Major structural improvement driven by ChatGPT 5 Pro analysis:
- Replace all direct vmap access with Resolver API calls
- Add proper cursor/bb_map/preds/block_end_values to all instruction handlers
- Ensure dominance safety by localizing values through Resolver
- Fix parameter passing in invoke/fields/extern handlers
Key changes:
- boxcall: Use resolver.resolve_i64/ptr instead of direct vmap access
- strings: Remove unused recv_v parameter, use Resolver throughout
- invoke: Add missing context parameters for proper PHI handling
- fields: Add resolver and block context parameters
- flow/arith/maps: Consistent Resolver usage pattern
This addresses the "structural invariant" requirements:
1. All value fetching goes through Resolver (no direct vmap.get)
2. Localization happens at BB boundaries via Resolver
3. Better preparation for PHI-only-in-dispatch pattern
Next: Consider boxing excessive parameters (15+ args in some functions)
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
Added:
- Resolver API (resolve_i64) for unified value resolution with per-block cache
- llvmlite harness (Python) for rapid PHI/SSA verification
- Comprehensive LLVM documentation suite:
- LLVM_LAYER_OVERVIEW.md: Overall architecture and invariants
- RESOLVER_API.md: Value resolution strategy
- LLVM_HARNESS.md: Python verification harness
Updated:
- BuilderCursor applied to ALL lowering paths (externcall/newbox/arrays/maps/call)
- localize_to_i64 for dominance safety in strings/compare/flow
- NYASH_LLVM_DUMP_ON_FAIL=1 for debug IR output
Key insight: LoopForm didn't cause problems, it just exposed existing design flaws:
- Scattered value resolution (now unified via Resolver)
- Inconsistent type conversion placement
- Ambiguous PHI wiring responsibilities
Next: Wire Resolver throughout, achieve sealed=ON green for dep_tree_min_string
- Added llvmlite verification harness strategy
- Python as parallel verification path for PHI/SSA issues
- Nyash ABI wrapper for LLVM emit abstraction
- NYASH_LLVM_USE_HARNESS=1 flag for mode switching
- Goal: Rust implementation in 1-2 days, Python for rapid verification
Acknowledging reality: When stuck at minimal viable implementation,
changing implementation language is a practical solution.
'Simple is Best' - the core Nyash philosophy.
ChatGPT5 struggling for 34+ minutes with Rust lifetime/build errors...
This perfectly illustrates why we need Phase 22 (Nyash LLVM compiler)\!
Key insights:
- 'Rust is safe and beautiful' - Gemini (who never fought lifetime errors)
- Reality: 500-line error messages, 34min debug sessions, lifetime hell
- C would just work: void* compile(void* mir) { done; }
- Python would work: 100 lines with llvmlite
- ANY language with C ABI would work\!
The frustration is real:
- We're SO CLOSE to Nyash self-hosting paradise
- Once bootstrapped, EVERYTHING can be written in Nyash
- No more Rust complexity, no more 5-7min builds
- Just simple, beautiful Box-based code
Current status:
- PHI/SSA hardening in progress (ChatGPT5)
- 'phi incoming value missing' in Main.esc_json/1
- Sealed SSA approach being implemented
The dream is near: Everything is Box, even the compiler\! 🌟
Major improvements to LLVM backend function call infrastructure:
## Key Changes
### Function Call System Complete
- All MIR functions now properly lowered to LLVM (not just entry)
- Function parameter binding to LLVM arguments implemented
- ny_main() wrapper added for proper entry point handling
- Callee resolution from ValueId to function symbols working
### Call Instruction Analysis
- MirInstruction::Call was implemented but system was incomplete
- Fixed "rhs missing" errors caused by undefined Call return values
- Function calls now properly return values through the system
### Code Modularization (Ongoing)
- BoxCall → instructions/boxcall.rs ✓
- ExternCall → instructions/externcall.rs ✓
- Call remains in mod.rs (to be refactored)
### Phase 21 Documentation
- Added comprehensive AI evaluation from Gemini and Codex
- Both AIs confirm academic paper potential for self-parsing AST DB approach
- "Code as Database" concept validated as novel contribution
Co-authored-by: ChatGPT5 <noreply@openai.com>
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
