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

This blog post discusses how "Agent Workflow" emerged out of Agent from business practice.

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.