We solved reasoning. The remaining challenge was apparently pressing Enter.

Every week I see discussions about more capable models.

Better reasoning.

Better coding.

Longer context.

More autonomy.

Meanwhile most real-world AI workflows still look like this:

AI works.

Human clicks continue.

AI works.

Human clicks continue.

Repeat until boredom wins.

I became curious how much of that friction was actually necessary.

So I built Ghost in the Loop.

It's an open-source project that automatically continues multi-step AI conversations across major AI platforms.

What's interesting isn't the automation itself.

What's interesting is watching how far current models can go once the conversation isn't constantly interrupted.

Sometimes the results are impressive.

Sometimes they're complete disasters.

Both are useful data.

I'd love feedback from people who spend time thinking about AI systems and human-in-the-loop design.

Questions I'm exploring:

• Where should autonomy stop?

• Where should humans stay involved?

• What tasks benefit from longer loops?

• What tasks become worse?

GitHub:

TL;DR

I built a tool that removes one layer of human intervention from AI workflows.

Now I'm trying to figure out where that becomes valuable and where it becomes a mistake.