Major improvement to reduce parameter explosion (15+ args → 3-4 contexts):
- Add LowerFnCtx/BlockCtx for grouping related parameters
- Add lightweight StrHandle/StrPtr newtypes for string safety
- Implement boxed API wrappers for boxcall/fields/invoke
- Add dev checks infrastructure (NYASH_DEV_CHECK_DISPATCH_ONLY_PHI)
Key achievements:
- lower_boxcall: 16 args → 7 args via boxed API
- fields/invoke: Similar parameter reduction
- BuilderCursor discipline enforced throughout
- String handle invariant: i64 across blocks, i8* only at call sites
Status:
- Internal migration in progress (fields → invoke → marshal)
- Full cutover pending due to borrow checker constraints
- dep_tree_min_string.o generation successful (sealed=ON)
Next: Complete internal migration before flipping to boxed APIs
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
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 paper-g-ai-assisted-compiler folder documenting:
- Week-long LLVM backend development with AI assistance
- Key insights from PHI/SSA struggles to Resolver API solution
- Development log capturing the chaotic reality
- Abstract in both English and Japanese
Key quote: 'I don't remember anymore' - capturing the authentic
experience of intensive AI-assisted development where the process
itself becomes the research data.
This represents potentially the first fully documented case of
building a compiler backend primarily through AI assistance.
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.
- Added NYASH_LOOPFORM_LATCH2HEADER environment variable
- When enabled, latch block jumps back to header (completing the loop)
- When disabled (default), latch remains unreachable (safe mode)
- Preserves header predecessor count stability in default mode
This allows gradual testing of full LoopForm loop structure.
- Added LoopForm IR scaffolding with 5-block structure (header/body/dispatch/latch/exit)
- Implemented dispatch block with PHI nodes for tag(i8) and payload(i64)
- Created registry infrastructure for future body→dispatch wiring
- Header→dispatch wiring complete with Break=1 signal
- Gated behind NYASH_ENABLE_LOOPFORM=1 environment variable
- Successfully tested with loop_min_while.nyash (1120 bytes object)
Next steps:
- Implement 2-step Jump chain detection
- Add NYASH_LOOPFORM_BODY2DISPATCH for body→dispatch redirect
- Connect latch→header when safe
🚀 Phase 1 foundation complete and working!
- Add NYASH_ENABLE_LOOPFORM=1 gate for experimental loop normalization
- Detect simple while-patterns in Branch terminator (header→body→header)
- Add loopform.rs with scaffold for future Signal-based lowering
- Wire detection in codegen/mod.rs (non-invasive, logs only)
- Update CURRENT_TASK.md with LoopForm experimental plan
- Goal: Centralize PHIs at dispatch blocks, simplify terminator management
This is the first step towards the LoopForm IR revolution where
"Everything is Box × Everything is Loop". Currently detection-only,
actual lowering will follow once basic patterns are validated.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
ChatGPT5's investigation revealed builder position management issues:
- Added verbose logging for block lowering and terminator emission
- Enhanced position_at_end calls before all terminator operations
- Added debug output for emit_jump/emit_branch operations
- Improved snapshot vs vmap fallback reporting in seal_block
Key findings:
- Sealed SSA snapshot mechanism is working correctly
- Block terminator issues persist due to builder position drift
- Main.has_in_stack/2 shows terminator missing after emit
Next steps:
- Add immediate terminator verification after each emit
- Track builder position changes in complex operations
- Investigate specific functions where builder drift occurs
This commit adds diagnostic infrastructure to pinpoint
where LLVM IR builder position gets misaligned.
PHI type coercion and core-first routing fixes:
- Auto type conversion for PHI nodes (i64↔i8*↔i1↔f64)
- Fixed ArrayBox.get misrouting to Map path
- Core-first strategy for Array/Map creation
- Added comprehensive debug logging ([PHI], [ARR], [MAP])
Results:
✅ Array smoke test: 'Result: 3'
✅ Map smoke test: 'Map: v=42, size=1'
After 34+ minutes of battling Rust lifetime errors,
ChatGPT5 achieved a major breakthrough\!
Key insight: The bug wasn't in PHI/SSA logic but in
Box type routing - ArrayBox.get was incorrectly caught
by Map fallback due to missing annotations.
We're SO CLOSE to Nyash self-hosting paradise\! 🌟
Once this stabilizes, everything can be written in
simple, beautiful Nyash code instead of Rust complexity.
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\! 🌟
- Add function name prefix to basic block labels to avoid cross-function conflicts
- blocks.rs: create_basic_blocks now takes fn_label parameter
- Format: 'Main_join_2_bb23' instead of just 'bb23'
- Add conservative fallback for missing terminators (jump to next or entry)
- This fixes 'Basic Block does not have terminator' verification error
Analysis insights:
- MIR output was correct (all blocks had terminators)
- Problem was LLVM-side block name collision between functions
- Classic case of 'Rust complexity' - simple C++ style fix works best
- Sometimes the simplest solution is the right one\!
## LLVM Call Instruction Modularization
- Moved MirInstruction::Call lowering to separate instructions/call.rs
- Follows the principle of one MIR instruction per file
- Call implementation was already complete, just needed modularization
## Phase 21 Documentation
- Moved all Phase 21 content to private/papers/paper-f-self-parsing-db/
- Preserved AI evaluations from Gemini and Codex
- Academic paper potential confirmed by both AIs
- Self-parsing AST database approach validated
## Next Steps
- Continue monitoring ChatGPT5's LLVM improvements
- Consider creating separate nyash-llvm-compiler crate when LLVM layer is stable
- This will reduce build times by isolating LLVM dependencies
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
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>
