A Stack Overflow question from 2015, a Joel Spolsky essay from 2002, and a year of AI agents deleting things they were told not to touch — read together, they say the same thing: abstractions save you time working, never time learning.
ReAct, Plan-Then-Execute, Search over Actions, Reflection, and Code-as-Action — the control-flow shapes underneath almost every coding agent, research agent, and tool-using assistant you've used
Single-agent loops, multi-agent teams, memory architectures, and integration patterns — a reference map every agent builder now needs, with the two numbers that actually decide whether a pattern belongs in production
Why This Module Exists (Big Picture) Before an AI Agent can act, it must: Understand what the user said Extract useful signals Decide what to do next NLP is the bridge between raw text and agent decisions 1️⃣ NLP Foundations – What & Why Natura...
Introduction We are living in a strange era of AI where a model can write a convincing Shakespearean sonnet in seconds but might confidently tell you that 42 multiplied by 8 is 350. This "paradox of competence" happens because Large Language Models (...
