I’ve been wanting to test Xano 2.0 for a while now, I did test the previous version a long time ago but I heard only good things about the 2.0 update. But here’s the thing: I hate building test projects just for the sake of learning a tool.
You know those tutorial apps? “Build a Todo List!” “Make a Blog!” They’re fine for learning syntax, but they don’t stick. You finish them and immediately forget everything because you never actually use them.
I wanted something real. Something that would force me to actually explore Xano’s capabilities while also solving a problem I genuinely have.
So I thought about my game dev workflow and what annoys me most on a daily basis.
The Problem
Here’s the thing about game development, you accumulate stuff.
Audio files with names like SFX_UI_Click_02_v3_final_ACTUALLY_FINAL.wav. 3D models buried six folders deep. Images scattered across Google Drive, and that external hard drive you can’t find right now.
And the worst part? You can’t search for what you actually need.
Scrolling through files. Opening random sounds. Playing them one by one. “Was it this one? No… maybe this one? No…”
And I thought: “Can’t I just make this whole process a bit easier ?”
Perfect. This would be my Xano test project.
I’d build an AI-powered asset library that actually understands what my files are. And I’d learn Xano in the process.
Two birds, one stone.
The Idea
The concept was simple: What if every audio file I uploaded was automatically analyzed by AI and categorized in a way that actually made sense?
Not just metadata like “duration: 2.3 seconds” or “format: WAV.”
But real, useful descriptions like:
- “Smooth, upward-sweeping magical transition sound”
- “Short, satisfying UI click with medium intensity”
- “Airy, fantasy-themed whoosh”
And then, what if I could just talk to my library?
Instead of searching for filenames, I could say: “Show me some satisfying chill UI sounds.”
And the system would just… get it. Because it understands what “satisfying” means. What “chill” means. What these sounds actually feel like.
That’s semantic search.
This project would let me test:
- Xano’s database for storing metadata
- Xano’s file storage for audio files
- Xano’s API builder for creating endpoints
- Xano’s function stack for integrating external APIs (Gemini and OpenAI)
- Xano’s authentication for user management
Basically, I’d touch every major feature. Perfect learning project.
The Build
The project took me pretty much one day to build from the ground up, there was some iteration with Gemini and Claude on the whole architecture, then I setup the Xano backend with integrations with Gemini and OpenAI, built the frontend with the help of Cursor and finally I uploaded the files. But here is a more detailed explanation.
The Stack
Frontend: React Because I know it, it’s fast to prototype with, and honestly, it’s just comfortable at this point. And with the help of Cursor it’s even faster.
Backend: Xano 2.0
This was the whole point. Xano handled:
- Database schema and tables
- File storage for audio uploads
- All API endpoints
- User authentication
- Background functions for AI processing
- Integration with external APIs
No server setup. No DevOps. No wrestling with database migrations or authentication middleware. Just… build.
AI Analysis: Google Gemini 2.5 Flash
I send audio files to Gemini, and it comes back with structured analysis:
- Sound name
- Primary category (Magic, UI, Combat, etc.)
- Rich description
- Technical tags (one-shot, whoosh, transition, etc.)
- Perceived materials (Magic, Air, Metal, etc.)
- Intensity level (Low, Medium, High)
Semantic Search: OpenAI Embeddings
After Gemini analyzes each sound, I convert that description into an embedding, basically a vector that represents the “meaning” of the sound in mathematical space.
When I search, my query gets converted to an embedding too. Then the system finds sounds whose embeddings are closest to my query’s embedding.
Translation: It finds sounds that mean similar things to what I asked for.
The Flow
- Upload: Drag and drop an audio file into the web app
- Storage: Xano saves the file and creates a database record
- Background Processing: Xano triggers a function that:
- Sends the file to Gemini for AI analysis
- Formats the analysis into searchable text
- Calls OpenAI to generate an embedding vector
- Stores everything in the database
- Search: Type natural language queries, get semantically relevant results
The whole process takes maybe 5 seconds per file. And then it’s searchable forever.
What I Learned About Xano
The Good:
- Speed: I had a working backend in hours, not days
- Function Stack: Being able to call external APIs (Gemini, OpenAI) directly from Xano was great
- No Infrastructure: Zero time spent on deployment, servers, or scaling concerns
- Built-in Auth: JWT authentication out of the box
- Vector Support: Xano 2.0’s vector fields made semantic search possible without additional databases
The Challenges:
- Learning Curve: The visual function builder and the many features it provides can take a bit of time getting used to
- Debugging: Error messages could be more helpful
But honestly? For a day-one project, I’m impressed. Xano did exactly what it promised.
Why This Matters (Beyond My Personal Laziness)
Here’s the bigger picture: This workflow isn’t just for audio files.
The same approach could work for:
- 3D models: “Low-poly medieval weapons” or “sci-fi spaceship assets”
- Images: “Pixel art forest backgrounds” or “hand-drawn character portraits”
- Textures, animations, entire scenes…
Any creative asset that’s hard to name but easy to describe? This system could handle it.
For game developers, this is huge. Our work is inherently visual, auditory, and experiential. We think in moods and feelings, not filenames and folder structures.
Having tools that understand creative intent, not just file metadata, changes the game.
And being able to build something like this in a day? That’s what makes it accessible. You don’t need a team or months of development. You can just… do it.
What I Learned
1. Pick learning projects that solve real problems
Building this was way more motivating than any tutorial could be. I wanted it to work because I’d actually use it.
2. Modern AI is really good at understanding media
Gemini’s audio analysis blew me away. It doesn’t just detect technical properties, it understands context. It knows what sounds “magical” or “satisfying” or “intense.”
3. No-code/low-code platforms are underrated
I used to think platforms like Xano were for non-developers or simple apps. I was wrong. They’re for anyone who wants to ship fast without infrastructure headaches.
4. You can build a lot in a day if you scope ruthlessly
I didn’t try to build the perfect system. I built the minimum viable version that solved my immediate problem. That focus is what made one day possible.
What’s Next?
Right now, this is just for me. A personal tool to make my life easier.
But I’m thinking about expanding it:
- 3D models and textures (because those folders are even messier)
- Better categorization with custom taxonomies for different asset types
- Collaboration features for team projects
Or maybe I just keep it simple and personal. We’ll see.
For now, I’m just enjoying the fact that I can type “chill magical transition” and actually find what I need in 10 seconds instead of 10 minutes.
And I know Xano well enough now to build my next idea without hesitation.
The Takeaway
If you’re looking to learn a new tool or technology, don’t build a Todo app.
Build something you actually need. Something that solves a real problem in your workflow.
You’ll be more motivated. You’ll learn more deeply because you’ll hit real-world challenges. And you’ll end up with something useful instead of another abandoned tutorial project.
And honestly? Modern tools like Xano, combined with AI APIs, have made it absurdly easy to go from idea to working prototype. You don’t need a team. You don’t need months. You just need to be annoyed enough at a problem to actually build something.
That’s the future I’m here for.