feat: add new articles on AI agents and machine learning

- Created a new article on "Understanding AI Agents, LLMs, and RAG" detailing the synergy between AI agents, LLMs, and Retrieval-Augmented Generation.
- Added an introductory article on "What is Machine Learning?" covering types, model selection, workflow, and evaluation metrics.

chore: setup ESLint and Nuxt configuration

- Added ESLint configuration for code quality.
- Initialized Nuxt configuration with various modules and settings for the application.

chore: initialize package.json and TypeScript configuration

- Created package.json for dependency management and scripts.
- Added TypeScript configuration for the project.

feat: implement API endpoints for activity and stats

- Developed API endpoint to fetch user activity from Lanyard.
- Created a stats endpoint to retrieve Wakatime coding statistics with caching.

feat: add various assets and images

- Included multiple images and assets for articles and projects.
- Added placeholder files to maintain directory structure.

refactor: define types for chat, lanyard, time, and wakatime

- Created TypeScript types for chat messages, Lanyard activities, time formatting, and Wakatime statistics.

content/projects/bikes-glm.md

@@ -0,0 +1,18 @@
+---
+slug: bikes-glm
+title: 🚲 Generalized Linear Models for Bikes prediction
+description: Predicting the number of bikes rented in a bike-sharing system using Generalized Linear Models.
+publishedAt: 2025/01/24
+readingTime: 1
+tags:
+  - r
+  - data
+  - maths
+---
+
+The project was done as part of the course `Generalised Linear Model` at the Paris-Dauphine PSL University. The goal of the project is to determine the best model that predicts/explains the number of bicycle rentals, based on various characteristics of the day (temperature, humidity, wind speed, etc.).
+
+You can find the code here: [GLM Bikes Code](https://github.com/ArthurDanjou/Studies/blob/master/M1/General%20Linear%20Models/Projet/GLM%20Code%20-%20DANJOU%20%26%20DUROUSSEAU.rmd)
+
+<iframe src="/projects/bikes-glm/Report.pdf" width="100%" height="1000px">
+</iframe>