Command Center 3.2 claims recursive AI fix

Big claim, thin proof: this reads less like a conventional product launch and more like a theory-of-operation wrapped in product language. If the system really reduces drift and apology-shaped deflection across models, the interesting part is not the rhetoric but the reproducible evaluation.

• –The core thesis is coherent: many AI failures do look like early lock-in followed by rationalized output, so a recursion-first architecture is a plausible framing.
• –The feature set maps cleanly to known pain points: drift, sycophancy, compression loss, and corrective feedback handling.
• –The evidence bar is high because the writeup asserts cross-platform deployment and operational results without showing benchmarks, traces, or comparative evals.
• –If real, this is more an overlay discipline than a model breakthrough: a language-layer control system for steering existing LLMs.
• –The strongest signal is the product philosophy, not the branding; the weakest signal is the absence of measurable before/after data.