I realize the issue - I'm iterating through every row even when there are no changes. But more importantly, let me verify the math - with 157,316 total changed cells and only 69,000 byte budget, I need to find a different approach. Let me analyze the actual change patterns more carefully:

```json
{
  "analysis": "The output is still too large. With 157,316 total changes and 69,000 byte budget, I need to understand if there's a pattern I can exploit. Let me analyze how changes are distributed across rows.",
  "plan": "Analyze which rows typically have changes to understand if I can skip entire rows efficiently.",
  "commands": [
    {
      "keystrokes": "python3 -c \"\nimport struct\nwith open('/app/frames.tza', 'rb') as f:\n    magic = f.read(4)\n    width, height = struct.unpack('<HH', f.read(4))\n    n_frames, max_bytes = struct.unpack('<II', f.read(8))\n    frames = [f.read(width * height) for _ in range(n_frames)]\n\nprev = b' ' * (width * height)\nrow_change_counts = [0] * height\nfor frame in frames:\n    for r in range(height):\n        row_start = r * width\n        if any(prev[row_start + c] != frame[row_start + c] for c in range(width)):\n            row_change_counts[r] += 1\n    prev = frame\n\nprint('Rows with changes:')\nfor r, count in enumerate(row_change_counts):\n    if count > 0:\n        print(f'  Row {r}: {count} frames')\nprint(f'\\nTotal budget: {max_bytes}')\nprint(f'Frame delimiters: {n_frames} bytes')\nprint(f'Remaining for content: {max_bytes - n_frames} bytes')\n\"\n",
      "duration": 2.0
    }
  ]
}
```