Cursor - The BEST AI Tool for Developers!
TLDRIn this 'Coding with AI' episode, Vishwas demonstrates the integration of AI into his daily coding routine using Builder.io's AI assistant and Cloud Sonet 3.5. He showcases the process of enhancing the AI assistant's capabilities to analyze images, utilizing Cursor, an AI-integrated IDE. Through a series of steps, including adding an image attachment feature and modifying the server-side API, he successfully implements image recognition in the assistant, illustrating the power of AI to accelerate development and boost productivity.
Takeaways
- ๐ Vishwas introduces the second episode of 'Coding with AI', focusing on integrating AI into daily coding workflows to enhance productivity.
- ๐ ๏ธ The video showcases an AI assistant tool from Builder.io that works with Cloud Sonet 3.5, initially supporting text queries but aiming to include image content analysis.
- ๐ A to-do list is presented, outlining the steps needed to implement new vision capabilities for the AI to understand and analyze images.
- ๐ง The first step involves adding an image attach button within the text area, which is achieved with the help of Cursor, an AI-integrated IDE.
- ๐ผ๏ธ Subsequent steps include handling image objects, showing image previews, modifying the submit handler to accommodate images, and updating the server-side API.
- ๐ Cursor's AI generates code snippets based on user prompts, demonstrating the tool's ability to speed up development tasks.
- ๐จโ๐ป The process of implementing the image attach feature is detailed, including handling typescript errors and adjusting UI components.
- ๐ Debugging steps are shown, highlighting the iterative nature of development when integrating new features.
- ๐ The video emphasizes the efficiency gains from using AI in coding, claiming the task could take a day or two without AI but was completed in an hour.
- ๐ The presenter encourages viewers to leverage AI in their development workflows, showing how it can assist rather than replace human developers.
- ๐ The video concludes with a successful MVP demonstration of the new image analysis feature, marking the completion of the to-do list.
Q & A
What is the main focus of the video by Vishwas?
-The main focus of the video is to demonstrate how Vishwas uses AI in his daily workflow to accelerate his coding process and boost productivity, specifically by implementing new vision capabilities in an AI assistant that works with Cloud Sonet 3.5.
What is the relationship between Builder.io and the AI assistant discussed in the video?
-The AI assistant discussed in the video is one of Builder.io's internal tools, which is designed to work with Cloud Sonet 3.5 and help users with various queries, including text and image content analysis.
What is the purpose of the 'Cursor' tool mentioned in the video?
-Cursor is an IDE built from the ground up with AI integration in mind. It is essentially a fork of Visual Studio Code with powerful AI features built in, which Vishwas uses to accelerate the task of implementing new vision capabilities.
What are the steps outlined in the video for implementing new vision capabilities in the AI assistant?
-The steps outlined are: 1) Adding an image attach button within the text area, 2) Handling the image object in the specified format, 3) Showing a preview of the image with a close button, 4) Modifying the current submit handler in the codebase to accommodate an image, and 5) Modifying the server-side API that makes the actual request to Anthropic.
How does the AI assistant handle image content before the new vision capabilities were implemented?
-Before the new vision capabilities were implemented, the AI assistant could only handle text content. It would provide a text response to queries, but it did not have the capability to understand or analyze images.
What is the significance of the image attach button in the text area component?
-The image attach button is significant as it allows users to attach an image, which the AI assistant can then analyze. This is a new feature that Vishwas is implementing to expand the assistant's capabilities beyond text-based queries.
What is the role of the 'image content block' in the AI assistant's functionality?
-The 'image content block' is a part of the user message object that corresponds to an image type. It contains the source and properties of the image, allowing the AI assistant to process and analyze the image content.
How does the video demonstrate the use of AI for coding tasks?
-The video demonstrates the use of AI for coding tasks by showing how Vishwas leverages the Cursor IDE and the AI assistant to generate code snippets, handle image attachments, and integrate new features into the AI assistant's capabilities.
What is the importance of modifying the server-side API in the context of the video?
-Modifying the server-side API is important because it allows the AI assistant to make requests to Anthropic with both text and image content. This is crucial for the new vision capabilities that enable the assistant to analyze images.
What challenges did Vishwas encounter during the implementation of the new vision capabilities?
-Vishwas encountered challenges such as TypeScript errors, incorrect import statements, and issues with rendering content in the chat message component. He also had to debug and correct the implementation of the API changes to ensure the new vision capabilities worked correctly.
How does the video conclude regarding the implementation of the new vision capabilities?
-The video concludes with the successful implementation of the new vision capabilities. Vishwas demonstrates that the AI assistant can now analyze images as part of the input, marking the completion of the feature and showcasing the productivity benefits of using AI in the development workflow.
Outlines
๐ค Introduction to AI in Coding Workflow
Vishwas introduces the second episode of 'Coding with AI,' where he plans to demonstrate the integration of AI into his daily coding routine to enhance productivity. The focus is on utilizing Builder.io's AI assistant in tandem with Cloud Sonet 3.5 for handling text and image queries. The goal is to implement new capabilities for the AI assistant to analyze images, a more complex task than the previous episode. The tool 'Cursor', an AI-integrated IDE, is highlighted as the development environment. A to-do list is established to outline the steps needed for the integration, including adding an image attach button, handling image objects, previewing images, modifying the submit handler, and updating the server-side API.
๐ ๏ธ Implementing Image Attachment Feature
The paragraph details the process of adding an image attachment button to the text area within the Cursor IDE. Vishwas uses the chat assistant to generate the necessary code by providing detailed prompts. The AI successfully adds the button, handles image attachments, and updates the UI to show a preview and a close button for the image. However, some TypeScript errors related to icon imports are encountered and corrected. The implementation includes updating the submit handler to accommodate images and modifying the server-side API to handle image content.
๐ Enhancing Image Preview and Removal Functionality
Vishwas proceeds to enhance the image upload feature by adding a preview of the uploaded image and an 'X' button to remove it. He uses the AI chat assistant to generate the required code snippets, which include updating the state to store the image preview URL and adding a new function to handle image removal. After addressing TypeScript errors related to icon imports, the functionality is tested and confirmed to work as expected, with the image preview displayed above the text area and the removal functionality performing correctly.
๐ Adjusting Code to Handle Optional Image Attachments
The focus shifts to modifying the existing codebase to handle optional image attachments. This involves updating the 'onPromptSubmit' function to accept an additional argument for the image attachment and modifying the 'userPromptToMessage' function to accommodate the new content structure. The process includes updating type definitions and handling the logic to check for both text prompts and image attachments, ensuring the content is correctly formatted for the Cloud API.
๐ง Backend API Modifications for Image Support
Vishwas outlines the necessary changes to the backend API to support image content in messages sent to the 'anthropic' service. The 'postUserMessage' function is identified for updates, including modifying the mapping function to handle both string and array content types. The AI-generated code is reviewed and adjusted to ensure URLs are expanded correctly for text content and that image content is included as part of the message payload.
๐ผ๏ธ Finalizing Image Integration and Testing
The final steps involve testing the image integration feature by uploading an image and requesting analysis from the AI. After encountering and resolving an error related to the rendering of content in the 'chatMessage' component, the feature is successfully demonstrated. The AI provides accurate descriptions of the images, confirming that the vision integration works as intended. Vishwas concludes by reflecting on the productivity gains made possible by AI and encourages viewers to leverage AI in their development workflows.
Mindmap
Keywords
๐กAI
๐กCloud Sonet
๐กCursor
๐กImage attachment
๐กBase64
๐กAPI
๐กAnthropic
๐กTypeScript
๐กReact
๐กMVP
Highlights
Introduction to using AI in the daily coding workflow to accelerate the coding process and boost productivity.
Showcasing Builder.io's internal AI assistant integrated with Cloud Sonet 3.5 for handling queries with text and image content.
The goal of implementing New Vision capabilities to allow the assistant to understand and analyze images.
Utilizing Cursor, an AI-integrated IDE, to streamline the task of adding image understanding features.
Creating a to-do list for implementing image attachment functionality within the text area.
Adding an image attach button to the text area using Cursor's AI code generation.
Handling image objects in the specified format and updating the user message object structure.
Implementing a preview of the uploaded image with a close button to remove it.
Modifying the submit handler in the codebase to accommodate images in addition to text content.
Updating the server-side API to make requests to Anthropic with image content.
Rendering the image as part of the response to demonstrate the successful integration of image analysis.
Debugging and resolving TypeScript errors related to icon imports and component placements.
Refining the image preview size and user experience based on feedback from the implementation.
Updating the 'onPromptSubmit' function to handle optional image attachments.
Adjusting the 'userPromptToMessage' function to accept image attachments and forwarding them to the API layer.
Defining new types for text and image content to accommodate changes in message structure.
Fixing type errors and updating the 'userMessage' type to include the new 'CloudMessageContent' type.
Modifying the API endpoint to correctly process image content for Anthropic instead of OpenAI.
Testing the new image analysis feature and successfully receiving responses from the AI.
Completing the MVP for the image support feature and reflecting on the increased productivity with AI assistance.
