docs: update README, wiki overview, index, log; add roles/code-review/pydantic wiki pages

- README: remove autogen/crewai references, add v2 architecture diagram,
  code-review scenario, cross-LLM config examples, roadmap
- wiki/overview: event-driven architecture, Pydantic v2, roadmap
- wiki/index: add entities/roles, concepts/code-review, packages/pydantic
- wiki/log: Phase 1 completion, code-review decision
- New: entities/roles.md, concepts/code-review.md, packages/pydantic.md

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

.wiki/concepts/code-review.md

@@ -0,0 +1,37 @@
+---
+title: Cross-LLM Code Review
+type: concept
+updated: 2026-05-04
+---
+
+# Cross-LLM Code Review
+
+Use meeting-room to run multi-agent code review where different LLM providers cross-check each other's work.
+
+## How it works
+
+1. Configure multiple providers (OpenAI, Anthropic, DeepSeek, local Ollama)
+2. Assign different providers to review roles (defender, attacker, security)
+3. Point meeting-room at a project directory with `-w /path/to/project`
+4. Agents read code files, debate quality, and produce structured findings
+
+## Scenario
+
+```bash
+meeting-room -s code-review.md -w /path/to/project
+```
+
+## Why cross-LLM?
+
+Different models have different blind spots. GPT-4o may catch type errors that Claude misses, while DeepSeek may spot performance issues neither other model flagged. Cross-LLM review is the "second pair of eyes" principle applied to AI code review.
+
+## Role separation
+
+- **defender** — explains design decisions, pushes back on unjustified criticism
+- **attacker** — hunts bugs, SOLID violations, dead code, edge cases
+- **security** — checks injection, auth gaps, CVEs in dependencies
+- **moderator** — categorizes findings (CRITICAL / IMPORTANT / MINOR / APPROVED)
+
+## Future (Phase 3)
+
+Boss role will allow injecting questions mid-review. CSO (Claude Code) will be able to join the review via REST API.