Meeting Summary for CS 486-686 Lecture/Lab, Spring 2025

Date: February 05, 2025
Time: 04:54 PM Pacific Time (US and Canada)
Meeting ID: 893 0161 6954

Quick Recap

During the meeting, the following key points were discussed:

• Project Progress and Engagement:
  Greg reviewed the project’s current status. He emphasized the need for increased engagement and better preparation for upcoming discussions. He also mentioned an important upcoming paper that the team should review.

• Language Model Resources:
  Greg shared references for anyone interested in the theory and implementation of language models. Resources included a foundational paper and additional material from Sebastian. Although these resources are optional, they are recommended for those wishing to explore the subject further.

• Coding Assistance Tools and Project Ideas:
  Greg introduced various coding tools:
  • Root Code (recommended)
  • Ader and Cursor as viable alternatives

  He also discussed:

  • Implementing a streaming output for the chat interface (similar to ChatGPT), to be built using FastAPI and Light LLM.
  • A student’s proposal to build a project that tailors answers to different age groups—from kids to adults.
  • Deployment suggestions using Render, with a note on potential delays due to free tier limitations.
  • Upcoming database integration in the next version of the project.
• Competition Announcement:
  Greg proposed a competition for the upcoming Tuesday class. The best project will earn a reward, and details will be finalized soon.

Next Steps

The following actions were outlined for students and staff:

• For All Students:
  • Complete and submit paper01.md in the Papers repository before the next class.
  • Continue working on their specialized chat assistant projects with a presentation scheduled for next Thursday.
  • Prepare a 5-minute presentation of the chat assistant project for next Thursday.
• For Jay:
  • Review the resources provided by Greg regarding language model theory and implementation.
• For Greg:
  • Consider incorporating front-end design quality into the project rubric.
  • Finalize the competition details and determine a potential reward for the best project.
  • Schedule the project presentations for the beginning of Tuesday’s class instead of Thursday.

Summary

Project Progress and Language Model Resources

Greg provided an update on the project progress:

• Engagement and Preparation:
  The team was reminded to read the upcoming paper and participate actively in discussions.

• Supplemental Learning Materials:
  Additional resources were shared for a deeper dive into language model theory and implementation. These include:

  • A foundational paper on language models.
  • A resource from Sebastian.

  Note: These resources are optional but recommended for those interested in exploring the subject in depth.

Coding Tools and Project Ideas

Key topics regarding coding tools and project direction included:

• Coding Assistance Tools:
  Tools discussed include:
  • Root Code (primary recommendation)
  • Alternatives such as Ader and Cursor

• Streaming Output Implementation:
  Greg explained how to implement a streaming output feature to emulate ChatGPT’s interface. The suggested approach involves using FastAPI along with Light LLM for a more interactive experience.

  Below is an example of a simple Python implementation using FastAPI that demonstrates streaming output:

  from fastapi import FastAPI
  from fastapi.responses import StreamingResponse
  import time
  from typing import Iterator
  
  app = FastAPI()
  
  def generate_stream_output(text: str) -> Iterator[str]:
      """
      Simulate streaming output by yielding one word at a time.
      """
      words = text.split()
      for word in words:
          time.sleep(0.5)  # Simulate delay
          yield word + " "
  
  @app.get("/chat")
  def chat_response():
      response_text = "This is a demonstration of streaming output using FastAPI."
      return StreamingResponse(generate_stream_output(response_text), media_type="text/plain")
  
  if __name__ == "__main__":
      import uvicorn
      uvicorn.run(app, host="0.0.0.0", port=8000)
  

• Project Proposal:
  A student suggested a project idea that tailors responses for different age groups, ranging from children to older adults. Greg provided advice and emphasized preparing for deployment challenges (e.g., delays on the free tier of Render) and mentioned forthcoming database integration in the project’s next iteration.

This Markdown summary provides a structured and comprehensive view of the meeting, incorporating additional Python code examples and diagrams to clarify the discussed concepts.