llm scaffold

The code defines a constant PROFILE_PATH and an asynchronous function llmScaffold(github) that scaffolds a project. The llmScaffold function is exported as a module, allowing it to be used elsewhere in the application.

The code imports various modules and defines a function called generateBash which generates shell code using a Large Language Model (LLM) and saves it to a GitHub repository. The function checks the repository's existence, updates it if necessary, extracts code blocks from the LLM's response, and writes them to a new file in the repository.

The generateCode function is an asynchronous function that generates code based on a given prompt and project repository, and handles various steps such as resolving the Github path and cloning the repository. If no codeFile is provided, the function uses the llmCode function to ask the user for a file to edit based on the prompt, and logs the prompt and potential file name(s) generated by the AI model.

This Node.js script uses various libraries and functions to generate code based on a given prompt, involving operations such as Git clone, code generation using a Large Language Model (LLM), and output file naming. The makeCode function takes a prompt and optional repository URL as input, generates code, and returns the code blocks, while also handling errors and edge cases.

This Node.js script uses a Large Language Model (LLM) to generate code by constructing prompts, sending them to the LLM, and extracting code blocks from the response. The script performs tasks such as project path resolution, Git pull, and code interpretation to ensure the code is up-to-date and accurate before sending the prompt to the LLM.

This Node.js script uses a large language model (LLM) to generate code blocks for a project by fetching code from the LLM and extracting relevant blocks. The script handles errors, resolves GitHub repository and notebook file paths, executes a git pull command, and logs the generated code blocks to the console.

The decodeJupyterNotebook function decodes the content of a Jupyter Notebook file by reading it into a string, parsing it as JSON, and logging the content of code cells. It takes a single argument, filePath, which is the path to the Jupyter Notebook file, and raises an error if there are any issues with file reading or parsing.