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Arthur DANJOU
05963bb605
- 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.
858 B
858 B
slug, title, description, publishedAt, readingTime, tags
| slug | title | description | publishedAt | readingTime | tags | ||
|---|---|---|---|---|---|---|---|
| schelling-segregation-model | 📊 Schelling Segregation Model | A Python implementation of the Schelling Segregation Model using Statistics and Data Visualization. | 2024/05/03 | 4 |
|
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