Is Cursor's Copilot++ BETTER than Github Copilot? FAST AI Coding Master Class
TLDRThe video explores Cursor's AI coding assistant, Copilot++, comparing it to Github Copilot. It demonstrates the ability to run multiple prompts simultaneously and highlights Cursor's advanced autocomplete features, which complete multiple lines of code contextually. The assistant also shows how to integrate reference documents for coding guidance and updates graph edges with step styles and colors. The video emphasizes the importance of leveraging AI to elevate one's role in the development stack, focusing on high-level logic manipulation and rapid implementation.
Takeaways
- 🚀 Cursor's Copilot++ is a new AI coding assistant that aims to provide more advanced features compared to Github Copilot.
- 🔍 It offers in-line auto-completion that is aware of more context and can complete multiple lines without the cursor being in the exact position.
- ✂️ The assistant can remove comments and add new features like buttons with aligned items through prompts.
- 🔄 Copilot++ is capable of running multiple prompts simultaneously, allowing for efficient coding.
- 🛠️ It provides auto-completion for function calls and can generate code for adding and removing nodes in a graph.
- 📐 The assistant can help in updating the position of nodes and edges, and even auto-generate functions based on the context.
- 📚 Reference documents can be added to enhance the assistant's understanding and provide more accurate code suggestions.
- 🌈 It can help in styling and theming elements, such as changing edge colors and making step edges in a graph.
- 🔄 The iterative control feature allows users to correct mistakes made by the assistant, ensuring precision in code generation.
- 📈 The use of AI coding assistants can help engineers uplevel their skills, focusing more on high-level logic and decision-making rather than individual lines of code.
- 🔑 The key to utilizing AI coding assistants effectively is to enhance engineering abilities and stay relevant in a competitive job market.
Q & A
What is the main topic of the video script?
-The main topic of the video script is comparing Cursor's Copilot++ with Github Copilot and demonstrating how to use AI coding assistance for tasks such as auto-completion and code generation in a coding project.
What is Cursor's Copilot++ and how does it differ from Github Copilot?
-Cursor's Copilot++ is an AI coding assistant that was launched recently. It is similar to Github Copilot in providing inline auto-completion, but it is designed to be more context-aware and capable of auto-completing multiple lines without the cursor necessarily being in the position for the completion.
How can multiple prompts be run simultaneously in the AI coding assistant?
-Multiple prompts can be run simultaneously by initiating different tasks or commands at the same time, as demonstrated in the script where the user runs different prompts for adding and removing nodes.
What is the significance of auto-completing multiple lines in AI coding assistance?
-Auto-completing multiple lines is significant as it allows the AI to understand the broader context of the code and make more intelligent suggestions, which can greatly speed up the coding process and reduce the need for manual input.
What is the 'fit to view' function used for in the script?
-The 'fit to view' function is used to automatically resize the view to fit the newly added nodes and edges in the graph, ensuring that all elements are visible without the need for manual scrolling.
How does the AI coding assistant handle iterative corrections in the code?
-The AI coding assistant allows for iterative corrections by providing suggestions and auto-completions that can be accepted or modified by the user. It is designed to learn from the corrections made by the user to improve its future suggestions.
What is the role of the 'use view flow' library in the script?
-The 'use view flow' library is used to create programmatically interesting graphs and charts in Vue.js. It is a tool that helps in manipulating the nodes and edges in the graph, as demonstrated in the script.
How can reference documents be added and utilized in the AI coding assistant?
-Reference documents can be added through the chat interface of the AI coding assistant. Once added, they can be referenced in inline commands to guide the AI in providing accurate and context-specific code suggestions.
What is the importance of moving up the stack in terms of coding with AI assistants?
-Moving up the stack in coding with AI assistants means focusing more on high-level logic and decision-making rather than individual lines of code. This approach allows developers to work more like product managers or UX engineers, leveraging AI to enhance their engineering abilities and stay relevant in the industry.
How does the AI coding assistant handle the creation of functions based on context?
-The AI coding assistant can create functions based on the context of the code and the user's instructions. It uses the surrounding code and user prompts to generate the necessary function code, as shown when creating the 'create custom edge' function in the script.
What is the 'apply diff' functionality mentioned in the script?
-The 'apply diff' functionality is a feature that is supposed to apply changes to the code based on the suggestions made by the AI assistant. However, in the script, it is noted that this functionality may not be working as expected and requires manual copy-pasting of the suggested code changes.
Outlines
🤖 Introduction to AI Coding Assistants
The video script introduces the capabilities of AI coding assistants, specifically focusing on Cursor, which recently launched a feature called co-pilot Plus+. The script demonstrates how AI can handle multiple coding prompts simultaneously, such as creating buttons and implementing features like auto-completion across multiple lines of code without the need for the cursor to be in the exact position. The assistant also showcases how to use Cursor's features to manipulate UI elements and improve coding efficiency.
🔄 Enhancing Productivity with AI Coding Features
This section delves into the advanced features of Cursor's AI coding assistance, emphasizing the iterative control and correction capabilities of the tool. It illustrates how to use view watchers for reactive updates and the co-pilot Plus+ feature for contextual auto-completion. The script also covers the process of adding reference documents to Cursor for inline command prompts, demonstrating how to integrate documentation into the coding process to enhance productivity and accuracy.
🌐 Building a Node Graph System with AI Assistance
The final paragraph of the script details the process of building a node graph system using AI coding assistance. It explains how to utilize Cursor's co-pilot Plus+ to auto-generate functions, create edges, and manage node IDs efficiently. The script also highlights the importance of moving up the decision-making stack in software development, focusing on high-level logic manipulation rather than individual lines of code, and emphasizes the use of AI to expedite the coding process and maintain relevance in the engineering field.
Mindmap
Keywords
💡AI coding assistant
💡Cursor's Copilot++
💡Autocompletion
💡Vue.js
💡Graph
💡Nodes
💡Edges
💡Inline instructions
💡Documentation
💡Apply diff
💡Context awareness
Highlights
Cursor's AI coding assistant, Copilot++, was recently launched and is being compared to Github Copilot.
AI coding assistance can run multiple prompts simultaneously, enhancing productivity.
Copilot++ offers inline auto-completion that is aware of more context, unlike traditional autocomplete tools.
The new feature of Cursor's Copilot++ auto-completes multiple lines without the cursor being in the exact position.
Cursor's Copilot++ is shown to be more advanced than Github Copilot by making changes wherever needed, not just in front of the cursor.
AI coding assistants are iteratively controllable, allowing for corrections of mistakes made by the AI or requested by the user.
Cursor's ability to add reference documents is highlighted as a key feature, enhancing the context understanding of the AI.
The integration of documentation with coding prompts allows for more accurate and context-aware code generation.
A demonstration of updating edges and nodes in a graph using AI coding assistance is provided.
Auto-completion of code is shown to be significantly faster and more accurate with Cursor's Copilot++.
Cursor's Copilot++ is capable of understanding and applying changes to code based on the context of the entire file.
The use of 'fit to view' functionality is shown to automatically resize and adjust the view after adding new nodes.
AI coding assistants are recommended for upleveling one's position on the stack, focusing more on high-level logic rather than individual lines of code.
The importance of using AI coding assistants to stay relevant and enhance engineering abilities in the face of industry changes is discussed.
The video concludes with a call to utilize technology like AI coding assistants to create concrete value in daily life as engineers.
