This is exactly why we believe in customization. Quick context: Factory's original secret scanner was deterministic, so it either flagged things that weren't actually secrets (false positives) or missed secrets that didn't match its patterns (false negatives). Their solution was elegant: two small post-trained models—one to catch missed secrets and another to filter out false alarms. The result: models that outperform GPT-5.5 and Opus 4.8 on this specific task while running at a fraction of the cost and latency. This is our thesis in action: take a strong open model, post-train it for a specific production problem, and you can build something that's faster, cheaper, and better than frontier models at that job. Congrats to @FactoryAI. Proud they built Droid Shield 2.0 on @FireworksAI_HQ.