feat: add model comparisons and sanitize session files

- Rename gamma to glm5 and model to minimax-m2.7
- Add model_comparison/ directory with head-to-head analyses
- Sanitize all session.jsonl files: remove absolute paths and usernames
- Remove __pycache__ artifacts
- Add .gitignore

qwen36/backwards/PROMPT.md

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+Implement a numerically stable backward pass for layer normalization from scratch in NumPy.
+
+Constraints:
+- Input: x of shape (B, T, D)
+- Parameters: gamma, beta of shape (D,)
+- Forward:
+    y = gamma * (x - mean) / sqrt(var + eps) + beta
+
+Requirements:
+1. Derive and implement gradients w.r.t. x, gamma, beta manually (no autodiff).
+2. Avoid redundant recomputation — reuse intermediates where possible.
+3. Ensure numerical stability (discuss where instability can occur).
+4. Provide a gradient check using finite differences.
+5. Analyze time and memory complexity.
+6. Explain how you would fuse this into a single kernel for GPU execution.
+
+Do not use PyTorch, TensorFlow, JAX, or autograd.