A few days ago, I decided to stop just using AI and finally try building one myself.
No full roadmap.
No perfect plan.
Just curiosity and the willingness to figure things out.
What followed was a mix of confusion, frustration, small wins, and one big realization:
Building AI is way harder—and way more rewarding—than it looks.
💡 Why I Started
I’ve been using AI tools for a while, like most developers. But at some point, I kept wondering:
How do these models actually connect?
What’s happening behind the API calls?
Can I build something like this myself?
Instead of watching another tutorial, I decided to just start building.
⚙️ The Stack (What I Used)
I kept things simple (or at least I tried to):
AI Models via API (LLMs)
A basic frontend interface
Deployment on a cloud platform (like Vercel)
Lots of trial and error 😅
Nothing fancy—but enough to build something real.
😵 The Problems I Faced
Let’s be honest: things broke. A lot.
Some of the errors I ran into:
API Error: No endpoints found for openchat/openchat
API Error: No endpoints found for mistralai/mistral-7b-instruct
API Error: No endpoints found for google/gemma-7b-it
At first, I thought I messed up everything.
Turns out:
Some models weren’t available
Some endpoints were incorrect
Some configs were just… wrong
This is the part no one talks about enough.
🧠 What I Learned (The Real Stuff)
- Not All Models Are Plug-and-Play
Just because a model exists doesn’t mean you can use it instantly.
You need:
Valid endpoints
Proper API providers
Correct configurations
- Debugging Is the Real Skill
Most of my time wasn’t spent building—it was spent fixing.
And that’s where the real learning happened.
- Deployment Is a Different Game
Running something locally is easy.
Deploying it?
That’s where things get real:
Environment variables
API keys
Build errors
Runtime issues
- You Don’t Need to Know Everything
I didn’t fully understand everything when I started.
And that’s okay.
You figure things out as you go.
🚀 The Result
After all the chaos, I finally had:
A working AI app
Live deployment
Real responses from the model
It wasn’t perfect—but it worked.
And honestly, that’s enough for version 1.
🔥 If You’re Thinking of Building AI…
Here’s my advice:
Start before you feel ready
Expect things to break
Don’t trust every tutorial blindly
Learn by doing, not just watching
💬 Final Thoughts
This wasn’t just about building AI.
It was about:
Learning how systems actually work
Dealing with failure
Staying consistent when things don’t make sense
And most importantly:
Real growth happens when you stop consuming and start creating.
If you’re building something similar or stuck somewhere, feel free to reach out—always happy to connect with fellow devs 👨💻
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