Building AI Agents? The LLM isn't your bottleneck—Data Ingestion is.

#ai #api #automation #indiedev

As an indie developer, I’ve spent the last few months deep in the trenches building automation workflows and experimenting with different AI agent frameworks.

But the further I got, the more I hit a frustrating wall.

Models are incredibly smart now, but if you can't feed them clean, real-time external data, they're practically useless.

The "Dirty Work" of AI Development

This became painfully obvious when I needed to pull social data—specifically from X/Twitter (like targeted follower lists, engagement metrics, or structured comment threads)—to feed into my AI for context analysis.

Here is what usually happens:

The Official Route: The official API is prohibitively expensive for indie hackers and side projects.
The Custom Scraper Route: Maintaining custom scrapers means fighting an endless, exhausting war against rate limits, proxies, and anti-bot systems.
The Data Format Issue: Even if you get the data, it's usually a messy HTML/JSON soup that eats up your LLM's context window and leads to hallucinations.

Scratching My Own Itch

I got so tired of this "dirty work" stalling my core application logic that I paused my main project to just build the infrastructure I wished existed.

My goal was simple: abstract away all the complex scraping and proxy routing, and just deliver clean, structured data.

I recently finished packaging it up, and it now fully supports CLI commands and direct AI tool calling. This means you can now seamlessly plug it directly into Cursor, Claude, or your custom agent scripts without writing a single line of scraping logic. It outputs clean JSON or CSV, ready for your LLM to digest.

If you’re also building AI tools and banging your head against the wall trying to ingest X data reliably, you can check out the infrastructure I built here: Twexapi.