After two weeks of tinkering, I built an automated AI content pipeline—here are the pitfalls I encountered

Lately I’ve been running more and more accounts, and there’s no way I can feed all those content slots by myself, so I started thinking—could I just let the machines generate stuff on their own? Here’s the rough workflow I’ve been testing: using Python’s transformers library to hook into pre-trained models on Hugging Face for text generation, then setting up a scheduled script with GitHub Actions.

Once it finishes, it sends me an email notification. I also plugged in the Google Trends API to check what people are searching for lately, so the generated content doesn’t stray too far off-topic. For the data side, I used matplotlib to whip up a simple chart to track trends.

The most useful part, honestly, was the quality scoring module I added at the end—it filters by coherence and relevance. Otherwise, purely generated stuff reads like cheap plastic, and if I sent that out, I’d get roasted. Still tweaking it now—the output is barely usable, but it’s a long way from being fully hands-off.

That GitHub Actions scheduled runs thing is legit, free tier quota is more than enough.

Quality scoring is the real make-or-break step, otherwise the stuff you generate is just straight-up unwatchable.

Yo, has the Trends API ever returned empty for you guys? I got hit with that once, anyone else run into this?

You can still smell the AI-generated vibe even when it comes straight from the pretrained model.

Oh nice, the email notification feature is a solid touch, saves me from constantly refreshing the page.

I’m working on something similar too, been stuck on the evaluation module step and can’t get it tuned right no matter what.