Document current coding-workflow stack state

Snapshot of where opencode + Qwen3-Coder + MCPs + Kimi-Linear + voice
  + Phoenix tracing land today, plus in-flight (oc-tree, kimi-linear
  context ramp) and next (ComfyUI) items with pointers to per-project
  NEXT_STEPS.md guides.

qwen-large-codebase-roadmap.md

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+# Qwen Large Codebase Roadmap
+
+This document outlines recommendations for working with large codebases using opencode and your existing vector database setup.
+
+## Current Setup
+- Large codebase enriched by large-context LLM
+- Vector database for searching codebase
+- Opencode with existing MCP servers (Playwright, SearXNG, Serena, Basic-memory, Sequential-thinking, Task-master)
+
+## Recommended Tooling
+
+### 1. Continue.dev Integration
+- **Purpose**: Better codebase indexing and search with nomic-embed-text
+- **Benefits**: Inline completion support, enhanced code understanding capabilities
+- **Implementation**: Deploy continue.dev alongside existing setup
+
+### 2. Enhanced RAG Implementation
+- **Purpose**: Leverage vector DB for efficient codebase navigation
+- **Approach**: 
+  - Custom MCP server that queries your vector DB for relevant code sections
+  - Cited search variants for precise code reference retrieval
+- **Integration**: Combine with existing symbolic search from Serena MCP
+
+### 3. Structured Workflows
+- **Purpose**: Organize complex projects and search activities
+- **Tools**: 
+  - Task-master MCP for task management
+  - Workflow patterns that tie vector DB queries to specific tasks
+- **Benefits**: Better tracking of search results and implementation progress
+
+### 4. Memory Management
+- **Purpose**: Persistent documentation of codebase insights
+- **Approach**:
+  - Leverage basic-memory MCP for notes tied to vector DB queries
+  - Create patterns for documenting important code patterns, API decisions, and insights
+- **Integration**: Connect memory entries to specific vector search results
+
+### 5. Monitoring Integration
+- **Purpose**: Track performance of vector database queries
+- **Tools**:
+  - Phoenix tracing for performance monitoring
+  - Track tool usage patterns for optimizing search strategies
+- **Benefits**: Visibility into query efficiency and system performance
+
+### 6. Code Navigation Enhancement
+- **Purpose**: Combine vector search with symbolic navigation
+- **Approach**:
+  - Use Serena MCP for symbolic code navigation
+  - Augment with vector search results for context
+- **Integration**: Create hybrid search approaches that use both methods
+
+## Implementation Approach
+
+1. **Phase 1**: Install continue.dev for enhanced code understanding
+2. **Phase 2**: Set up vector DB query tools as custom MCP servers
+3. **Phase 3**: Create patterns for combining vector search with symbolic navigation
+4. **Phase 4**: Implement persistent memory patterns for documenting findings
+5. **Phase 5**: Establish monitoring and optimization practices
+
+## Key Integration Points
+
+### Vector DB + Opencode
+- Use vector database queries to find relevant code sections
+- Combine with symbolic search for complete context understanding
+- Enable citation-based referencing of code locations
+
+### Memory + Search
+- Document search results in basic-memory
+- Create connections between vector DB entries and memory notes
+- Maintain a searchable knowledge base of codebase insights
+
+### Monitoring + Performance
+- Track query performance through Phoenix
+- Optimize search strategies based on usage patterns
+- Monitor system efficiency as complexity scales
+
+This roadmap provides a gradual approach to enhancing your codebase management capabilities while leveraging your existing infrastructure.