I was working on my app builds and ran into the same wall again.

Every time I let the AI write the app and connect the APIs, something breaks.

There are errors with authentication, parameters, and responses that don’t match.

I spend hours checking if the issue is in the code or in the API.

The debugging takes away the whole rhythm of building.

That’s when I realized I was approaching it backwards.

I was trying to integrate before I even knew what worked.

So I changed how I build.

Now I start every idea by thinking of the data flow first.

I imagine the app in terms of how data moves between APIs.

I write down every API that might be part of that flow.

Once I have the list, I don’t write any code.

I open the terminal and begin curl-testing them one by one.

Each API gets tested for headers, payload, authentication, and response format.

If the request succeeds, I save the exact curl command and its response.

If it fails, I note down what happened and move on.

After going through all of them, I end up with a clear document.

That document shows which APIs work, what data they return, and how they behave.

It becomes my personal catalog of verified building blocks.

Once that’s ready, I give it to the AI powered IDE.

I tell it exactly which APIs passed the tests and what their structures are.

Then I ask it to integrate those APIs into the app.

This approach makes the process clean from the start.

The integrations work as intended because they come from verified sources.

The app now builds on a tested foundation instead of uncertain endpoints.

I can track every part of the workflow because I’ve seen each response myself.

The process itself becomes predictable and easy to expand later.

Over time, this method forms a growing library of trusted APIs.

Each new project starts with a set of working pieces ready to use.

I call this my curl-test method.

Here’s the workflow I follow now:

1. Ideate the app.
   Start with the goal: what’s the value or automation you want to create?
2. Brainstorm the APIs.
   List every endpoint that can help you achieve that — data sources, enrichment tools, communication APIs, anything that moves information forward.
3. Map the workflow.
   Sketch the logical flow: which API calls feed into others, and what the data looks like at each step.
4. Curl-test each API.
   Use curl to manually test each endpoint before writing a single line of app code.
   Check authentication, headers, parameters, and sample responses. Confirm that the data looks as expected.
5. Document the results.
   For every tested API, note whether it worked, the sample response, and any quirks (rate limits, field names, edge cases). This document becomes your validated API catalog.
6. Build the app using that document.
   When you or an AI start coding, use the validated API doc as the source of truth. The app then becomes a simple orchestrator of reliable components.

Why It Works

This approach flips the development process from “guess and debug” to “verify and assemble.”
It creates clarity, eliminates integration ambiguity, and turns the AI into a reliable builder rather than a blind guesser.

Over time, your validated API docs evolve into a reusable personal library — a catalog of proven integrations ready for new projects. You can plug them into different workflows, mix and match endpoints, or hand them off to AI with zero uncertainty.

The Takeaway

If APIs make up most of your product’s value, treat them like the foundation they are.
Curl-test first, build later.
It’s a small shift that turns development from trial-and-error into structured engineering — and it’s the simplest hack I’ve found for scaling API-based apps fast without breaking things.