Refactor: Split portfolio to projects and writings sections, and update content structure

- Renamed 'portfolio' collection to 'projects' in content configuration.
- Introduced a new 'writings' collection with corresponding schema.
- Updated README to reflect changes in content structure and navigation.
- Removed the old portfolio page and added new pages for projects and writings.
- Added multiple new project and writing markdown files with relevant content.
- Updated license year to 2025.
- Enhanced AppHeader for new navigation links.
- Improved ProseImg component styling.

content/projects/arthome.md

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+---
+slug: arthome
+title: 🏡 ArtHome
+description: Your personalised home page in your browser
+publishedAt: 2024/09/04
+readingTime: 1
+cover: arthome/cover.png
+tags:
+  - web
+---
+
+[ArtHome](https://home.arthurdanjou.fr) is a personalised page where you can create categories and tabs to have a one page with all your shortcuts on all browsers.
+
+You can customize your tabs and categories with different colors and icons. Feel free to set your page in private if you want to keep secret your tabs.
+
+Made with:
+
+- [Nuxt](https://nuxt.com): Nuxt is an **open source framework** that makes web development intuitive and powerful.Create performant and production-grade full-stack web apps and websites with confidence.
+- [NuxtHub](https://hub.nuxt.com): Deploy and scale your Nuxt applications worldwide with NuxtHub, the Cloudflare-powered platform that ensures lightning-fast performance at low cost and with full-stack capabilities.
+- [NuxtUI](https://ui.nuxt.com): Nuxt UI simplifies the creation of stunning and responsive web applications with itscomprehensive collections of fully styled and customizable UI components designed for Nuxt.
+- [Eslint](https://eslint.org): ESLint is an open source project that helps you find and fix problems with your JavaScript code.
+- [Drizzle](https://orm.drizzle.team/): Drizzle ORM is a headless TypeScript ORM with a head
+- [Zod](https://zod.dev/): TypeScript-first schema validation with static type inference
+- and ❤️

content/projects/artsite.md

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+---
+slug: artsite
+title: 🌍 ArtSite
+description: My personal website, my portfolio, and my blog.
+publishedAt: 2024/06/01
+readingTime: 1
+cover: artsite/cover.png
+tags:
+  - web
+---
+
+[**ArtSite**](https://arthurdanjou.fr) is my personal website, my portfolio, and my blog. It's a place where I can share my projects, my thoughts, and my experiences. It's also a place where I can experiment with new technologies and design ideas.
+
+## ⚒️ Tech stack
+
+- **UI** → [Vue.js](https://vuejs.org/)
+- **Framework** → [Nuxt.js](https://nuxtjs.org/)
+- **Content** → [Nuxt Content](https://content.nuxtjs.org/)
+- **Design System** → [NuxtUI](https://nuxtui.com/)
+- **CMS & Editing** → [Nuxt Studio](https://nuxt.studio)
+- **Langage** → [Typescript](https://www.typescriptlang.org/)
+- **Deployment** → [NuxtHub](https://hub.nuxt.com/)
+- **Styling** → [Sass](https://sass-lang.com/) & [Tailwind CSS](https://tailwindcss.com/)
+- **Package Manager** → [pnpm](https://pnpm.io/)

content/projects/bikes-glm.md

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+---
+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>

content/projects/monte-carlo-project.md

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+---
+slug: monte-carlo-project
+title: 💻 Monte Carlo Methods Project
+description: A project to demonstrate the use of Monte Carlo methods in R.
+publishedAt: 2024/11/24
+readingTime: 3
+tags:
+  - r
+  - maths
+---
+
+This is the report for the Monte Carlo Methods Project. The project was done as part of the course `Monte Carlo Methods` at the Paris-Dauphine University. The goal was to implement different methods and algorithms using Monte Carlo methods in R.
+
+Methods and algorithms implemented:
+- Plotting graphs of functions
+- Inverse c.d.f. Random Variation simulation
+- Accept-Reject Random Variation simulation
+- Random Variable simulation with stratification
+- Cumulative density function
+- Empirical Quantile Function
+
+You can find the code here: [Monte Carlo Project Code](https://github.com/ArthurDanjou/Studies/blob/0c83e7e381344675e113c43b6f8d32e88a5c00a7/M1/Monte%20Carlo%20Methods/Project%201/003_rapport_DANJOU_DUROUSSEAU.rmd)
+
+<iframe src="/projects/monte-carlo-project/Report.pdf" width="100%" height="1000px">
+</iframe>

content/projects/python-data-ml.md

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+---
+slug: python-data-ml
+title: 🐍 Python Data & ML
+description: A repository dedicated to learning and practicing Python libraries for machine learning.
+publishedAt: 2024/11/01
+readingTime: 1
+tags:
+  - data
+  - ai
+  - python
+---
+
+[Python Data & ML](https://github.com/ArthurDanjou/Python-Data-Machine-Learning) is a repository dedicated to learning and practicing Python libraries for machine learning. It includes a variety of projects and exercises that cover the following topics.
+This project explores tools like NumPy, Pandas, scikit-learn, and others to understand and master key machine learning concepts. Perfect for strengthening skills in data processing, modeling, and algorithm optimization.
+
+The goal is to improve my level of understanding of machine learning and data science concepts, as well as to practice Python programming and using libraries like NumPy, Pandas, scikit-learn, etc., to manipulate and analyze data, during my free time.
+
+## Tech Stack
+
+- [Python](https://www.python.org/)
+- [NumPy](https://numpy.org/)
+- [Pandas](https://pandas.pydata.org/)
+- [scikit-learn](https://scikit-learn.org/stable/)
+- [Matplotlib](https://matplotlib.org/)
+- [Seaborn](https://seaborn.pydata.org/)
+- [Jupyter Notebook](https://jupyter.org/)
+- [TensorFlow](https://www.tensorflow.org/)
+- [Keras](https://keras.io/)
+- [PyTorch](https://pytorch.org/)

content/projects/schelling-segregation-model.md

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+---
+slug: schelling-segregation-model
+title: 📊 Schelling Segregation Model
+description: A Python implementation of the Schelling Segregation Model using Statistics and Data Visualization.
+publishedAt: 2024/05/03
+readingTime: 4
+tags:
+  - python
+  - maths
+---
+
+This is the French version of the report for the Schelling Segregation Model project. The project was done as part of the course `Projet Numérique` at the Paris-Saclay University. The goal was to implement the Schelling Segregation Model in Python and analyze the results using statistics and data visualization.
+
+You can find the code here: [Schelling Segregation Model Code](https://github.com/ArthurDanjou/Studies/blob/e1164f89bd11fc59fa79d94aa51fac69b425d68b/L3/Projet%20Num%C3%A9rique/Segregation.ipynb)
+
+<iframe src="/projects/schelling/Projet.pdf" width="100%" height="1000px">
+</iframe>

content/projects/studies.md

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+---
+slug: studies
+title: 🎓 Studies projects
+description:  A collection of projects done during my studies.
+publishedAt: 2023/09/01
+readingTime: 1
+tags:
+  - data
+  - python
+  - r
+---
+
+[Studies projects](https://github.com/ArthurDanjou/studies) is a collection of mathematics projects done during my studies. It includes projects in _Python_ and in _R_.
+
+The projects are divided into two main categories: _L3_ and _M1_, corresponding to the third year of the bachelor's degree and the first year of the master's degree in mathematics.
+
+File structure:
+- `L3`
+  - `Analyse Matricielle`
+  - `Analyse Multidimensionnelle`
+  - `Calculs Numériques`
+  - `Equations Différentielles`
+  - `Méthodes Numériques`
+  - `Probabilités`
+  - `Projet Numérique`
+  - `Statistiques`
+- `M1`
+  - `Data Analysis`
+  - `General Linear Models`
+  - `Monte Carlo Methods`
+  - `Portfolio Management`
+
+Made with:
+- [Python](https://www.python.org): Python is an interpreted, high-level and general-purpose programming language.
+- [R](https://www.r-project.org): R is a programming language and free software environment for statistical computing and graphics.
+- [Jupyter](https://jupyter.org): Jupyter is a free, open-source, interactive web tool known as a computational notebook, which researchers can use to combine software code, computational output, explanatory text and multimedia resources in a single document.
+- [Pandas](https://pandas.pydata.org): Pandas is a fast, powerful, flexible and easy to use open source data analysis and data manipulation library built on top of the Python programming language.
+- [Numpy](https://numpy.org): NumPy is the fundamental package for scientific computing in Python.
+- [Scipy](https://www.scipy.org): SciPy is a free and open-source Python library used for scientific and technical computing.
+- [Matplotlib](https://matplotlib.org): Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.
+- [RMarkdown](https://rmarkdown.rstudio.com): R Markdown is an authoring framework for data science. You can use a single R Markdown file to save and execute code and generate high-quality reports that can be shared with an audience.
+- [FactoMineR](https://factominer.free.fr/): FactoMineR is an R package dedicated to multivariate exploratory data analysis.
+- [ggplot2](https://ggplot2.tidyverse.org): ggplot2 is a system for declaratively creating graphics, based on The Grammar of Graphics.
+- and my 🧠