Lakshay Dagar

Shares his initial experience and approach to participating in Kaggle competitions, focusing on data exploration, model building (Random Forest, Gradient Boosting), and feature engineering.

A detailed article on creating a self-hosted LLM API, discussing infrastructure, inference frameworks (vLLM, SGlang), and deployment using Modal.

A technical deep dive into the challenges and pitfalls of using the Unsloth library for LLM fine-tuning, offering solutions and best practices for memory management and debugging in PyTorch and Transformers.

Describes a method for integrating Gradio Python APIs into FlutterFlow applications, addressing challenges in dynamic widget updates and API endpoints for machine learning models.