Computing Life

Explores using generative AI to remove CMOS dust from astronomical images. By having AI generate dust masks rather than direct corrections, then applying deterministic flat-field calibration, achieves both scientific integrity and AI's powerful recognition capabilities.

AI analyzes five years of time logs and reveals a "multi-threaded human compute node" lifestyle—balancing parenting, work, and building AI tools while unconsciously becoming a human-system hybrid.

Exploring the paradigm shift from assembling slides to holistic rendering with NBP, and building a Generative Kernel workflow to handle visual consistency, hallucination, and cost.

Observes that when code cost approaches zero, building disposable tools for one-off decisions becomes optimal. Argues AI Native means adopting new strategies enabled by collapsed information costs—shifting from intuition-driven to high-resolution, data-driven decisions.

Proposes that User-Generated Software requires a new paradigm. Instead of finished products, companies deliver "Generative Kernels"—core kits, guiding knowledge, and leverage tools designed for AI to use, maximizing expressive range, intent fidelity, and generation efficiency.