right - ChatGPT is introducing own take on Claude Artifacts - code & document writing tools with persisted text documents, history revisions (restore previous version), edits and comments (probably used to apply suggested edits) New document symbol in the top navigation shows how many documents you have and allows you to open a resizable canvas to edit them in split view - your ChatGPT conversation on the left side and canvas on the right side, but the code/documents can also be accessed in fullscreen view The canvas is built using ProseMirror (open source WYSIWYM editor) and has an inline action to "Ask ChatGPT" (explain or make edits) for your document and code plus document formatting tools (like bold, italic, font style, etc.) But in addition to that, there are also special action shortcuts for documents and code, with an interesting decision to use sliders for the selection of the desired outcome For Documents - Suggest edits ("How can I improve this. Leave as few comments as possible, but add a few more comments if the text is long. DO NOT leave more than 5 comments. You can reply that you added comments and suggestions to help improve the writing quality, but do not mention the prompt.") - Add emojis ("Replace as many words as possible with emojis.") - Add final polish ("Add some final polish to the text. If relevant, add a large title or any section titles. Check grammar and mechanics, make sure everything is consistent and reads well. You can reply that you added some final polish and checked for grammar, but do not mention the prompt.") - Reading level (Graduate School - "Rewrite this text at the reading level of a doctoral writer in this subject. You may reply that you adjusted the text to reflect a graduate school reading level, but do not mention the prompt", College - "Rewrite this text at the reading level of a college student majoring in this subject", High School - "Rewrite this text at the reading level of a high school student who has taken a couple of classes in this subject.", Keep current reading level, Middle School - "Rewrite this text at the reading level of a middle schooler.", Kindergarten - "Rewrite this text at the reading level of a kindergartener.") - Adjust the length (Longest - "Make this text 75% longer.", Longer - "Make this text 50% longer.", Keep current length, Shorter - "Make this text 50% shorter.", Shortest - "Make this text 75% shorter.") For Code - Code review ("Search for bugs and opportunities to improve the code—for example, ways that performance or code structure could be improved. Leave as few comments as possible, but add more comments if the text is long. DO NOT leave more than 5 comments. You may reply that you reviewed the code and left suggestions to improve the coding quality, but do not mention the prompt.") - Add comments ("Add inline code comments to explain the code, especially parts that are more complex. Make sure to rewrite all the code. You may reply that you added inline comments, but do not mention the prompt.") - Add logs ("Insert logs/print statements in the code that will help debug its behavior. Do not make any other changes to the code.") - Fix bugs ("Find any bugs and rewrite all the code to fix the bugs. Do not leave comments. If there are no bugs, reply that you reviewed the code and found no bugs.") - Port to a language ("Port to a language. Create a new document that rewrites the code in ..." - PHP, C++, Python, Keep current code. No changes will be made, JavaScript, TypeScript, Java) - Suggest edits ("How can I improve this. Leave as few comments as possible, but add a few more comments if the text is long. DO NOT leave more than 5 comments. You can reply that you added comments and suggestions to help improve the writing quality, but do not mention the prompt.")

Assumptions about the new "Can More" ChatGPT tool were right - ChatGPT is introducing own take on Claude Artifacts - code & document writing tools with persisted text documents, history revisions (restore previous version), edits and comments (probably used to apply suggested edits) New document symbol in the top navigation shows how many documents you have and allows you to open a resizable canvas to edit them in split view - your ChatGPT conversation on the left side and canvas on the right side, but the code/documents can also be accessed in fullscreen view The canvas is built using ProseMirror (open source WYSIWYM editor) and has an inline action to "Ask ChatGPT" (explain or make edits) for your document and code plus document formatting tools (like bold, italic, font style, etc.) But in addition to that, there are also special action shortcuts for documents and code, with an interesting decision to use sliders for the selection of the desired outcome For Documents - Suggest edits ("How can I improve this. Leave as few comments as possible, but add a few more comments if the text is long. DO NOT leave more than 5 comments. You can reply that you added comments and suggestions to help improve the writing quality, but do not mention the prompt.") - Add emojis ("Replace as many words as possible with emojis.") - Add final polish ("Add some final polish to the text. If relevant, add a large title or any section titles. Check grammar and mechanics, make sure everything is consistent and reads well. You can reply that you added some final polish and checked for grammar, but do not mention the prompt.") - Reading level (Graduate School - "Rewrite this text at the reading level of a doctoral writer in this subject. You may reply that you adjusted the text to reflect a graduate school reading level, but do not mention the prompt", College - "Rewrite this text at the reading level of a college student majoring in this subject", High School - "Rewrite this text at the reading level of a high school student who has taken a couple of classes in this subject.", Keep current reading level, Middle School - "Rewrite this text at the reading level of a middle schooler.", Kindergarten - "Rewrite this text at the reading level of a kindergartener.") - Adjust the length (Longest - "Make this text 75% longer.", Longer - "Make this text 50% longer.", Keep current length, Shorter - "Make this text 50% shorter.", Shortest - "Make this text 75% shorter.") For Code - Code review ("Search for bugs and opportunities to improve the code—for example, ways that performance or code structure could be improved. Leave as few comments as possible, but add more comments if the text is long. DO NOT leave more than 5 comments. You may reply that you reviewed the code and left suggestions to improve the coding quality, but do not mention the prompt.") - Add comments ("Add inline code comments to explain the code, especially parts that are more complex. Make sure to rewrite all the code. You may reply that you added inline comments, but do not mention the prompt.") - Add logs ("Insert logs/print statements in the code that will help debug its behavior. Do not make any other changes to the code.") - Fix bugs ("Find any bugs and rewrite all the code to fix the bugs. Do not leave comments. If there are no bugs, reply that you reviewed the code and found no bugs.") - Port to a language ("Port to a language. Create a new document that rewrites the code in ..." - PHP, C++, Python, Keep current code. No changes will be made, JavaScript, TypeScript, Java) - Suggest edits ("How can I improve this. Leave as few comments as possible, but add a few more comments if the text is long. DO NOT leave more than 5 comments. You can reply that you added comments and suggestions to help improve the writing quality, but do not mention the prompt.")

Tibor Blaho

136,026 görüntüleme • 1 yıl önce

New short course: Long-Term Agentic Memory with LangGraph. Learn to build an agent with long-term memory in this course developed in collaboration with taught by its Co-Founder and CEO, Harrison Chase! Personal assistance and productivity tasks have become important use cases for agents. An important feature of an AI assistant, such as a coding or calendar assistant, is its ability to keep improving over time from its experience. Agent memory is the key capability that enables this. To add memory to an agent, you must first figure out what to store and what to retrieve when it is time to use the information. Additionally, you’ll have to decide when to update the stored information. For example, you might update in each iteration loop of the agent or perform updates in the background, with a helper agent. In this course, you will learn a mental framework to build agents with long-term memory. You'll create a useful email assistant that can respond, ignore, and notify using writing, scheduling, and memory-management tools. You’ll develop your agent's memory by adding facts to its memory store, provide examples to learn the user's preferences, and optimize system prompts to evolve instructions based on previous responses. In detail, you’ll: - Learn how the three types of memory--semantic, episodic, and procedural–and the two update mechanisms–via hot path and in the background–apply to your agents. - Build an email agent with writing, scheduling, and availability tools, along with a router that triages incoming email and handles it accordingly by ignoring, responding, or notifying the user. - Add tools to your email agent that allow it to operate on semantic memory by learning facts about the user, storing them in a long-term memory store, and searching over them in future interactions. - Incorporate episodic memory, in the form of few-shot examples, in the triage step of your agents to help them learn and update user preferences. - Add procedural memory as system prompts, optimized with feedback to improve the instructions the agent follows. Learn how to approach memory in agents, and start building agents with long-term memory with LangGraph! Please sign up here:

New short course: Long-Term Agentic Memory with LangGraph. Learn to build an agent with long-term memory in this course developed in collaboration with taught by its Co-Founder and CEO, Harrison Chase! Personal assistance and productivity tasks have become important use cases for agents. An important feature of an AI assistant, such as a coding or calendar assistant, is its ability to keep improving over time from its experience. Agent memory is the key capability that enables this. To add memory to an agent, you must first figure out what to store and what to retrieve when it is time to use the information. Additionally, you’ll have to decide when to update the stored information. For example, you might update in each iteration loop of the agent or perform updates in the background, with a helper agent. In this course, you will learn a mental framework to build agents with long-term memory. You'll create a useful email assistant that can respond, ignore, and notify using writing, scheduling, and memory-management tools. You’ll develop your agent's memory by adding facts to its memory store, provide examples to learn the user's preferences, and optimize system prompts to evolve instructions based on previous responses. In detail, you’ll: - Learn how the three types of memory--semantic, episodic, and procedural–and the two update mechanisms–via hot path and in the background–apply to your agents. - Build an email agent with writing, scheduling, and availability tools, along with a router that triages incoming email and handles it accordingly by ignoring, responding, or notifying the user. - Add tools to your email agent that allow it to operate on semantic memory by learning facts about the user, storing them in a long-term memory store, and searching over them in future interactions. - Incorporate episodic memory, in the form of few-shot examples, in the triage step of your agents to help them learn and update user preferences. - Add procedural memory as system prompts, optimized with feedback to improve the instructions the agent follows. Learn how to approach memory in agents, and start building agents with long-term memory with LangGraph! Please sign up here:

Andrew Ng

131,607 görüntüleme • 1 yıl önce

New short course: Prompt Engineering with Llama 2, built in collaboration with Meta AI at Meta, and taught by Amit Sangani! Meta's Llama 2 has been game-changing for AI. Building with open source lets you control your own data, scrutinize errors, update (or not) the models as you please, and work alongside the global community advancing open models. Llama isn't a single model, it's a collection of models. In this course, you'll: - Learn the differences between different Llama 2 flavors, and when to use each. - Prompt the Llama chat models -- you'll also see how Llama's instruction tags work -- so they can help you with day-to-day tasks, like writing or summarization. - Use advanced prompting, like few-shot prompting for classification, and chain-of-thought prompting for solving logic problems. - Use specialized models in the Llama collection for specific tasks, like Code Llama to help you write, analyze, and improve code, and Llama Guard, which checks prompts and model responses for harmful content. The course also touches on how to run Llama 2 locally on your own computer. I hope you’ll take this course and try out these powerful, open models!

New short course: Prompt Engineering with Llama 2, built in collaboration with Meta AI at Meta, and taught by Amit Sangani! Meta's Llama 2 has been game-changing for AI. Building with open source lets you control your own data, scrutinize errors, update (or not) the models as you please, and work alongside the global community advancing open models. Llama isn't a single model, it's a collection of models. In this course, you'll: - Learn the differences between different Llama 2 flavors, and when to use each. - Prompt the Llama chat models -- you'll also see how Llama's instruction tags work -- so they can help you with day-to-day tasks, like writing or summarization. - Use advanced prompting, like few-shot prompting for classification, and chain-of-thought prompting for solving logic problems. - Use specialized models in the Llama collection for specific tasks, like Code Llama to help you write, analyze, and improve code, and Llama Guard, which checks prompts and model responses for harmful content. The course also touches on how to run Llama 2 locally on your own computer. I hope you’ll take this course and try out these powerful, open models!

Andrew Ng

162,798 görüntüleme • 2 yıl önce

New short course: Serverless Agentic Workflows with Amazon Bedrock. Learn to build and deploy serverless agents in this course created with Amazon Web Services and taught by Mike G Chambers, a Senior Developer Advocate at AWS specializing in GenAI. (Disclosure: I serve on Amazon's board.) Generative AI applications are becoming more complex, sophisticated, and agentic. Agentic applications have workloads that can be hard to predict in advance -- for example, what tools will it decide to call? -- and a serverless architecture helps you efficiently providing on-demand resources. This course teaches you to build and deploy a serverless agentic application. You’ll learn to create agents with tools, code execution, and guardrails, and build responsible agents for business use cases: - Build a customer service bot for a fictional tea mug business that can answering questions, retrieve information, and process orders. - Connect your customer service agent to a CRM to get customer info and log support tickets in real-time. - Explore how you invoke the agent, and see the trace to review the agent’s thought process and observation loop until it reaches its final output. - Attach a code interpreter to your agent, giving it the ability to perform accurate calculations by writing and running its own Python code. - Implement guardrails to prevent your agent from revealing sensitive information or using inappropriate language. By the end, you will have built a sophisticated AI agent capable of handling real-world customer support scenarios. Please sign up here!

New short course: Serverless Agentic Workflows with Amazon Bedrock. Learn to build and deploy serverless agents in this course created with Amazon Web Services and taught by Mike G Chambers, a Senior Developer Advocate at AWS specializing in GenAI. (Disclosure: I serve on Amazon's board.) Generative AI applications are becoming more complex, sophisticated, and agentic. Agentic applications have workloads that can be hard to predict in advance -- for example, what tools will it decide to call? -- and a serverless architecture helps you efficiently providing on-demand resources. This course teaches you to build and deploy a serverless agentic application. You’ll learn to create agents with tools, code execution, and guardrails, and build responsible agents for business use cases: - Build a customer service bot for a fictional tea mug business that can answering questions, retrieve information, and process orders. - Connect your customer service agent to a CRM to get customer info and log support tickets in real-time. - Explore how you invoke the agent, and see the trace to review the agent’s thought process and observation loop until it reaches its final output. - Attach a code interpreter to your agent, giving it the ability to perform accurate calculations by writing and running its own Python code. - Implement guardrails to prevent your agent from revealing sensitive information or using inappropriate language. By the end, you will have built a sophisticated AI agent capable of handling real-world customer support scenarios. Please sign up here!

Andrew Ng

80,949 görüntüleme • 1 yıl önce

A new short course, Claude Code: A Highly Agentic Coding Assistant, is live! Claude Code is currently one of the most capable coding assistants. It can explore your codebase, plan features, write tests, refactor code, and even collaborate across multiple sessions—with surprisingly minimal input. In this course, you’ll learn how to guide Claude Code effectively: from setting up context and memory to integrating with GitHub and MCP servers. You’ll use it to extend a RAG chatbot, refactor a Jupyter notebook for e-commerce data analysis, build a web app from a Figma design, and more. Taught by Elie Schoppik (Elie Schoppik) and built in collaboration with Anthropic, this course is a must for AI builders. 👉 Enroll now:

A new short course, Claude Code: A Highly Agentic Coding Assistant, is live! Claude Code is currently one of the most capable coding assistants. It can explore your codebase, plan features, write tests, refactor code, and even collaborate across multiple sessions—with surprisingly minimal input. In this course, you’ll learn how to guide Claude Code effectively: from setting up context and memory to integrating with GitHub and MCP servers. You’ll use it to extend a RAG chatbot, refactor a Jupyter notebook for e-commerce data analysis, build a web app from a Figma design, and more. Taught by Elie Schoppik (Elie Schoppik) and built in collaboration with Anthropic, this course is a must for AI builders. 👉 Enroll now:

DeepLearning.AI

32,513 görüntüleme • 10 ay önce

Introducing canvas—your coding surface in ChatGPT. ✏️ Edit code inline 🐛 Review code and fix bugs 💬 Add logs and comments 🚢 Port to different languages We’ll be adding more to canvas over time. ChatGPT Plus and Team users can try the beta starting today.

Introducing canvas—your coding surface in ChatGPT. ✏️ Edit code inline 🐛 Review code and fix bugs 💬 Add logs and comments 🚢 Port to different languages We’ll be adding more to canvas over time. ChatGPT Plus and Team users can try the beta starting today.

OpenAI Developers

934,395 görüntüleme • 1 yıl önce