Switch local AI from Ollama to bundled llama-server, add MIT license

- Replace Ollama dependency with bundled llama-server (llama.cpp)
  so users need no separate install for local AI inference
- Rust backend manages llama-server lifecycle (spawn, port, shutdown)
- Add MIT license for open source release
- Update architecture doc, CLAUDE.md, and README accordingly

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

CLAUDE.md

@@ -7,7 +7,8 @@ Desktop app for transcribing audio/video with speaker identification. Runs local
 - **Desktop shell:** Tauri v2 (Rust backend + Svelte/TypeScript frontend)
 - **ML pipeline:** Python sidecar process (faster-whisper, pyannote.audio, wav2vec2)
 - **Database:** SQLite (via rusqlite in Rust)
-- **AI providers:** LiteLLM, OpenAI, Anthropic, Ollama (local)
+- **Local AI:** Bundled llama-server (llama.cpp) — default, no install needed
+- **Cloud AI providers:** LiteLLM, OpenAI, Anthropic (optional, user-configured)
 - **Caption export:** pysubs2 (Python)
 - **Audio UI:** wavesurfer.js
 - **Transcript editor:** TipTap (ProseMirror)
@@ -15,7 +16,9 @@ Desktop app for transcribing audio/video with speaker identification. Runs local
 ## Key Architecture Decisions
 - Python sidecar communicates with Rust via JSON-line IPC (stdin/stdout)
 - All ML models must work on CPU. GPU (CUDA) is optional acceleration.
-- AI cloud providers are optional. Local models (Ollama) are a first-class option.
+- AI cloud providers are optional. Bundled llama-server (llama.cpp) is the default local AI — no separate install needed.
+- Rust backend manages llama-server lifecycle (start/stop/port allocation).
+- Project is open source (MIT license).
 - SQLite database is per-project, stored alongside media files.
 - Word-level timestamps are required for click-to-seek playback sync.