Factory fine-tunes open Qwen on Fireworks, catching ~20% more secrets than GPT-5.5

Fireworks spotlighted Factory's Droid Shield as a Training API customer story. Factory's droids write and commit code faster than human reviewers can keep up, so secret detection has to catch real leaks without flooding the team with false alarms. Using the Fireworks Training API, Factory fine-tuned an open Qwen model that caught almost 20 percent more real secrets than GPT-5.5, at lower cost and latency. The result is a specialized scanner sitting in the commit path rather than a generic frontier chat model asked to grep for keys. For coding-agent vendors, it is a concrete example that a domain-fine-tuned open-weight model can beat a larger closed model on a verifiable production task, then ship at a cost that matches high-frequency agent traffic. It also shows why training and inference on one platform matters: the specialized weights have to stay cheap enough to run on every commit.