## Summary
Investigated OpenAI's new GPT-5-Codex model and Codex GitHub PR review integration capabilities.
## GPT-5-Codex Analysis
### Benchmark Performance (Good)
- SWE-bench Verified: 74.5% (vs GPT-5's 72.8%)
- Refactoring tasks: 51.3% (vs GPT-5's 33.9%)
- Code review: Higher developer ratings
### Real-World Issues (Concerning)
- Users report degraded coding performance
- Scripts that previously worked now fail
- Less consistent than GPT-4.5
- Longer response times (minutes vs instant)
- "Creatively and emotionally flat"
- Basic errors (e.g., counting letters incorrectly)
### Key Finding
Classic case of "optimizing for benchmarks vs real usability" - scores well on tests but performs poorly in practice.
## Codex GitHub PR Integration
### Setup Process
1. Enable MFA and connect GitHub account
2. Authorize Codex GitHub app for repos
3. Enable "Code review" in repository settings
### Usage Methods
- **Manual**: Comment '@codex review' in PR
- **Automatic**: Triggers when PR moves from draft to ready
### Current Limitations
- One-way communication (doesn't respond to review comments)
- Prefers creating new PRs over updating existing ones
- Better for single-pass reviews than iterative feedback
## 'codex resume' Feature
New session management capability:
- Resume previous codex exec sessions
- Useful for continuing long tasks across days
- Maintains context from interrupted work
🐱 The investigation reveals that while GPT-5-Codex shows benchmark improvements, practical developer experience has declined - a reminder that metrics don't always reflect real-world utility\!
## Summary
Documented the "init block vs fields-at-top" design discussion as a valuable example of AI-human collaboration in language design.
## Changes
### Paper G (AI Collaboration)
- Added field-declaration-design.md documenting the entire discussion flow
- Showcased how complex init block proposal evolved to simple "fields at top" rule
- Demonstrates AI's tendency toward complexity vs human intuition for simplicity
### Paper H (AI Practical Patterns)
- Added Pattern #17: "Gradual Refinement Pattern" (段階的洗練型)
- Documents the process: Complex AI proposal → Detailed analysis → Human insight → Convergence
- Field declaration design as a typical example
### Paper K (Explosive Incidents)
- Added Incident #046: "init block vs fields-at-top incident"
- Updated total count to 46 incidents
- Shows how a single human comment redirected entire design approach
## Design Decision
After analysis, decided that BoxIndex should remain a compiler-internal structure, not a core Box:
- Core Boxes: User-instantiable runtime values (String, Integer, Array, Map)
- Compiler internals: BoxIndex for name resolution (compile-time only)
- Clear separation of concerns between language features and compiler tools
## Philosophy
This discussion exemplifies key principles:
- The best design needs no explanation
- Constraints provide clarity, not limitation
- "Everything is Box" doesn't mean "compiler internals are Boxes"
- AI tends toward theoretical completeness; humans toward practical simplicity
🐱 Sometimes the simplest answer is right in front of us\!
Major changes:
- Split runner module: 1358→580 lines (via Gemini)
- Create new modules: dispatch.rs, selfhost.rs, pipeline.rs, pipe_io.rs
- Fix build errors from incomplete method migrations
- Add warning to CLAUDE.md about JIT/Cranelift not working
- Create interpreter.rs mode module
- Refactor loop builder into separate module
Build status:
- ✅ Executable builds successfully
- ✅ Basic execution works (tested with print)
- ⚠️ 106 warnings remain (to be cleaned up next)
- ⚠️ execute_mir_mode still in mod.rs (needs further migration)
Note: ChatGPT correctly fixed runner.execute_mir_mode() calls
that I incorrectly changed to super::modes::mir::
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
