With the big hype around AI agents these days, I couldn’t stop thinking about how AI agents could truly enhance real-world activities. What sort of applications could we build with those AI agents: agentic RAG? self-correcting text-to-sql? Nah, boring…
Passionate about outdoors, I’ve always dreamed of a tool that could simplify planning mountain trips while accounting for all potential risks. That’s why I built 𝗔𝗹𝗽𝗶𝗻𝗲 𝗔𝗴𝗲𝗻𝘁, a smart assistant designed to help you plan safe and enjoyable itineraries in the French Alps and Pyrenees.
Built using Hugging Face's 𝘀𝗺𝗼𝗹𝗮𝗴𝗲𝗻𝘁𝘀 library, Alpine Agent combines the power of AI with trusted resources like 𝘚𝘬𝘪𝘵𝘰𝘶𝘳.𝘧𝘳 (https://skitour.fr/) and METEO FRANCE. Whether it’s suggesting a route with moderate difficulty or analyzing avalanche risks and weather conditions, this agent dynamically integrates data to deliver personalized recommendations.
In my latest blog post, I share how I developed this project—from defining tools and integrating APIs to selecting the best LLMs like 𝘘𝘸𝘦𝘯2.5-𝘊𝘰𝘥𝘦𝘳-32𝘉-𝘐𝘯𝘴𝘵𝘳𝘶𝘤𝘵, 𝘓𝘭𝘢𝘮𝘢-3.3-70𝘉-𝘐𝘯𝘴𝘵𝘳𝘶𝘤𝘵, or 𝘎𝘗𝘛-4.
Starting this collection to gather models, spaces, dataset or even papers related to disability. Feel free to ping me if you see something relevant to add
We shut down XetHub today after almost 2 years. What we learned from launching our Git-scaled product from scratch: - Don't make me change my workflow - Data inertia is real - ML best practices are still evolving
Closing the door on our public product lets us focus on our new goal of scaling HF Hub's storage backend to improve devX for a larger community. We'd love to hear your thoughts on what experiences we can improve!