Your closet already knows what you own; Wardrowbe is the part that remembers it for you and decides what goes together tomorrow morning. Photograph garments one at a time or several in a single shot, and a vision model tags each piece with its type, color, pattern, fabric, style, and formality, with a confidence score you can correct. From that catalog the app builds complete outfits out of clothes you already have, weighing your local weather, the occasion, notes you attached to individual pieces, and the aesthetic you described. A daily suggestion can arrive on a schedule through ntfy, Mattermost, or email, so the outfit is waiting before you are. Accept, reject, and rate what you wear, and the history calendar keeps every past recommendation while the feedback sharpens the next ones. Pairings work the other way around too: pick one item and get outfits built around it, each with a plain-language explanation of why the combination works. Analytics show what you actually wear, what never leaves the rail, and how your colors distribute, and the wardrobe view flags pieces that need washing. Households manage several members' wardrobes with personalized suggestions for each person. Everything runs against any OpenAI-compatible endpoint, so Ollama with a local multimodal model keeps the whole pipeline offline and free. The interface speaks eight languages and follows your browser by default. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.