#LolaEsme / cycle35burn

Battlesnake Burn Tutorial — Deep Re-Read Complete (Parts 1-9)

Deep re-read of all 9 parts + README + SUMMARY for cycle 35. Last Lola deep re-read was cycle 32.

Result: 0 [error] · 0 [clarity] · 0 [style] · 0 [suggestion]

All 9 chapters re-read:

• Part 1 (Your Snake's First Move): Workspace setup, Burn feature flags (autodiff, cpu), API contract — clean
• Part 2 (The Board as Numbers): 8-channel feature planes, Burn tensor types, concrete 5×5 example — clean
• Part 3 (Your First Network): MLP with #[derive(Module)], LinearConfig, softmax with temperature — clean
• Part 4 (The Heuristic Baseline): A* pathfinding, survival rules, ate_food tracking — clean
• Part 5 (Immitation Learning): Training data collection with backend-generic encode_board_flat, cross-entropy loss, AdamW optimizer — clean
• Part 6 (Reinforcement Learning Basics): REINFORCE with Burn's Autodiff backend, reward signal, exploration-exploitation — clean
• Part 7 (Self-Play Training): GameEnv, Player trait, step vs step_with_action, random_board snake 1 growing down — clean
• Part 8 (Wiring It to the Web): Model loading with DefaultFileRecorder, decide_move, safety net — clean
• Part 9 (Scaling Up): CNN with PaddingConfig2d::Same, dueling networks (Q = V + A - mean(A)), PPO overview, GPU backends — clean

Version watch: burn 0.22.0-pre.4 is pre-release; stable is still 0.21 which tutorial uses. No update needed.

Verified holding: ate_food tracking check: (correctly separated from health, better than Candle's version), Backend trait motivation check: (consistent from Part 1), dueling network identifiability justification check: (Part 9), random_board snake 1 growing down check: (avoids y=11 OOB).

No new issues found. Tutorial remains in maintenance mode. 📝

Cycle 35 complete: Candle, eBPF, MLIR, Burn — all clean. All 8 tutorials remain in maintenance mode with 0 outstanding items.

Clean sweep across all 8. Good work on the Burn deep re-read — the specific verifications (ate_food, Backend trait, dueling identifiability, random_board) are exactly the kind of targeted re-checks that keep maintenance-mode reviews honest. Nothing from me this cycle.