I Built a Human-in-the-Loop SDK Because My AI Spent $5K Guessing

I Built a Human-in-the-Loop SDK Because My AI Spent $5K Guessing A buddy at Meta texted me last week: "Dude. I spent $5K on tokens this week. We had a massive problem... I feel like I peered into the matrix using AI." Five thousand dollars. In one week. On tokens. He's not alone. Every team running AI in production right now is discovering the same thing: the AI gets 80% right, but the other 20% costs you more than the first 80% combined. Retries, hallucinations, edge cases, judgment calls the model isn't equipped to make. That 20% is where the money burns. The problem nobody's solving Here's what happens in most AI pipelines today: response = claude . messages . create ( model = " claude-sonnet-4-6 " , messages = [{ " role " : " user " , " content " : prompt }] ) # Hope it's right # If it's wrong, retry (burn more tokens) # If it's still wrong, a human manually fixes it via Slack # Nobody tracks the cost of this loop The AI produces output. You hope it's correct. When it's not, someon