Explorations in aerospace engineering, AI/ML, computational fluid dynamics, and the future of technology.
Modern design pipelines optimize external shapes and structures — often with simulations trained on simulations — while the systems integration, certification, manufacturability, and envelope-corner knowledge that turns a design into a program sits below the waterline, carried in the open literature and in the engineers and organizations that earned it.
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A candid post-mortem of an LLM-run smoke-tunnel validation — the confidently-wrong drag number, the 0 m/s artifact, the failed vision detection — and why setup, execution, and interpretation still demand careful human hands.
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A fluids engineer directing an LLM through natural-language conversation produced a complete OpenFOAM simulation, parametric shape optimization, and automated ParaView visualization — on a bear geometry.
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Physics-informed neural networks trained on 1959 wind tunnel data capture missing physics that traditional CFD simulations miss entirely — on a single-board computer.
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How AI-powered development enabled the creation of SkyView Pro, a comprehensive real-time geospatial platform using only open-source data.
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Smoke flow visualization, optical flow velocity vectors, and SINDy analysis on a 1:64 scale car model — all from a small wind tunnel at home.
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Why specialized tools developed by engineering firms over decades often outperform modern flashy alternatives that deviate from real-world physics.
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A tool for precise analysis of movement in video using Dlib — for loading videos, calibrating measurements, selecting regions of interest, and deriving velocity and displacement data.
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Integrating advanced computer vision (Optical Flow) with data-driven modeling (SINDy) to understand and predict fluid dynamics from video data.
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How a tiny energy-efficient powerhouse became the brain of a home automation system with Home Assistant, AI vision, and media serving.
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