Computing & Artificial Intelligence
From foundational machine learning to modern generative architectures: self-attention, token sampling, and autonomous agent loops.
How AI Agents Work
The architectural loop behind autonomous planning, tool execution, and stateful problem solving
Inquiry Learning Paths
Structured sequences designed to take you from foundational mechanics to complex interactions.
From Next-Token Prediction to Autonomous AI Agents
A step-by-step inquiry path tracing how statistical word prediction scales into goal-directed agents that use tools in the real world.
How Large Language Models Generate Text
Foundational mechanism: Next-token autoregressive generation
Why AI Chatbots Sometimes Make Things Up
Failure mode: Statistical plausibility vs. factual grounding
How AI Agents Work
Autonomous architecture: Closing the ReAct tool execution loop
How an AI Agent Decides Which Tool to Use
Decision layer: Schema matching and constrained token decoding
All Published Explainers
Every verified first-principles analysis in this domain.
How AI Agents Work
The architectural loop behind autonomous planning, tool execution, and stateful problem solving
How Large Language Models Generate Text
From raw prompt strings to token embeddings, self-attention calculations, and next-token probability sampling
How Search Engines Actually Work
Web crawling, inverted indexing, PageRank link graphs, and neural vector retrieval across 50 billion pages
How an AI Agent Decides Which Tool to Use
The mechanics of schema matching, token probabilities, and semantic tool routing
Why AI Chatbots Sometimes Make Things Up
The mechanics of hallucination, probability vs. truth, and the engineering behind grounded AI responses
Why Search Results Differ Between People
Geographic IP Anycast routing, session history vectors, freshness ranking, and A/B experiment flight allocation
Inquiry Roadmap & Research Pipeline
Next-order causal questions in this discipline currently undergoing source verification and mechanism synthesis.
“Why Neural Networks Were Invented”
Investigate the conceptual origins and developmental history of artificial neural networks, examining early bio-inspired computational models (McCulloch-Pitts, Rosenblatt), linear separability constraints identified by Minsky and Papert, and the emergence of multi-layer architectures trained via gradient-based backpropagation.
“How Transformers Changed AI”
Examine the architecture of the Transformer model (Vaswani et al.), analyzing how the self-attention mechanism replaced recurrence and convolution, enabling parallel computation over sequence contexts and altering scaling behavior in deep learning.
Technical Systems & Protocols Analyzed
Hardware, protocol switches, and central clearing houses examined in this hub.