| # MINIMIZATION PROOF | |
| ## Source vs Kernel | |
| | Metric | tinygrad/device.py (leader) | kernel.py (ours) | Notes | | |
| |---|---|---|---| | |
| | Total LOC | 420 | 10 | wc -l | | |
| | Absorbed LOC | 16 (lines 39–54) | 10 | direct distillation | | |
| | Reduction ratio (total) | — | **42.0×** | 420 ÷ 10 | | |
| | Reduction ratio (absorbed) | — | **1.6×** | 16 ÷ 10 | | |
| ## What was kept | |
| | tinygrad concept | Our kernel equivalent | | |
| |---|---| | |
| | `ALL_DEVICES = [...]` | `PATHS = ["CPU","GPU","QUANTIZED","MOE"]` | | |
| | `get_available_devices()` iterator | implicit iteration over `PATHS` dict | | |
| | `next(self.get_available_devices())` first-available | `min(costs, key=costs.__getitem__)` min-energy | | |
| | No energy model (tinygrad picks first live device) | NINA Butler-Volmer `nina(η)` supplies energy cost per path | | |
| ## What was added (not from leader) | |
| - NINA Butler-Volmer lambda (5 constants, 1 math expression) — domain-specific extension for energy-aware dispatch. | |
| - `random.Random(seed)` for deterministic η sampling from `world` signal inputs. | |
| ## Verification | |
| ``` | |
| $ wc -l kernel.py | |
| 10 kernel.py | |
| ``` | |
| Every line is either a comment/import, a constant, the NINA formula, or one of the 4 dispatch lines. No padding. | |