🎃 FixIt AI — How I Built a Full AI Repair Assistant in One Week Using Kiro

Alvin Messiwotso Akaba — Thu, 04 Dec 2025 03:14:11 +0000

Device repair shouldn’t feel like a horror movie.
But for millions of people, especially in regions like Ghana where I live, one broken phone or laptop can instantly kill productivity, communication, or even schoolwork. Repair shops are expensive, diagnostics are inconsistent, and simple issues often turn into costly tech graves.
During Kiroween, I decided to build something small but powerful:
A spooky, ghost-themed AI assistant that diagnoses device problems instantly — and saves people money.
This is the story of how I built FixIt AI in less than a week, with Kiro as my coding partner and creative engine.

👻 Inspiration — When a Simple Idea Meets Real Need
I didn’t build FixIt AI because it was “cool.”
I built it because I needed it — and people around me needed it too.
Where I live, many device issues (slow laptop, flickering screen, draining battery) don’t actually require a technician. But the knowledge gap means people pay every time. As a 17-year-old developer passionate about solving real problems, I wanted to build a tool that:
• empowers users with instant diagnostics
• reduces unnecessary repair costs
• helps young people and students avoid “tech emergencies”
• uses AI to make hardware more accessible
Kiroween gave me the creative angle:
a funny, spooky ghost assistant that “haunts away” your device problems.
FixIt AI was born.

🛠️ What FixIt AI Does
FixIt AI analyzes symptoms like:
“My laptop is overheating”
“My phone won’t charge”
“My microwave is sparking”
Then generates:
• structured diagnosis
• severity rating
• estimated cost
• whether a technician is needed
• beginner, intermediate, and advanced repair steps
• safety warnings
• device lifespan notes
• probability of success
• spooky flavor text
• and a glowing ghost animation while processing
Everything runs on:
Frontend: Netlify (HTML, CSS, JS)
Backend: Node.js + Express on Render
AI logic: Kiro-assisted diagnosis engine

⚙️ How I Used Kiro to Build FixIt AI
This hackathon wasn’t just about using AI — it was about using Kiro specifically, and I made sure to use it throughout my build.
Here’s exactly how:

🟣 1. Vibe Coding — My Main Workflow
I built FixIt AI conversationally, using Kiro like a pair-programming teammate.
Kiro helped me:
• rewrite complex frontend functions (ex: showResult() — twice!)
• design the multi-tier repair instructions structure
• write spooky-themed UI text and animation concepts
• debug deployment errors and Express routing issues
• build the diagnosis engine logic
• optimize JSON responses
• refine prompts for better reasoning
This was the most useful part of Kiro for me — real-time iteration, line-by-line improvement, and creative coding.

🟣 2. Spec-Driven Development
Before coding the engine, I defined:
• required JSON structure
• expected fields (severity, probability, steps)
• multi-layer outputs
• UI expectations
• error-handling behavior
I handed this spec to Kiro and had it generate consistent logic.
This made development much faster and prevented contradictory outputs.

🟣 3. Steering
A lot of my prompts were steering instructions:
• “Rewrite this completely to be more professional.”
• “Improve the output, make it spookier but still readable.”
• “Make this judge-friendly.”
• “Simplify; now restructure it; now enhance it.”
Each round, Kiro became more aligned with what I needed.

🟣 4. Rapid Prototyping & Debugging
Kiro helped me solve:
• CORS errors
• Render’s Node version failure
• Express incorrect dependency path
• Frontend / backend connection mismatch
• CSS animation placement issues
This alone saved hours.

🟣 5. Creativity + User Experience
Kiro also shaped:
• the ghost loading animation
• the glowing UI
• the spooky diagnostic responses
• problem-to-solution storytelling
• architecture diagrams
• day-by-day plan
FixIt AI ended up looking more polished than anything I expected to finish within a hackathon.

🚀 What I Learned
• How to combine AI reasoning with traditional backend logic
• How to structure complex JSON outputs
• How to deploy full-stack apps (Netlify + Render)
• How to design a delightful UX under time pressure
• How to work collaboratively with an AI assistant
• How powerful a spec-driven approach can be
• How much Kiro accelerates development compared to coding alone

😅 Challenges I Faced
• Debugging Express on Render
• Designing a spooky theme that still feels usable
• Making outputs structured instead of random
• Rewriting results UI multiple times
• Time pressure — I had only one day to finish everything
But Kiro helped me push through every block.

🎯 Final Thoughts
FixIt AI isn’t just a Halloween-themed app.
It solves a real problem — one that affects real people every day.
Kiroween taught me that even with limited resources, a solo developer can build something useful, beautiful, and impactful when paired with the right AI tools.
This is just the beginning.
I plan to expand FixIt AI into something even bigger — and bring affordable diagnostics to anyone with a device.

🔗 Try it here
Frontend: https://fixit-ai-kiroween.netlify.app
Backend: https://fixit-backend-up86.onrender.com
Code: https://github.com/legendstechgh/fixit-backend

🧡 If you enjoyed this or want to collaborate, connect with me:
LinkedIn: https://www.linkedin.com/in/alvin-messiwotso-akaba-544565398/

👻 Powered by Kiro.

DEV Community: Alvin Messiwotso Akaba

🎃 FixIt AI — How I Built a Full AI Repair Assistant in One Week Using Kiro

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