Concise monthly summary for 2026-01 focusing on business value and technical achievements. Key feature delivered: Quantum Kernel Pre-screening Demo for PennyLaneAI/qml, implementing a geometric difference metric to pre-screen quantum kernels before training. The demo supports multiple kernel variants (fidelity-based and projected) and uses synthetic two-moons data to illustrate how the g metric guides kernel selection prior to costly training runs. No major bugs fixed this month; minimal stability and wiring tweaks were performed to ensure demo reliability. Overall impact: accelerates kernel evaluation cycles, helps researchers avoid unproductive kernel configurations, and provides a practical, reproducible workflow for assessing quantum kernel potential. Technologies/skills demonstrated: Python-based demo development, geometric difference metric implementation, kernel methods evaluation, data generation (synthetic datasets), collaboration and co-authorship, and release-ready demo packaging.