Using AI Tools
Understand inputs, settings and limits, then work through practical tool problems.
Choose tools for your materials and troubleshoot their use
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Can Claude Watch Videos? A Transcript-and-Frame Workflow
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Developer Foundations
- Developer FoundationsLibrary vs. SDK vs. Framework vs. API: Who Calls Whom?Compare libraries, SDKs, frameworks, and APIs by tracing who calls whom, how data moves, and where errors occur in a runnable Python exercise.
- Developer FoundationsFrom a Python Script to a Small Project You Can RerunStructure a small rerunnable Python project with an entry point, input, helper module, JSON persistence, and README, then verify the result from a fresh process.
- Developer FoundationsHow Does Python Process Data? From Variables and Containers to Conditions and FunctionsTrace Python data from values and types through containers, conditions, loops, functions, and return values so you can predict behavior, locate failures, and verify output.
- Developer FoundationsWhere Is Python Actually Running? Understanding Anaconda, Jupyter, Terminal, and EnvironmentsTrace Python from interface and shell to interpreter, kernel, environment, packages, and working directory so you can diagnose common setup failures with evidence.
- Developer FoundationsIs a Longer Context Always Better? From Tokens and Context Windows to Context EngineeringLearn how tokens, context windows, state, memory, and context engineering fit together—and why better-selected context matters more than simply adding more tokens.
- Developer FoundationsPrompt Is Not Magic: Structured Output, Tool Calling, and NondeterminismLearn how prompt constraints, Structured Output, Tool Calling, temperature, top-p, and invariant-based testing turn an LLM into a more reliable AI application component.
- Developer FoundationsClaude Code Runs an Agent Loop, Not Better AutocompleteClaude Code cycles through planning, execution, and verification. Learn how that agent loop changes task definition, feedback, and collaboration with the tool.