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Meeting Summary: CS 486-686 Lecture/Lab – Spring 2025

Date: January 22, 2025, 04:45 PM Pacific Time (US and Canada)

Summary of Main Topics Discussed

Class Introduction and Attendance

  • The instructor made a concerted effort to memorize student names and identify each student in the class.
  • Students introduced themselves, and some shared details about recent changes, such as joining the class late.

Account Setup and Free Credits

  • A discussion took place regarding the process of obtaining free credits for OpenRouter.
  • Recommendations were given on managing API usage costs by starting with small amounts (e.g., $10) to explore capabilities.

Using OpenRouter and LLM CLI Tools

  • The session included a demonstration on setting up OpenRouter accounts, generating API keys, and managing them securely.
  • The introduction of LLM CLI tools highlighted their utility for quick prompt testing and automation.
  • Examples illustrated how to efficiently send prompts and retrieve outputs.

Virtual Environments and Python Setup

  • Guidance was provided on creating Python virtual environments to manage dependencies safely.
  • The instructor emphasized using modern Python practices, such as setting up virtual environments per project or globally.
  • Specific instructions covered the installation of Python and managing virtual environments on macOS (via Homebrew) and Windows (using WSL).

Integration with IDEs and Development Tools

  • Tools like Aider (an AI pair programmer) were showcased, with a focus on integration with Git repositories.
  • Aider was demonstrated to automate code commits with human-like messages.
  • Recommendations included using Aider with VS Code and exploring file monitoring features.

LiteLLM and API Abstraction

  • LiteLLM was introduced as an abstraction layer that is compatible with various LLM APIs.
  • Its flexibility to work with models such as Claude, OpenAI, and Google Gemini was explained.
  • The session highlighted LiteLLM’s role in simplifying API calls and supporting configurations involving multiple models.

Practical Applications and Demonstrations

  • Live coding demonstrations featured the use of Aider to create a web application for base conversions.
  • Modifications were made to integrate FastAPI for backend processing.
  • The session included a review of generated code and a validation of real-time backend interactions.

Troubleshooting and Technical Challenges

  • Common issues were addressed, including challenges with environment setup, API key management, and package installations.
  • Discussions covered compatibility problems arising from fast-evolving libraries and frameworks.
  • The importance of using virtual environments to mitigate dependency conflicts was emphasized.

Prompt Engineering and Real Python Tutorial

  • Practical prompt engineering exercises were introduced, drawing on examples from Real Python tutorials.
  • The session encouraged exploring prompt techniques and sample data for effective LLM interactions.
  • Clarifications were provided on the appropriate use of Aider versus manual coding during learning exercises.

Next Steps and Assignments

  • Students were expected to set up their environments, including LiteLLM and OpenRouter.
  • A weekend assignment was given to review Real Python tutorials and practice prompt engineering.
  • Students were encouraged to post questions and issues on Campus Wire to facilitate collaborative troubleshooting.

References