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FortyGuard Heat Alert Bot — Hyper-Local Heat Warning System
Built an AI-powered heat alert and microclimate monitoring system as part of FortyGuard Hackathon'26, earning a Certificate of Demonstrated Excellence for technical excellence and innovation. The system delivers hyper-local heat warnings by processing real-time environmental data and using AI to detect risk patterns, then automatically triggers alerts to keep people informed and safe during extreme heat events.
Tools used: n8n, AI/LLM integration, API integration
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GROOOMY 🐶 Smart booking for a one-woman dog groomer
Grooomy is a booking app for Marloes, who runs a dog grooming atelier on her own. Customers book in about a minute, and the app reserves exactly the table time each dog needs.
What I built: Every groom takes a different amount of time. A Chihuahua takes an hour, a matted Bernese takes four. Marloes used to plan every appointment over WhatsApp: nine messages, two days and the occasional no-show. Grooomy fixes that:✅
✅ Customers describe their dog (size, coat, tangles), and the duration and price are calculated automatically.
✅ Only time slots where the full groom fits are shown, so there are no double bookings and no gaps too short to use.
✅ Returning customers are recognised by phone number, and new customers pay a small deposit.
✅ Customers can change or cancel with one tap.
✅ When a day is full, dogs join a waitlist, and a cancellation is offered to the next matching dog automatically.
✅ Marloes gets a dashboard with her day at a glance, the week, the waitlist, customers and every automated message.
How I built it: I started with the scenario and design research, designed the brand and every screen, then turned each screen into a precise prompt for Lovable. The app runs on Lovable Cloud with a real database for bookings, customers and the waitlist. The design process video is on the Story page.
CHECK IT OUT: https://grooomy.lovable.app
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Worked really hard on this and loved working with Lovable for the first time! Let me know what you think! 🔥
Built MessyDev, a hands-on AI learning platform that teaches users what AI is doing while they build. Interactive lessons and real-world exercises cover AI, APIs, databases, deployment, debugging, and shipping software—turning passive learning into practical experience.
"Businesses lose time and money to invoice errors that go unnoticed until it's too late. I built an automated pipeline that extracts invoice data, cross-checks it against vendor history, and flags anomalies with clear AI-generated reasoning — all visualized in a real-time dashboard. Stack: LangChain, Groq, n8n, FastAPI, Next.js."
