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Real-Time Streaming

Continuous AI on live data streams

What is it?

Real-time streaming architecture processes continuous data (audio, video, sensor data) as it arrives. Unlike batch processing, you're dealing with incomplete information and need to make decisions before you have the full picture.

The key challenge isn't accuracy—it's timing. With continuous audio, you often don't know when the user is "done." You need two brains: a streaming brain (draft mode) for immediate feedback, and a commit brain (final actions) for durable decisions.

💡 Key Insight

"The hardest part isn't accuracy—it's timing. When do you commit to an action based on incomplete input? Real-time forces you to care about latency, partial results, reconnection, and graceful degradation."

Tradeoffs

Advantages

  • Immediate responsiveness - users see results as they speak/type
  • Natural interactions - feels like real conversation
  • Live feedback - users can course-correct mid-stream
  • Engaging UX - streaming creates anticipation

Tradeoffs

  • Complex state management - tracking partial results
  • Timing challenges - when to commit vs. wait
  • Latency sensitive - network issues destroy UX
  • Graceful degradation required - handle reconnections
  • Hard to debug - race conditions and timing bugs

Technical Deep Dive

Architecture

Real-time streaming requires persistent connections (WebSockets), dual-mode processing (draft + commit), and careful state management to handle incomplete data.

  • •Transport: WebSockets for bidirectional streaming
  • •Draft Mode: Streaming AI for immediate preview
  • •Commit Mode: Final processing for durable actions
  • •State Management: Track partial results and timing

The Two-Brain Pattern

Real-time apps need two separate AI processing modes:

Streaming Brain (Draft Mode)

  • • Processes data as it arrives
  • • Shows immediate, partial results
  • • Updates continuously
  • • Non-committal - can be wrong or incomplete

Commit Brain (Final Mode)

  • • Triggered by timing signals (silence, explicit commit)
  • • Processes complete, finalized input
  • • Creates durable actions (save to DB, send email, etc.)
  • • Higher quality, slower, but definitive

Example: FreeScribe Audio Processing

  1. 1.Audio Stream: User speaks, audio chunks stream to server
  2. 2.Draft Transcription: Partial transcripts stream back in real-time
  3. 3.Timing Detection: Silence detector watches for pauses
  4. 4.Commit Trigger: After 2s silence or explicit action
  5. 5.Final Processing: Clean up, format, save to database
  6. 6.Reconnection Handling: Resume from last known state

When to Use This Pattern

  • ✓Voice/audio processing (transcription, translation)
  • ✓Live video analysis or filtering
  • ✓Real-time collaboration tools
  • ✓Sensor data processing (IoT, monitoring)
  • ✓When immediate feedback is critical to UX

When NOT to Use This Pattern

  • ✗When batch processing is acceptable
  • ✗Simple request/response patterns work fine
  • ✗When you don't need sub-second responsiveness
  • ✗Team lacks experience with WebSockets/streaming

Apps Using This Pattern

FreeScribe

Real-time audio transcription with streaming AI

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