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All Solution Plays

Play 94

AI Podcast Generator

High Designed

Text-to-podcast pipeline with multi-speaker voice synthesis and music transitions.

Text-to-podcast pipeline with multi-speaker voice synthesis, music transitions, chapter markers, and content safety review. Converts blog posts, research papers, or meeting notes into professionally narrated audio content using Azure AI Voice Live SDK.

Architecture Pattern

Podcast pipeline: content ingestion - script generation - multi-voice synthesis - music layering - chapter marking - safety review

Azure Services

Azure AI SpeechAzure OpenAIAzure Blob StorageAzure CDNAzure Functions

DevKit (.github Agentic OS)

  • agent.md — root orchestrator with builder→reviewer→tuner handoffs
  • 3 agents — Podcast Builder (gpt-4o), Reviewer (gpt-4o-mini), Tuner (gpt-4o-mini)
  • 3 skills — deploy (217 lines), evaluate (105 lines), tune (228 lines)
  • 4 prompts — /deploy, /test, /review, /evaluate with agent routing
  • .vscode/mcp.json — FrootAI MCP with OpenAI + Speech inputs + envFile

TuneKit (AI Config)

  • config/openai.json - script generation and content adaptation prompts
  • config/podcast.json - voice personas, speaking rates, music styles
  • config/guardrails.json - content safety thresholds, audio quality minimums
  • evaluation/eval.py - Audio quality >4.0 MOS, Content fidelity >90%

Tuning Parameters

Voice persona selectionSpeaking rateMusic transition styleChapter detection sensitivityContent safety threshold

Machine evidence

FrootAI evidence lifecycle

This is an internal evidence maturity label, not third-party certification, accreditation, legal compliance, or a production guarantee. Missing or expired evidence demotes automatically; catalog claims cannot promote a play.

Designed
designed
build verified
evaluation verified

This play currently has design evidence only. A runnable scenario, endpoint evaluation, and build receipts are the next contiguous gates.

Loading architecture and cost model…

Repo Intelligence

v1

A no-clone, revision-pinned map for agents and humans. Observed evidence is separated from inferred flow so the output stays useful without pretending to be a full call graph.

Indexing bounded repository evidence…