Code With Me - Build a Code-Generating AI Assistant (Like Copilot)
AI Summary
Video Summary: Building an AI Pair Programmer
- Introduction:
- The video discusses the creation of “Kevin,” an Enterprise AI programmer.
- Kevin assists in writing code for complex enterprise applications, ensuring compliance, reusability, and maintainability.
- The AI writes boilerplate code and checks for errors and warnings.
- Kevin’s Features:
- Open-source and available for download.
- Runs locally, keeping source code private.
- Accessible via a provided QR code or URL (not specified in the transcript).
- Tutorial Overview:
- The tutorial demonstrates how to build an AI pair programmer using Kevin’s code.
- It involves installing libraries from Longchain and OpenAI and setting up an OpenAI API key.
- Interacting with OpenAI Library:
- A system prompt is set up to define the AI’s behavior (e.g., being a funny programmer).
- A test prompt (“Hello, how are you”) is used to verify integration with the OpenAI Library.
- Setting Up Local LLM with O Lama:
- Installation of O Lama and downloading the Lama 3.1 model.
- A quantized version of LLM is used for local machines, which offers lower quality but still functional results.
- Writing Code with LLM:
- The system prompt is modified to instruct the AI to write TypeScript code following certain standards.
- An example task is given to create an HTML login page with email and password fields.
- The AI generates the code and specifies the file name format in the response.
- Parsing and Saving Code:
- The tutorial shows how to parse the AI’s response and save the generated code to a specified file.
- Updating Existing Code:
- The AI is instructed to update code, such as changing “email” to “username.”
- The source code is read and fed into the AI for updating.
- Error Checking with Lint:
- A lint check is performed to identify errors or warnings in the code.
- The AI is then used to automatically fix the errors based on lint check results.
- Code Correction Process:
- The AI is prompted to fix issues and introduce new lines of code.
- Corrected code is obtained from the AI and saved back to the file.
- Conclusion:
- The process is similar to how GitHub Copilot or Cursor works, integrating LLM into an IDE.
- The AI updates code based on its responses, simplifying the coding process.
- Call to Action:
- Viewers are encouraged to like and subscribe for more content (no self-promotion included).
Note: The exact URL for accessing Kevin and specific CLI commands were not provided in the transcript.