Doing the laundry is one of the most forgettable household chores for me. The clothes keep piling on until that one day, I have to step out of the house, and suddenly, all my outfits are missing.
Having recently built a local coding AI for VS Code, I decided to put it to the test by building an automated laundry tracking app. Now, I know where every single piece of clothing I own is, and I can get AI-powered outfit ideas as a bonus.
Why bother automating laundry?
Because wasting brainpower on finding clean clothes is overrated
If you read the title and rolled your eyes, I understand. After all, how hard can it be to keep track of where your clothes are, right?
Our lives are surrounded by small decisions that we make every day. What to wear, what to eat, where to go. All these choices overload your nervous system, causing something known as decision fatigue. The more decisions you take, the more they deteriorate in quality. I want to reserve good quality decisions for my work, which means anything unnecessary gets tracked digitally, so I don’t spend unnecessary brain power.
Washing clothes, drying them, folding them, and getting them back into a cupboard is no small chore. Not to mention the decisions you run through when deciding what outfit to wear for a particular occasion. My clothes make me look good, but they don’t necessarily contribute to my productivity at work.
The solution I came up with is quite simple in principle, and with the help of AI, I was able to get a vibe-coded prototype up and running on a casual Sunday afternoon. It’s far less effort than I thought it would be, and for a personal project that doesn’t have to meet real-world production-level standards, it’s quite versatile.
A one-tap system that logs where your clothes are
For me, the biggest issue with doing laundry isn’t washing—it’s tracking. I didn’t realize this until I ran out of clothes, walked begrudgingly to the washing machine, only to realize I was missing a ton of clothes I usually wear. After searching my cupboards and laundry basket, I found an entire stash of dirty clothes which I had completely forgotten about.
I solved this with NFC tags, the same technology that powered tap-to-pay cards and apps. Each piece of clothing I own gets a small NFC sticker sewn onto it. Then, I placed NFC readers in my cupboard, washing machine, laundry basket, and drying rack. Whenever I move something, whether it’s from the washing machine to the drying rack or back into my cupboard, I just tap the tag to the respective reader.
This tap updates the cloth’s status in my local database. The backend, built with TypeScript and Express, logs every change. This way I always know where my clothes are. I can see what clothes I have where at any given point and can even search for specific garments and accessories.
The entire project is ridiculously simple but quite effective. No cloud syncs, no subscriptions, no smart washing machine—just good old NFC tags and a local server is all you need. You can pick up a 50-piece NFC sticker pack on Amazon for as low as $8, and since the app is web-based, it runs on any device that can run a web browser.
AI that suggests what to wear
A local AI that knows your mood—and your laundry status
Tracking clothes is only half the fun. As easy as it is to know exactly where each piece of clothing I own is, the real benefit is knowing what outfits are ready to go when I head out of the house.
The system connects to a small local LLM running via LMStudio. I’ve already been using local LLMs to ensure I never pay for AI again. Hooking up the app to the LLM to analyze my clothes and suggest outfits based on a certain mood or aesthetic seemed like a natural step.
When I add a piece of clothing to my tracking system, it includes information like the type of garment, brand, and color. That’s all the information a context-aware LLM needs to spin up outfit suggestions for a specific look. Since the AI also knows what’s the status of a garment—whether it’s in the washing machine, the drying rack, or the laundry basket—it only suggests clothes I have ready to go. No more mental gymnastics required to look sharp for any occasion. The outfit suggestions aren’t perfect. Sometimes it’s predictable and suggests outfit combinations I already had in mind; sometimes, it’s oddly creative. Regardless, it saves time and reduces decision fatigue.
What’s next for the project
Smarter outfits, weather picks, and maybe a fully self-aware wardrobe
Right now, my laundry tracker is a simple web app, but there’s plenty of room to expand. I’m already considering these expansions:
- Weather integration: Pulling in local weather forecasts to suggest weather-appropriate outfits.
- Multi-user support: This would let users track multiple wardrobes.
- Laundry scheduling: Automatically suggests wash cycles when too many items are marked dirty.
- Smart mirror mode: Displaying outfit suggestions on a connected screen.
- Outfit generation improvements: Implementing a full chat system into the outfit generator.
What started as a weekend experiment is now a genuinely useful system that saves me the stress of rummaging through piles of clothes—only to realize what I wanted to wear is in the washing machine yet to be dried. It helps me avoid a lot of mental gymnastics, and often helps me dress better.
Automating laundry tracking might sound absurd, but it’s the perfect example of how AI can make small, everyday problems go away. You don’t need a massive cloud model or subscription service to build something genuinely helpful for yourself. Just throw some affordable tech like NFC tags with local AI, and suddenly, one of the dullest chores becomes fun. It’s proof that smart homes don’t always need to be complicated.
Sometimes, all you need is a way to remember where your favorite hoodie is and whether it’s clean. And honestly? This is the kind of secure, low-cost home automation I can get behind.
