English

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.

Shares how a 3-minute voice prompt led to AI completing a senior scientist's full-day work. Details five management principles—hiring, task delegation, onboarding, process guidance, and product acceptance—that unlock AI's full potential.

Why AI slacks off on large tasks: LLM output length limitations cause attention drift. Wide Research solves this by parallelizing with lightweight models, then aggregating results with a primary LLM.

A deep dive into EV vs ICE car chassis engineering, exploring different philosophies: ICE cars shape character through strategic freedom, while EVs fight physics with tactical technology.

Analyzing how OpenAI's Apps SDK extension with _meta field violates MCP's design philosophy, creating dialects that may fragment the standard like SQL or CSS.

Addresses common AI frustrations by reframing them as management problems. When we treat AI as an intern rather than a tool, issues like unreliability, hallucinations, and code quality become solvable through established management principles like trust-building and hierarchy.