🙈 Made a hilariously redundant R package for a simple OpenAI calls, but the real win was finally learning how to build an R package! 🛠️ Is it efficient? Absolutely not!Was it worth the time and experience? Yes! Will I do it again? Yes! Will it break? Yes! 🤣
How do we identify relevant articles in our domains? This project uses example journal RSS feeds with abstracts, uses LLMs to extract points of interest, and shares insights on Bluesky—stimulating curiosity.
"Fascinating" describes my journey with Stable Diffusion 3. It’s deepened my appreciation for original art and masterpieces. Understanding how to generate quality art is just the beginning—it drives me to explore the underlying structure. Join me in exploring SD3 in R!
Overall, I am quite impressed with the responses! With minimal prompt engineering, document cleaning! It was able to return accurate responses, and even separated different conditions and provided appropriate treatment options. It was also able to return the correct response for tricky questions that our RAG was not able to. It definitely has potential!
Wow, what a journey, and more to come! We learned how to perform simple RAG with an LLM and even ventured into LangChain territory. It wasn’t as scary as some people said! The documentation is fantastic. Best of all, we did it ALL in R with Reticulate, without leaving RStudio! Not only we can read IDSA Guidelines, we can use LLM to assist us with retrieving information!
I’m delighted that R users can have access to the incredible Hugging Face pre-trained models. In this demonstration, we provide a straightforward example of how to utilize them for sentiment analysis using GPT-generated synthetic data from evaluation comments. Let’s go!
The PyWhy Causal-learn Discord community is fantastic! The package documentation is equally impressive, making experiential learning both fun and informative. Truly, it’s another exceptional tool for causal discovery at our fingertips!
Get ready for a thrill ride in causal discovery! We’re diving into gCastle, a Python package, right in R to amp up our skills. Let’s orchestrate our prior knowledge and nail that true DAG. 🔥
I have learned a tremendous amount from everyone involved, including members from other teams. The entire experience has been truly enriching, and I thoroughly enjoyed every moment of it. I want to express my heartfelt gratitude to everyone involved for making this an exceptional learning journey. Thank you all!
R you doing it?Just Because in a true sense :D