API Docs Almost Broke Me. So I Taught an AI to Read Them.
Hey IH fam,
For the past year, I've been obsessed with a single problem: why is integrating a new API so damn painful?
My project, Apives, started as a curated list to find reliable APIs. But I quickly realized finding the API is just 10% of the work. The other 90% is spent fighting with its documentation.
I’m talking about:
Docs that are basically a 50-page PDF from 2017.
"Examples" that don't even work.
Ambiguous descriptions that leave you with more questions than answers.
This "documentation hell" almost made me give up. It felt like an unsolvable problem. But then I thought... what if we didn't have to read them at all?
So, I spent the last few months on a crazy experiment: Could I teach an AI to read and understand the worst API docs on the internet, so developers don't have to?
It was hard. Really hard. Teaching an AI to understand human ambiguity is a nightmare. But after countless iterations, it started to work.
Today, this experiment is live inside Apives. It's called Apives AI.
Now, you can find a vetted API on Apives and just ask questions in plain English. No more opening 10 tabs. No more guesswork.
"What does this endpoint do?"
"Show me the JSON for a failed request."
"How do I authenticate?"
Get instant answers.
This isn't just a new feature; it's the result of my fight against bad developer experiences. I'd be incredibly grateful if you could try it out and tell me if all that struggle was worth it.
Help me test the AI that almost broke me -> Apives.com
Does it actually save you time? Where does it fail? Your honest feedback would mean the world.
I totally get that struggle with API docs, it's a huge time sink. I happen to know a few software developers who regularly integrate APIs and would likely be happy to answer your questions about this for free.
This is a very relatable problem — bad API docs are painful.
I took a look at it from a practical angle, and one thing I’d be really curious about:
does it actually replace the way developers work with docs,
or does it end up being more like an additional layer on top?
Because in reality, when integrating an API, people usually:
– scan multiple endpoints
– compare examples
– test things quickly
– and build a mental model of how everything connects
So even if the AI answers individual questions well,
the key question is:
does it reduce the need to open docs,
or do users still go back to them to “verify” things?
Feels like the real win here is not just answering questions,
but replacing that whole back-and-forth between reading and testing.
Curious what you’re seeing so far — are people actually staying inside the AI flow,
or still relying on the original docs in parallel?