The conceptual foundation of OpenML dates all the way back to 2013 when a college boy in Physics major was reading and implementing the Matrix Numerov Method for Solving Schrödinger Equation

The post "软件行业的 AI 思维" ignited my to study CrewAI whose documentation starts with "Flow", which is why this post was initiated to help me systematically study a concept I had been encountering a lot of times

The conceptual foundations of RAG and its backing vector database rests on decades of research into Approximate Nearest Neighbor (ANN) search, designed to solve the "curse of dimensionality" where exact nearest-neighbor searches degrade to linear scans. As deep learning models like Word2Vec, BERT, and convolutional neural networks began mapping text, images, and audio into dense mathematical embeddings, engineering teams required ways to search these spaces efficiently. This produced the first wave of standalone vector search libraries in the mid-2010s. Around 2019, the industry shifted toward purpose-built systems, popularizing "vector database" as a distinct database category.

Building AI Agent from first principles with a durable understanding that frameworks alone cannot, This post structures a learning path that separates the core theory from the "from scratch" implementation, using resources that are independent of major agent frameworks, such as LlamaIndex.

Just as DevOps brought structure to software deployment and MLOps did the same for training models, AgentOps is the emerging field dedicated to the lifecycle of agentic AI. Shared evaluation frameworks like Harbor is an example of AgentOps. The core philosophy driving this shift is moving away from "walled-garden" testing (where companies build private tests that happen to make their own agents look good) toward Standardized Evaluation Environments. It is the realization that a benchmark is only trustworthy if everyone can run it, inspect it, and use the exact same ruler to measure success.

A deep dive into Whitehead's Process and Reality, replacing classical substance ontology with dynamic occasions of experience, relational prehensions, and the metaphysical foundations of quantum mechanics.