N8n - LLm - embeddings
This project showcases a powerful, zero-effort resume matching system built with n8n to automate the entire hiring pipeline. The solution seamlessly integrates Google Drive for resume storage, Google Gemini for generating advanced text embeddings, and Pinecone for indexing and querying a high-performance vector database. The system features two core n8n workflows: one for processing and indexing resumes from Google Drive into Pinecone, and another that triggers via a web form to match job requirements against the indexed resumes. When a user submits job details, an AI agent processes the query, performs a similarity search in Pinecone, and uses Google Gemini to interpret the results before automatically sending the top candidate recommendations via Gmail. This creates a fully automated, end-to-end solution for intelligent candidate sourcing.
N8n - LLm - SEO
HealthSEO Auditor is a fully automated website health and SEO auditing workflow built with n8n. This powerful tool allows users to submit a website URL via a simple form and choose from four distinct analysis types: a comprehensive SEO Audit, a Google PageSpeed Insights report, an Accessibility Check, or an overall Business Value Score. The n8n workflow intelligently routes the request, leveraging the Google PageSpeed Insights API for performance metrics and Google Gemini AI to conduct in-depth evaluations for SEO, accessibility, and business value. The AI then generates a clean, professional, and client-ready report from the findings. Finally, the system automatically formats the report into an HTML email and delivers it directly to the user's inbox, providing actionable insights with zero manual intervention.
NextJs - tailwind - Puppetee - TypeScript
This repository contains a web application that displays on-duty pharmacies (pharmacies de garde) in a given area. The application helps users quickly find pharmacies that are open outside regular business hours by scraping real-time data from relevant websites.
Python - Sklearn - Numpy
This repository contains a machine learning project for predicting car prices based on various features such as make, model, year, fuel type, transmission, engine size, and mileage. The project uses a RandomForestRegressor model from the scikit-learn library.
