Resources

My Related Talks

• LLMs, Chatbots, and Dashboards: Visualize Your Data with Natural Language
  • https://www.youtube.com/watch?v=ESUvuemGmPQ&t=17s
  • https://chendaniely.github.io/nydsaic2025-llm/
• Tips to Level-Up Your Shiny for Python Applications | PyData Global 2024
  • https://www.youtube.com/watch?v=2Cst7_s_4H8
• LLMs, Chatbots, and Dashboards_ Visualize Your Data with Natural Language - PyData Global 2025
  • https://www.youtube.com/watch?v=-czwZ_t6VUU&list=PLGVZCDnMOq0qmerwB1eITnr5AfYRGm0DF&index=50

Shiny

• Shiny for Python docs: https://shiny.posit.co/py/

• Component gallery: https://shiny.posit.co/py/components/

• Templates: https://shiny.posit.co/py/templates/

• Shiny concepts page: https://shiny.posit.co/py/docs/overview.html

• Mastering Shiny book (R): https://mastering-shiny.org/

• UBC DSCI 532: Data Visualization 2 course: https://ubc-mds.github.io/DSCI_532_vis-2_book/

Shiny Reactivity

• Gordon Shotwell & Tracy Teal — Build Simple and Scalable Apps with Shiny | PyData NYC 2023
  • Slides: https://github.com/gshotwell/shiny-algorithm
• Joe Cheng — Shiny: Data-centric web applications in Python | PyData Seattle 2023

Python, Shiny, AI, LLMs

• chatlas: https://posit-dev.github.io/chatlas/
• shinychat: https://posit-dev.github.io/shinychat/py/
• querychat: https://github.com/posit-dev/querychat

Testing LLM output

How do you test that the LLM is returning the correct result? This process is called “evals”

• chatlas + evals: https://posit-dev.github.io/chatlas/misc/evals.html
• inspectai evals framework: https://inspect.aisi.org.uk/

I have a demo of chatlas + inspect ai here:

• Tips to Level-Up Your Shiny for Python Applications | PyData Global 2024
  • https://www.youtube.com/watch?v=2Cst7_s_4H8