15 docs tagged with "foundations"

Precise technical definitions for chatbots, workflows, and AI agents - with decision criteria, cost/reliability tradeoffs, and code examples of all three for the same task.

Build a growth visualizer to observe how algorithms scale with input size and develop deep intuition about performance and computational complexity.

Design a structured ATM simulation to apply computational thinking, state management, and flow control using Python fundamentals.

Build a structured binary exploration tool to understand bit representation, power-of-two logic, overflow behavior, and hardware-level thinking using Python fundamentals.

Design a rule-based chess move validator to strengthen logical thinking, coordinate reasoning, and structured conditional flow using Python fundamentals.

Develop structured problem-solving skills by applying computational thinking principles before writing full Python programs.

Design a state-driven digital pet simulation to strengthen computational thinking, state transitions, and structured logic using Python fundamentals.

Master the foundational concepts of AI agents - what they are, how they reason, how they act, and when to use them.

Design a structured number analysis system that reinforces state tracking, single-pass logic, pattern detection, and computational thinking.

Master the Observe-Think-Act loop that drives every AI agent - from the detailed mechanics of each phase to error handling, backtracking, and token management.

Master the ReAct (Reasoning + Acting) pattern - the 2022 breakthrough that grounds LLM reasoning in real observations and prevents hallucination in agents.

Master how AI agents call tools - from JSON schema definitions to parallel execution, error handling, and the tool design principles that make agents reliable.

Design a traffic light validation system to strengthen rule enforcement, state transitions, and structured conditional logic using Python fundamentals.

Understand precisely what an AI agent is - the definition, the 5 key properties, the taxonomy, and why LLMs finally made agents practical.

A decision framework for when autonomous agents are appropriate vs. when simpler approaches are better - covering cost of agency, task classification, anti-patterns, and ROI analysis.