Story 18: Token-by-token streaming responses

- Backend: Added OllamaProvider::chat_stream() with newline-delimited JSON parsing
- Backend: Emit chat:token events for each token received from Ollama
- Backend: Added futures dependency and stream feature for reqwest
- Frontend: Added streamingContent state and chat:token event listener
- Frontend: Real-time token display with auto-scroll
- Frontend: Markdown and syntax highlighting support for streaming content
- Fixed all TypeScript errors (tsc --noEmit)
- Fixed all Biome warnings and errors
- Fixed all Clippy warnings
- Added comprehensive code quality documentation
- Added tsc --noEmit to verification checklist

Tested and verified:
- Tokens stream in real-time
- Auto-scroll works during streaming
- Tool calls interrupt streaming correctly
- Multi-turn conversations work
- Smooth performance with no lag

.living_spec/stories/archive/18_streaming_responses.md

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+# Story 18: Token-by-Token Streaming Responses
+
+## User Story
+As a user, I want to see the AI's response appear token-by-token in real-time (like ChatGPT), so that I get immediate feedback and know the system is working, rather than waiting for the entire response to appear at once.
+
+## Acceptance Criteria
+- [x] Tokens appear in the chat interface as Ollama generates them, not all at once
+- [x] The streaming experience is smooth with no visible lag or stuttering
+- [x] Auto-scroll keeps the latest token visible as content streams in
+- [x] When streaming completes, the message is properly added to the message history
+- [x] Tool calls work correctly: if Ollama decides to call a tool mid-stream, streaming stops gracefully and tool execution begins
+- [ ] The Stop button (Story 13) works during streaming to cancel mid-response
+- [x] If streaming is interrupted (network error, cancellation), partial content is preserved and an appropriate error state is shown
+- [x] Multi-turn conversations continue to work: streaming doesn't break the message history or context
+
+## Out of Scope
+- Streaming for tool outputs (tools execute and return results as before, non-streaming)
+- Throttling or rate-limiting token display (we stream all tokens as fast as Ollama sends them)
+- Custom streaming animations or effects beyond simple text append
+- Streaming from other LLM providers (Claude, GPT, etc.) - this story focuses on Ollama only
+
+## Technical Notes
+- Backend must enable `stream: true` in Ollama API requests
+- Ollama returns newline-delimited JSON, one object per token
+- Backend emits `chat:token` events (one per token) to frontend
+- Frontend appends tokens to a streaming buffer and renders in real-time
+- When streaming completes (`done: true`), backend emits `chat:update` with full message
+- Tool calls are detected when Ollama sends `tool_calls` in the response, which triggers tool execution flow