Complete archive of published writing across all domains.
ALAN SCOTT ENCINAS CASE STUDY · LUNARSITE (FULL PIPELINE) LunarSite: An end-to-end ML pipeline for lunar south pole landing site selection Sim-to-real terrain segmentation, fine-tuned crater detection, PSR-aware XGBoost site scoring, and calibrated uncertainty — validated against NASA’s Artemis III…
I built an AI agent that reads my CRM emails and scores leads for $0.01 each It scored 1,051 leads in one run for $15.07 — and surfaced $82K+ in pipeline I didn’t know I had. The problem I run…
People who think first and use AI to accelerate gain leverage. People who let AI think for them accumulate cognitive debt. Research from MIT and enterprise studies shows the divide is already forming.
AI is not uniformly enhancing or degrading human intelligence. It is redistributing it. As tools absorb certain forms of reasoning, humans reallocate effort toward higher-level synthesis, judgment, and coordination. This shift creates a bifurcation in cognition itself: some skills atrophy while others accelerate. Understanding this split is essential to designing systems that augment rather than erode human capability.
The IEA estimates global data center electricity consumption at 415 terawatt-hours in 2024, projecting growth to 945 TWh by 2030. This paper argues that achieving sustainable cognitive AI requires simultaneous optimization across three interdependent layers: neuromorphic hardware, software algorithms, and governance frameworks.
From Project Mercury's desperate survival missions to Artemis learning to build lunar infrastructure—exploring how space exploration evolved from fear-driven engineering to the discipline of endurance and continuity.
The commercial drone industry faces a critical scalability bottleneck: the "1:1 Ratio," where increasing fleet size linearly increases human cognitive load. COV—Cognitive Orchestration & Vision—is a novel architectural framework that decouples mission intent from flight execution, shifting the human role from "pilot" to "supervisor."
Years ago, I watched Prometheus and fixated on a small moment most people glossed over. Inside the alien structure, two spherical drones fly autonomously through dark, unknown corridors, scanning, mapping, and reconstructing the interior in real time. The scene wasn't flashy. It was practical. And it planted a thought that never quite faded: Why isn't this real yet?
There's a growing assumption right now that building a national missile shield like the Golden Dome is a largely solved engineering problem—expensive, ambitious, but fundamentally understood. That assumption is wrong. What's being underestimated isn't hardware or physics, but cognition at scale.
For most of the last century, hospitality scaled by height. Towers, elevators, concrete, and controlled environments. But the market is changing. What's growing now is a different model of development—one that treats land, culture, and community as the core product.
Building real-time fraud detection for financial systems: 92.6% accuracy with just 13 features and sub-millisecond inference. Through aggressive feature selection (435 features → 13), logistic regression proved that production-ready ML doesn't require complexity—it requires smart fundamentals and clear constraints.
How a flower classification model became a medical diagnostic tool for underserved communities. Using transfer learning with InceptionV3, we built a pneumonia detection system that achieved 73% validation accuracy and increased screening throughput by 340% at UCI Medical's community health center. Real deployment, real impact, real constraints.
