diff --git a/ipynb/Advent-2025-AI.ipynb b/ipynb/Advent-2025-AI.ipynb
index 65396e3..d457f21 100644
--- a/ipynb/Advent-2025-AI.ipynb
+++ b/ipynb/Advent-2025-AI.ipynb
@@ -23,6 +23,7 @@
     "- *I'm beginning to think I should use an LLM as an assistant for all my coding, not just as an experiment like this.*\n",
     "- *This is a huge improvement over just one year ago, when LLMs could not perform anywhere near this level.*\n",
     "- *The three LLMS seemed to be roughly equal in quality.*\n",
+    "- *I neglected to track the time it took them to produce the code, but it was a lot faster than me–maybe 20 times faster.*\n",
     "- *The LLMs knew the things you would want an experienced software engineer to know:*\n",
     "    - *How to see through the story about elves and christmas trees, etc. and get to the real programming issues*\n",
     "    - *Standard Python syntax, builtin types, and basic modules (e.g. `collections`, `functools`, `typing`, `numpy`)*\n",
@@ -3955,7 +3956,7 @@
     "\n",
     "*The human-written code is about **five times more concise** than the LLM code.*\n",
     "\n",
-    "*The LOC numbers are total lines of code, including blank lines, comments, doc strings, and the line to make the call to compute the answer. For the human-written code, I count the parsing code against Part 1.*"
+    "*The LOC numbers are total lines of code, including blank lines, comments, and doc strings.*"
    ]
   },
   {
@@ -3991,14 +3992,6 @@
     " |  **TOTAL** | | **1732** | **356** |   | \n",
     " |  **MEAN** | | **75** | **14.5** | |"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fdcde1d0-a999-4867-8b08-6e10a75fa385",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {