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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- 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.
- Tool Choice & SetupCC Switch Setup: Verify a Provider Change and Switch BackConfigure one provider in CC Switch, verify requests and billing records, and test restoring your previous Codex CLI configuration.
- Features & TroubleshootingCodex Permission Mode Unavailable: A Four-Layer Diagnostic ChecklistCheck your Codex environment, workspace scope, file and network boundaries, approval policy and managed profiles before changing settings.