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λλλλλ in Action

Explore working demonstrations of λ-calculus patterns, type-safe stream processing, and quantum-inspired architecture.

🚀 Try Examples

Core Concepts with Examples

Data Flow

Composable pipeline processors using quantum-inspired parallelism 


pipe(
    fromEvent("mousemove"),
    filter(e => e.clientX > window.innerWidth/2),
    debounceTime(16),
    map(e => ({x: e.clientX, y: e.clientY, quadrant: "right"})),
    toSink(updateDashboard)
)

Interactive mouse tracking with filtering and transformation

Type Inference

Automatic type resolution using category theory patterns 


const result = pipe(
    ["red", "green", "blue"],
    map(str => str.toUpperCase()),
    filter(s => s.length > 4),
    reduce((a,b) => a + " → " + b)
)
// Type inferred as: string

Complete type safety across chained operations

System Architecture

Architecture diagram showing core components: Core Engine, Reactive Systems, Type Inference, Distributed Layer and Client Layer with quantum-inspired connections

Our framework combines a type-safe core with quantum-inspired reactive patterns. The diagram shows:

  • • Core Engine for type-safe function composition
  • • Parallel execution pipelines
  • • Distributed processing architecture
  • • Real-time type inference system

Advanced Use Cases

Distributed Processing


const cluster = new FlowCluster({
    workers: 8,
    strategy: "adaptive"
})

pipe(
    fromEventStream("data-stream"),
    partitionBy(record => record.userId),
    map(rebalance),
    runOnCluster(cluster)
)

Quantum Pattern Matching


match(value): {
    case Even.n: return `Value is even: ${n}`
    case Odd.n: return `Value is odd: ${n}`
    case Int.x if x>1000000: return "Large number"
    _: return "Unknown type"
}

Ready to Build?

Our examples show just the tip of the iceberg. Start building your next project with type-safe functional patterns and quantum-inspired performance.

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