An engineering notebook on AI, systems, and everyday experiments.

探索用生成式AI解决天文图像中CMOS灰尘去除难题。通过让AI生成灰尘遮罩而非直接修复图像，结合确定性数学平场校准，实现既保留科学严谨性又获得AI强大效果的工作流。

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分析五年时间记录，揭示出"多线程人形算力节点"的生活模式——在带娃、工作和构建AI工具之间平衡，不自觉地走向人机融合的修仙之路。

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.

从拼凑到整体渲染的PPT制作范式转移，以及如何用代码骨架、资产约束和延迟渲染构建解决风格混乱、幻觉和成本问题的Generative Kernel。

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.

观察到当代码成本趋近于零时，为一次性决策构建专用工具反而是最优策略。提出AI Native的本质是在信息获取成本坍塌后采用全新策略——从直觉驱动转向高分辨率的数据驱动决策。

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.

提出用户生成软件需要新的范式。软件公司不再交付成品，而是交付"生成内核"——包括核心套件、引导知识和杠杆工具集，专为AI使用而设计，最大化表达范围、意图保真和生成效率。

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.