For Decision Makers

Paradigm

Programming Paradigms

Choose between functional, OOP, reactive, declarative—find the right approach.

The Transformation

Before scenario
(Coming soon)

After transformation
(Coming soon)

See It In Action

Scenario: “Functional vs OOP: Which paradigm fits our data pipeline?”

Functional Programming
TypeScriptFunctional
Problem Context

Data transformation pipeline with complex filtering, mapping, and aggregation across user events.

Paradigm Approach

1Model transformations as pure functions (no side effects)
2Compose small functions into pipelines
3Use immutable data structures throughout
4Leverage higher-order functions (map, filter, reduce)
5Separate data from behavior
Functional Pipeline
const processEvents = pipe(
  filter(isValidEvent),
  map(enrichWithMetadata),
  groupBy(event => event.userId),
  mapValues(calculateMetrics),
  filter(meetsThreshold)
);

const insights = processEvents(rawEvents);
Benefits
  • +Easier to test (pure functions, predictable outputs)
  • +Parallelizable (no shared mutable state)
  • +Composable (build complex from simple)
  • +Debuggable (data flows are explicit)
Limitations
  • -Steeper learning curve for OOP developers
  • -Memory overhead from immutability
  • -Some problems map better to objects
  • -Tooling/debugging can be less mature
Programming Paradigm • Data Transformation

When to Use Paradigm

  • 1
    Choosing programming approaches for new projects
  • 2
    Refactoring legacy code to modern patterns
  • 3
    Evaluating technology stack decisions
  • 4
    Team skill alignment discussions
  • 5
    Performance vs maintainability tradeoffs

Works Well With

Pro tip: Chain Paradigm with Pattern → Decide for comprehensive analysis.

Ready to use Paradigm?

Add Think to your AI assistant in one command

npx @anthropic-ai/claude-code mcp add think-mcp
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