diff --git a/ipynb/Advent-2024.ipynb b/ipynb/Advent-2024.ipynb
index ad705ff..09adea1 100644
--- a/ipynb/Advent-2024.ipynb
+++ b/ipynb/Advent-2024.ipynb
@@ -18,21 +18,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "ed82ed5b-a42d-468b-8f6e-288d3c2de20b",
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Matplotlib is building the font cache; this may take a moment.\n"
+      "3.12.7 | packaged by Anaconda, Inc. | (main, Oct  4 2024, 08:22:19) [Clang 14.0.6 ]\n"
      ]
     }
    ],
    "source": [
     "%run AdventUtils.ipynb\n",
-    "current_year = 2024"
+    "current_year = 2024\n",
+    "\n",
+    "print(sys.version)"
    ]
   },
   {
@@ -47,7 +49,7 @@
     "\n",
     "The function `parse` assumes that the input is a sequence of records (default one per line), each of which should be parsed in some way (default just left as a string, but the argument `ints` says to treat each record as a tuple of integers). The function `answer` records the correct answer (for regression testing), and records the run time (that's why a `lambda:` is used).\n",
     "\n",
-    "To fully understand each day's puzzle, and to follow along the drama involving Santa, the elves, the Chief Historian, and all the rest, you need to read the descriptions on the [**AoC**](https://adventofcode.com/) site, as linked in the header for each of my day's solutions, e.g. [**Day 1**](https://adventofcode.com/2023/day/1) below. Since you can't read Part 2 until you solve Part 1, I'll partially describe Part 2 in this notebook. But I can't copy the content of AoC here, nor show my input files; you need to go to the site for that.\n",
+    "To fully understand each day's puzzle, and to follow along the drama involving Santa, the elves, the elephants, the Chief Historian, and all the rest, you need to read the descriptions on the [**AoC**](https://adventofcode.com/) site, as linked in the header for each of my day's solutions, e.g. [**Day 1**](https://adventofcode.com/2023/day/1) below. Since you can't read Part 2 until you solve Part 1, I'll partially describe Part 2 in this notebook. But I can't copy the content of AoC here, nor show my input files; you need to go to the site for that.\n",
     "\n",
     "\n",
     "\n",
@@ -61,12 +63,12 @@
    "source": [
     "# [Day 1](https://adventofcode.com/2024/day/1) Historian Hysteria\n",
     "\n",
-    "According to the narrative, North Pole Historians created two lists of **location IDs**. We can parse them as a sequence of pairs of integers, and then use the transpose function, `T` to get two lists of ID numbers:"
+    "According to the narrative, North Pole Historians created two lists of **location IDs**. We can parse them as a sequence of pairs of integers, and then use the transpose function, `T`, from my [AdventUtils](AdventUtils.ipynb) to get two lists of ID numbers:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "22e5d621-a152-4712-866f-f8b962b5dd14",
    "metadata": {},
    "outputs": [
@@ -119,17 +121,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "2dbfa3ae-3d47-4711-8821-7d1b2564bdc8",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Puzzle  1.1:   .0002 seconds, answer 1830467         ok"
+       "Puzzle  1.1:   .0001 seconds, answer 1830467         ok"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -155,7 +157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "e33f9705-3b51-4314-a302-6b3445290713",
    "metadata": {},
    "outputs": [
@@ -165,7 +167,7 @@
        "Puzzle  1.2:   .0001 seconds, answer 26674158        ok"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -192,7 +194,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "10e1ab83-a6ec-4143-ad9a-eaae220adcde",
    "metadata": {},
    "outputs": [
@@ -243,12 +245,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "368cbe1c-b6b6-4a82-bef9-599ee9725899",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def safe(report: Ints) -> bool:\n",
+    "def is_safe(report: Ints) -> bool:\n",
     "    \"\"\"A report is safe if all differences are either in {1, 2, 3} or in {-1, -2, -3}.\"\"\"\n",
     "    deltas = diffs(report)\n",
     "    return deltas.issubset({1, 2, 3}) or deltas.issubset({-1, -2, -3})\n",
@@ -258,12 +260,12 @@
     "    return {report[i] - report[i - 1] for i in range(1, len(report))}\n",
     "\n",
     "assert diffs((7, 6, 4, 2, 1)) == {-1, -2}\n",
-    "assert safe((7, 6, 4, 2, 1))  == True"
+    "assert is_safe((7, 6, 4, 2, 1))  == True"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "e662bf10-4d6a-40f1-95ce-dfc39f5b3fc2",
    "metadata": {},
    "outputs": [
@@ -273,14 +275,14 @@
        "Puzzle  2.1:   .0004 seconds, answer 257             ok"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "answer(2.1, 257, lambda:\n",
-    "       quantify(reports, safe))"
+    "       quantify(reports, is_safe))"
    ]
   },
   {
@@ -295,14 +297,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "67ba1d53-95b7-4811-b225-2ff15d6bdc5c",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def safe_with_dampener(report: Ints) -> bool:\n",
+    "def is_safe_with_dampener(report: Ints) -> bool:\n",
     "    \"\"\"Is there any way to drop one element of `report` to get a safe report?\"\"\"\n",
-    "    return any(map(safe, drop_one(report)))\n",
+    "    return any(map(is_safe, drop_one(report)))\n",
     "\n",
     "def drop_one(report) -> Iterable:\n",
     "    \"\"\"All ways of dropping one element of the input report.\"\"\"\n",
@@ -313,7 +315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "d1b9ffb5-af7a-465f-a063-c31df2d0605c",
    "metadata": {},
    "outputs": [
@@ -323,14 +325,14 @@
        "Puzzle  2.2:   .0022 seconds, answer 328             ok"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "answer(2.2, 328, lambda:\n",
-    "       quantify(reports, safe_with_dampener))"
+    "       quantify(reports, is_safe_with_dampener))"
    ]
   },
   {
@@ -345,7 +347,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "id": "78080200-0f9f-4492-9bee-c936737ee96f",
    "metadata": {},
    "outputs": [
@@ -384,37 +386,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "id": "bf6366b1-6952-47d8-8b3c-09f8d05ec093",
    "metadata": {},
    "outputs": [],
    "source": [
     "def execute(program: str) -> int:\n",
     "    \"\"\"The sum of the results of the multiply instructions.\"\"\"\n",
-    "    return sum(prod(ints(m)) for m in multiplications(program))\n",
+    "    return sum(prod(ints(m)) for m in re.findall(multiplications, program))\n",
     "\n",
-    "def multiplications(program: str) -> List[str]:\n",
-    "    \"\"\"A list of all the multiplication instructions in the program.\"\"\"\n",
-    "    return re.findall(r'mul\\(\\d+,\\d+\\)', program)\n",
-    "\n",
-    "test = \"xmul(2,4)%&mul[3,7]!@^do_not_mul(5,5)+mul(32,64]then(mul(11,8)mul(8,5))\"\n",
-    "assert execute(test) == 161\n",
-    "assert multiplications(test) == ['mul(2,4)', 'mul(5,5)', 'mul(11,8)', 'mul(8,5)']"
+    "multiplications = r'mul\\(\\d+,\\d+\\)'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "id": "2032c903-5f23-4c16-ba68-410b6c1750e1",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Puzzle  3.1:   .0006 seconds, answer 156388521       ok"
+       "Puzzle  3.1:   .0005 seconds, answer 156388521       ok"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -436,24 +432,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "id": "4525d01a-bac0-41c2-92b8-baf0fd395e88",
    "metadata": {},
    "outputs": [],
    "source": [
     "def enabled(program: str) -> str:\n",
     "    \"\"\"Just the part of the program that is enabled; remove \"don't()...do()\" text.\"\"\"\n",
-    "    return re.sub(r\"don't\\(\\).*?(do\\(\\)|$)\", \" \", program)\n",
-    "\n",
-    "test2 = \"xmul(2,4)&mul[3,7]!^don't()_mul(5,5)+mul(32,64](mul(11,8)undo()?mul(8,5))\"\n",
-    "assert enabled(test2) == 'xmul(2,4)&mul[3,7]!^ ?mul(8,5))'\n",
-    "assert execute(enabled(test2)) ==  2 * 4 + 8 * 5 == 48\n",
-    "assert multiplications(enabled(test2)) == ['mul(2,4)', 'mul(8,5)']"
+    "    return re.sub(r\"don't\\(\\).*?(do\\(\\)|$)\", \" \", program)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "id": "ce40f258-ca76-48c3-9965-27a6979a4243",
    "metadata": {},
    "outputs": [
@@ -463,7 +454,7 @@
        "Puzzle  3.2:   .0004 seconds, answer 75920122        ok"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -485,7 +476,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "id": "a0d903b9-018e-4861-9314-cafed59055fd",
    "metadata": {},
    "outputs": [
@@ -524,7 +515,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "id": "72d48abb-7a82-452f-b91d-838b3836a90f",
    "metadata": {},
    "outputs": [],
@@ -543,17 +534,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "id": "6175362b-d8b4-45d1-b70c-d8575a0fe188",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Puzzle  4.1:   .0338 seconds, answer 2401            ok"
+       "Puzzle  4.1:   .0337 seconds, answer 2401            ok"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -576,22 +567,22 @@
     "     .A.     .A.     .A.     .A.\n",
     "     M.S     S.M     S.S     M.M\n",
     "\n",
-    "I decided to find these by looking for each instance of the middle letter (\"A\") in the grid, and then, for each pair of diagonal directions, see if the target word (\"MAS\") can be spelled in both directions:"
+    "I decided to find these by first looking for each the middle letter (\"A\") in the grid, and then, for each \"A\" and for each of the four pairs of diagonal directions, see if the target word (\"MAS\") can be spelled in both directions:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 32,
    "id": "ff7540fd-b5cb-4d02-810d-5c77da2bd9f4",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Puzzle  4.2:   .0259 seconds, answer 1822            ok"
+       "Puzzle  4.2:   .0266 seconds, answer 1822            ok"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -626,7 +617,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 34,
    "id": "b77a5a1f-a43b-4ce8-a60c-94d69a595505",
    "metadata": {},
    "outputs": [
@@ -676,10 +667,18 @@
    "source": [
     "manual  = parse(5, sections=paragraphs)\n",
     "rules   = set(parse(manual[0], ints))\n",
-    "updates = parse(manual[1], ints)\n",
-    "\n",
+    "updates = parse(manual[1], ints)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "4c85a23e-686a-4129-a14c-ff6f6a88b9ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "assert (48, 39) in rules # `rules` is a set of (earlier, later) page number pairs\n",
-    "assert updates[0] == (61,58,51,32,12,14,71) # `updates` is a sequence of page number tuples"
+    "assert updates[0] == (61, 58, 51, 32, 12, 14, 71) # `updates` is a sequence of page number tuples"
    ]
   },
   {
@@ -719,7 +718,7 @@
     {
      "data": {
       "text/plain": [
-       "Puzzle  5.1:   .0006 seconds, answer 5762            ok"
+       "Puzzle  5.1:   .0007 seconds, answer 5762            ok"
       ]
      },
      "execution_count": 38,
@@ -740,7 +739,7 @@
     "### Part 2: What do you get if you add up the middle page numbers after correctly re-ordering the incorrect updates?\n",
     "\n",
     "In Part 2 we have to find the incorrect updates, re-order them into a correct order, and again sum the middle page numbers.\n",
-    "Since I have already defined `is_correct`, i could just generate all permutations of each update and find one that `is_correct`. That would work great if the longest update is only 5 pages long, as it is in the example input. But what is the longest update in the actual input?"
+    "Since I have already defined `is_correct`, i could just generate all permutations of each update and find one that `is_correct`. That would work great if the longest update consists of only 5 pages, as it does in the example input. But what is the longest update in the actual input?"
    ]
   },
   {
@@ -773,7 +772,7 @@
     "\n",
     "- `sum_of_corrected_middles` will find the incorrect rules, perform a correction on each, and sum the middle numbers.\n",
     "- `correction` will sort an update, obeying the rules. It used to be that Python's `sort` method allowed a `cmp` keyword to compare two values; there is vestigial support for this with the `functools.cmp_to_key` function. I will **sort** each update so that page *m* comes before page *n* if (*m*, *n*) is in the rules.\n",
-    "- Sorting will be a sextillion times faster than enumerating permutations."
+    "- Sorting will be BOUT a sextillion times faster than enumerating permutations."
    ]
   },
   {
@@ -804,7 +803,7 @@
     {
      "data": {
       "text/plain": [
-       "Puzzle  5.2:   .0008 seconds, answer 4130            ok"
+       "Puzzle  5.2:   .0009 seconds, answer 4130            ok"
       ]
      },
      "execution_count": 43,
@@ -822,7 +821,7 @@
    "id": "53b1ccbc-01ae-43d0-a75f-3f9389fdd3c9",
    "metadata": {},
    "source": [
-    "I have to say, I'm pleased that this day I got both parts right the first time (and in fact, the same for the previous days). I was worried I might have my `+1` and `-1` backwards in `cmp_to_key`, but so far, everything has gone very smoothly. (However, even if I started solving right at midnight (which I don't), I don't think I would show up on the leaderboard; I have been good at getting a correct answer the first time, but I'm still way slower than the skilled contest programmers."
+    "I have to say, I'm pleased that this day I got both parts right with no errors (and in fact, the same for the previous days). I was worried I might have my `+1` and `-1` backwards in `cmp_to_key`, but so far, everything has gone smoothly. (However, even if I started solving the second the puzzles are released (which I don't), I wouldn't show up on the leaderboard;  I'm still *way* slower than the skilled contest programmers."
    ]
   },
   {
@@ -880,34 +879,41 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "id": "aecb67fd-2c8a-40a8-9e67-e495b7cb8043",
+   "id": "95f0b409-a6d6-47bc-8ce5-1c2df80f2b18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def follow_path(grid: Grid, guard='^', facing=North) -> List[Point]:\n",
+    "    \"\"\"A list of all points in the path followed by the guard.\"\"\"\n",
+    "    path = grid.findall(guard) # A one-element list of positions, e.g. [(3, 4)]\n",
+    "    while (ahead := add2(path[-1], facing)) in grid:\n",
+    "        if grid[ahead] == '#':\n",
+    "            facing = make_turn(facing, 'R')\n",
+    "        else:\n",
+    "            path.append(ahead)\n",
+    "    return path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "f4be3d1f-7f24-4d55-8221-df0026178e1e",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Puzzle  6.1:   .0014 seconds, answer 5329            ok"
+       "Puzzle  6.1:   .0016 seconds, answer 5329            ok"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "def follow_path(grid: Grid, guard='^', facing=North) -> List[Point]:\n",
-    "    \"\"\"A list of all points in the path followed by the guard.\"\"\"\n",
-    "    path = grid.findall(guard) # A one-element list of positions, e.g. [(3, 4)]\n",
-    "    while True:\n",
-    "        ahead = add2(path[-1], facing)\n",
-    "        if ahead not in grid:\n",
-    "            return path\n",
-    "        elif grid[ahead] == '#':\n",
-    "            facing = make_turn(facing, 'R')\n",
-    "        else:\n",
-    "            path.append(ahead)\n",
-    "\n",
-    "answer(6.1, 5329, lambda: len(set(follow_path(lab_grid))))"
+    "answer(6.1, 5329, lambda: \n",
+    "       len(set(follow_path(lab_grid))))"
    ]
   },
   {
@@ -930,7 +936,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 51,
    "id": "1718fecb-aa3e-4162-9948-1c06d4ec5e8a",
    "metadata": {},
    "outputs": [],
@@ -938,17 +944,15 @@
     "def is_loopy_path(grid: Grid, guard_pos, facing=North) -> bool:\n",
     "    \"\"\"Does the path followed by the guard form a loop?\"\"\"\n",
     "    path = {(guard_pos, facing)}\n",
-    "    while True:\n",
-    "        ahead = add2(guard_pos, facing)\n",
-    "        if ahead not in grid:\n",
-    "            return False # Walked off the grid; not a loop\n",
-    "        elif grid[ahead] == '#':\n",
+    "    while (ahead := add2(guard_pos, facing)) in grid:\n",
+    "        if grid[ahead] == '#':\n",
     "            facing = make_turn(facing, 'R')\n",
     "            if (guard_pos, facing) in path:\n",
     "                return True\n",
     "            path.add((guard_pos, facing))\n",
     "        else:\n",
     "            guard_pos = ahead\n",
+    "    return False\n",
     "    \n",
     "def find_loopy_obstacles(grid: Grid) -> Iterable[Point]:\n",
     "    \"\"\"All positions in which placing an obstacle would result in a loopy path for the guard.\"\"\"\n",
@@ -962,17 +966,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 52,
    "id": "36196264-eb33-4fc0-95d5-06c985105ebf",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Puzzle  6.2:  1.9151 seconds, answer 2162            ok"
+       "Puzzle  6.2:  1.9691 seconds, answer 2162            ok"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1002,7 +1006,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 55,
    "id": "c1c6cee8-122c-43c9-8c7d-ed8980ea2b76",
    "metadata": {},
    "outputs": [
@@ -1063,7 +1067,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 58,
    "id": "6fa3907c-0e1a-4d4a-9fc3-f809b9325674",
    "metadata": {},
    "outputs": [
@@ -1073,7 +1077,7 @@
        "13"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1092,7 +1096,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 60,
    "id": "5dfe0edf-cf29-4623-bb2c-6180f832f4d7",
    "metadata": {},
    "outputs": [],
@@ -1108,17 +1112,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 61,
    "id": "3085596d-f5ec-4ba8-b05a-cf70cf276a0c",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Puzzle  7.1:   .0217 seconds, answer 1985268524462   ok"
+       "Puzzle  7.1:   .0211 seconds, answer 1985268524462   ok"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1140,17 +1144,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 63,
    "id": "5bdcb999-7f38-4814-bca5-13db88f4e214",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Puzzle  7.2:  1.1006 seconds, answer 150077710195188 ok"
+       "Puzzle  7.2:  1.0677 seconds, answer 150077710195188 ok"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1169,12 +1173,12 @@
    "source": [
     "# [Day 8](https://adventofcode.com/2024/day/8): Resonant Collinearity\n",
     "\n",
-    "Another grid input, this one a map of antenna locations. Each different non-\".\" character denotes an antenna of a given frequency."
+    "Another grid input, this one a map of antenna locations. Each different non-\"`.`\" character denotes an antenna of a given frequency."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 65,
    "id": "cf6361a7-e3bc-42ec-ae16-f9eec166055e",
    "metadata": {},
    "outputs": [
@@ -1215,7 +1219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 67,
    "id": "22180ce8-5d03-4aee-8c73-62f2afbddf71",
    "metadata": {},
    "outputs": [],
@@ -1236,17 +1240,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 68,
    "id": "dd173ce9-cbbb-4282-b43f-c7cff662bd90",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Puzzle  8.1:   .0026 seconds, answer 220             ok"
+       "Puzzle  8.1:   .0027 seconds, answer 220             ok"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1270,7 +1274,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 70,
    "id": "d30f8ce9-f186-46a0-a2e7-f74eceae6905",
    "metadata": {},
    "outputs": [],
@@ -1291,17 +1295,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 71,
    "id": "6bf85b57-8b8f-4196-9903-6d5fe082f404",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Puzzle  8.1:   .0025 seconds, answer 220             ok"
+       "Puzzle  8.1:   .0028 seconds, answer 220             ok"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1313,7 +1317,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 72,
    "id": "f232952c-5fc6-4696-a8b1-d0b54137ac02",
    "metadata": {},
    "outputs": [
@@ -1323,7 +1327,7 @@
        "Puzzle  8.2:   .0030 seconds, answer 813             ok"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1353,7 +1357,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 75,
    "id": "0e944f9e-5c16-440c-b12e-178058a87048",
    "metadata": {},
    "outputs": [
@@ -1394,7 +1398,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 77,
    "id": "76e8454d-a2f3-4b6b-92df-182116cf46e0",
    "metadata": {},
    "outputs": [],
@@ -1403,12 +1407,9 @@
     "\n",
     "def disk_layout(disk_map: Ints) -> list:\n",
     "    \"\"\"Convert a disk map into a disk layout.\"\"\"\n",
-    "    layout = []\n",
-    "    for id, i in enumerate(range(0, len(disk_map), 2)):\n",
-    "        layout.extend(disk_map[i] * [id])\n",
-    "        if i + 1 < len(disk_map):\n",
-    "            layout.extend(disk_map[i + 1] * [empty])\n",
-    "    return layout\n",
+    "    def empties(i): return (disk_map[i] * [empty] if i < len(disk_map) else [])\n",
+    "    return append(disk_map[i] * [id] + empties(i + 1)\n",
+    "                  for id, i in enumerate(range(0, len(disk_map), 2)))\n",
     "\n",
     "def compress_layout(layout: list) -> list:\n",
     "    \"\"\"Mutate layout by moving blocks one at a time from the end to the leftmost free space.\"\"\"\n",
@@ -1429,17 +1430,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 78,
    "id": "2aa7e2b9-844e-49ed-b41b-4a4cecff86b7",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Puzzle  9.1:   .0193 seconds, answer 6332189866718   ok"
+       "Puzzle  9.1:   .0200 seconds, answer 6332189866718   ok"
       ]
      },
-     "execution_count": 77,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1467,7 +1468,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 80,
    "id": "fcf4d832-3d7d-4987-aa57-e6e0f1df16bf",
    "metadata": {},
    "outputs": [],
@@ -1476,8 +1477,7 @@
     "    \"\"\"Mutate layout by moving files one at a time from the end to the leftmost free space.\"\"\"\n",
     "    layout = disk_layout(disk_map)\n",
     "    for file in file_slices(disk_map):\n",
-    "        free = find_freespace(layout, file)\n",
-    "        if free:\n",
+    "        if free := find_freespace(layout, file):\n",
     "            layout[file], layout[free] = layout[free], layout[file]\n",
     "    return layout\n",
     "\n",
@@ -1510,17 +1510,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 81,
    "id": "e3036875-88d0-496e-9d2f-facd0e80a5b2",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Puzzle  9.2:  2.6956 seconds, answer 6353648390778   ok"
+       "Puzzle  9.2:  2.6991 seconds, answer 6353648390778   ok"
       ]
      },
-     "execution_count": 80,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1550,7 +1550,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 84,
    "id": "5804fb03-05f3-402f-b6cc-6804c5d22512",
    "metadata": {},
    "outputs": [
@@ -1603,7 +1603,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 86,
    "id": "76b5379e-ee19-4607-91b8-88ec7b38023f",
    "metadata": {},
    "outputs": [],
@@ -1619,17 +1619,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 87,
    "id": "97cf05f7-fa56-4a90-b2d8-2cd4d9b81f95",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Puzzle 10.1:   .0046 seconds, answer 744             ok"
+       "Puzzle 10.1:   .0050 seconds, answer 744             ok"
       ]
      },
-     "execution_count": 86,
+     "execution_count": 87,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1653,7 +1653,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 89,
    "id": "b763450f-a565-4936-bee4-e531c2eeebdb",
    "metadata": {},
    "outputs": [],
@@ -1670,17 +1670,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 90,
    "id": "f8a87032-6556-4fc9-9bb8-573611aee8dc",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Puzzle 10.2:   .0057 seconds, answer 1651            ok"
+       "Puzzle 10.2:   .0059 seconds, answer 1651            ok"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 90,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1695,7 +1695,38 @@
    "id": "af410d30-7096-4be6-bb20-904b3c8e2f59",
    "metadata": {},
    "source": [
-    "Today I went pretty fast (for me); I started a few minutes late and finished in 15 minutes. From the point of view of a competitive coder I did foolish things like write docstrings and use variables of more than one letter, so while this time was fast for me, it placed well out of the top 1000."
+    "Today I went pretty fast (for me); I started a few minutes late and finished in 15 minutes. From the point of view of a competitive coder I did foolish things like write docstrings and use variables of more than one letter, so while this time was fast for me, it placed well out of the top 1000.\n",
+    "\n",
+    "Here's a visualization of the map:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "4b35defa-a19e-46c5-bd04-3af55bea14e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_topo(topo):\n",
+    "    plt.figure()\n",
+    "    scatter = plt.scatter(Xs(topo), Ys(topo), c=list(topo.values()), \n",
+    "                          cmap='coolwarm', marker='s', s=12)\n",
+    "    plt.colorbar(scatter, label='Elevation')\n",
+    "    plt.axis('square')\n",
+    "    plt.axis('off')\n",
+    "\n",
+    "plot_topo(topo)"
    ]
   },
   {
@@ -1710,7 +1741,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 94,
    "id": "76b68cef-d8de-4145-b65c-b254fedf1671",
    "metadata": {},
    "outputs": [
@@ -1754,7 +1785,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 96,
    "id": "1513df56-3d6f-42cf-8aec-1bdbeb991d90",
    "metadata": {},
    "outputs": [],
@@ -1780,17 +1811,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 97,
    "id": "eff17cd0-a2c7-4d69-bc55-c0ef97917915",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Puzzle 11.1:   .0626 seconds, answer 194482          ok"
+       "Puzzle 11.1:   .0680 seconds, answer 194482          ok"
       ]
      },
-     "execution_count": 95,
+     "execution_count": 97,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1816,7 +1847,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 99,
    "id": "707b5a97-0296-48df-bdab-e34064cc67c2",
    "metadata": {},
    "outputs": [],
@@ -1841,17 +1872,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 101,
    "id": "efdcdbf8-e8ec-4a85-9d09-90a20e08c66a",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Puzzle 11.1:   .0015 seconds, answer 194482          ok"
+       "Puzzle 11.1:   .0016 seconds, answer 194482          ok"
       ]
      },
-     "execution_count": 99,
+     "execution_count": 101,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1863,17 +1894,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 102,
    "id": "657b1f13-ffcc-44c6-84f1-398fa2fcdac7",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Puzzle 11.2:   .0548 seconds, answer 232454623677743 ok"
+       "Puzzle 11.2:   .0609 seconds, answer 232454623677743 ok"
       ]
      },
-     "execution_count": 100,
+     "execution_count": 102,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1903,7 +1934,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 105,
    "id": "8161ee7e-76e3-499a-abf8-a607991c9602",
    "metadata": {},
    "outputs": [
@@ -1942,7 +1973,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 107,
    "id": "79f91f38-e325-44f2-9e53-b64ce12d9d35",
    "metadata": {},
    "outputs": [],
@@ -1971,7 +2002,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 109,
    "id": "1fbabbfb-50c8-4197-8517-e7cee9582765",
    "metadata": {},
    "outputs": [],
@@ -1995,17 +2026,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 110,
    "id": "cdaf655b-d12c-4973-b19b-3132e5e691c6",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Puzzle 12.1:   .0307 seconds, answer 1402544         ok"
+       "Puzzle 12.1:   .0332 seconds, answer 1402544         ok"
       ]
      },
-     "execution_count": 108,
+     "execution_count": 110,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2032,20 +2063,20 @@
     "- A point with an edge to the South whose neighbor to the West also has an edge to the South\n",
     "- A point with an edge to the West whose neighbor to the North also has an edge to the West\n",
     "\n",
-    "Here's a diagram of a region of \"`X`\" crops with a \"`-`\" marking each place where a perimeter piece would be subtracted.\n",
+    "Here are two diagrams of two regions with \"`X`\" and \"`W`\" crops with a \"`-`\" marking each place where a perimeter piece would be subtracted.\n",
     "\n",
-    "     .X-...\n",
-    "     -X-.X-\n",
-    "     -XXXX-\n",
-    "     ..XXX.\n",
-    "     ...--.\n",
+    "     .X-...    .-------..\n",
+    "     -X-.X-    .WWWWWWWW-\n",
+    "     -XXXX-    -WWWWWWWW-\n",
+    "     ..XXX.    -WWWWWWWW.\n",
+    "     ...--.    ..-------.\n",
     "\n",
-    "Again, I'll parameterize `fence_price` to take a `region_price` parameter:"
+    "I'll parameterize `fence_price` to take a `region_price` parameter, and show that the change is backwards compatible to Part 1:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 112,
    "id": "38c30e15-3a33-40c2-b734-163a15af7a8a",
    "metadata": {},
    "outputs": [],
@@ -2068,17 +2099,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 113,
    "id": "72175812-dcd0-4f1b-9efa-0dceeeafa609",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Puzzle 12.1:   .0302 seconds, answer 1402544         ok"
+       "Puzzle 12.1:   .0307 seconds, answer 1402544         ok"
       ]
      },
-     "execution_count": 111,
+     "execution_count": 113,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2090,17 +2121,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 114,
    "id": "9defcd35-91bc-41d4-a16f-bb7a4ede75e7",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Puzzle 12.2:   .0426 seconds, answer 862486          ok"
+       "Puzzle 12.2:   .0436 seconds, answer 862486          ok"
       ]
      },
-     "execution_count": 112,
+     "execution_count": 114,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2110,6 +2141,278 @@
     "       fence_price(farm, discount_region_price))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "faa9d750-a619-40dc-965c-a9a8e2543e32",
+   "metadata": {},
+   "source": [
+    "# [Day 13](https://adventofcode.com/2024/day/13): Claw Contraption\n",
+    "\n",
+    "Today's puzzle involves arcade claw machines. Each input paragraph describes a machine with two buttons, **A** and **B**; every push of a button moves the claw a specified amount in the X and Y directions. The prize is at a specified location. We can parse the input by defining a `parse_claw` function and a `Claw` datatype to represent a claw machine:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "id": "e78f45c0-c420-4661-aad2-14e122b4473b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "────────────────────────────────────────────────────────────────────────────────────────────────────\n",
+      "Puzzle input ➜ 1279 strs:\n",
+      "────────────────────────────────────────────────────────────────────────────────────────────────────\n",
+      "Button A: X+24, Y+19\n",
+      "Button B: X+32, Y+86\n",
+      "Prize: X=1072, Y=2062\n",
+      "\n",
+      "Button A: X+70, Y+12\n",
+      "Button B: X+11, Y+43\n",
+      "Prize: X=3400, Y=3872\n",
+      "\n",
+      "...\n",
+      "────────────────────────────────────────────────────────────────────────────────────────────────────\n",
+      "Parsed representation ➜ 320 Claws:\n",
+      "────────────────────────────────────────────────────────────────────────────────────────────────────\n",
+      "Claw(A=(24, 19), B=(32, 86), Prize=(1072, 2062))\n",
+      "Claw(A=(70, 12), B=(11, 43), Prize=(3400, 3872))\n",
+      "Claw(A=(48, 77), B=(34, 12), Prize=(6510, 1583))\n",
+      "Claw(A=(76, 60), B=(29, 88), Prize=(5506, 6300))\n",
+      "Claw(A=(16, 75), B=(68, 20), Prize=(17148, 13490))\n",
+      "Claw(A=(46, 13), B=(33, 63), Prize=(15572, 9644))\n",
+      "Claw(A=(21, 79), B=(44, 32), Prize=(2433, 5147))\n",
+      "Claw(A=(13, 45), B=(36, 11), Prize=(4612, 19164))\n",
+      "...\n"
+     ]
+    }
+   ],
+   "source": [
+    "Claw = namedtuple('Claw', 'A, B, Prize') # A claw machine\n",
+    "\n",
+    "def parse_claw(text: str) -> Claw:\n",
+    "    \"\"\"Parse a description into a Claw object.\"\"\"\n",
+    "    ax, ay, bx, by, px, py = ints(text)\n",
+    "    return Claw((ax, ay), (bx, by), (px, py))\n",
+    "    \n",
+    "claws = parse(13, parse_claw, sections=paragraphs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f6da888-9288-42f1-bc51-2eb2a66d34a1",
+   "metadata": {},
+   "source": [
+    "### Part 1: What is the fewest tokens you would have to spend to win all possible prizes?\n",
+    "\n",
+    "We are told that for some machines it is possible to reach the prize and others it is impossible. We are also told that you need to spend 3 tokens to  press  the **A** button and 1 token to press the **B** button. There is a hint that no button will need to be pushed more than 100 times. It is usually a good idea to accept the hint, so I will  do the following:\n",
+    "- Exhaustively try from every number from 0 to 100 presses of the **A** button.\n",
+    "- For each number, figure out what the resulting position of the claw would be after those presses.\n",
+    "- Calculate how many presses of the **B** button would be required to get to the prize's X location.\n",
+    "- If the number of presses is an integer, and would also arrive at the prize's Y location, then yield that solution.\n",
+    "- Out of these solutions, the cheapest solution is  the one that needs the fewest tokens.\n",
+    "- (If there are no solutions, `cheapest_solution` will return 0, whcih is convenient for adding the results up.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "id": "c2c4bbc9-42cd-483d-8da2-97cf051e93fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def solve_claw(claw: Claw, limit=100) -> Iterable[Tuple[int, int]]:\n",
+    "    \"\"\"All possible (A-presses, B-presses) solutions to reach the prize on this machine.\"\"\"\n",
+    "    A, B, Prize = claw\n",
+    "    for Apresses in range(limit + 1):\n",
+    "        pos = mul(A, Apresses)\n",
+    "        if X_(pos) > X_(Prize) or Y_(pos) > Y_(Prize):\n",
+    "            return\n",
+    "        diff = sub2(Prize, pos)\n",
+    "        Bpresses = X_(diff) / X_(B)\n",
+    "        if Bpresses.is_integer() and Bpresses * Y_(B) == Y_(diff):\n",
+    "            yield (Apresses, int(Bpresses))\n",
+    "\n",
+    "def cheapest_solution(claw: Claw) -> int: \n",
+    "    \"\"\"The minimum cost out of every possible solution to this claw machine.\"\"\"\n",
+    "    return min((3 * A + 1 * B for (A, B) in solve_claw(claw)), default=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "id": "f5638ed4-1e59-4b9f-b1fc-427d2eb0d036",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Puzzle 13.1:   .0098 seconds, answer 29598           ok"
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "answer(13.1, 29598, lambda:\n",
+    "       sum(map(cheapest_solution, claws)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a36e76c-dd5b-4d80-ae88-9980e7d274ac",
+   "metadata": {},
+   "source": [
+    "### Part 2: What is the fewest tokens you would have to spend to win all possible prizes, now?\n",
+    "\n",
+    "In Part 2, we discover that the prize is actually much farther away, by 10,000,000,000,000 (ten trillion) steps in both the X and Y coordinates. Theoretically, we could still use `solve_claw` with a bigger `limit`, but that would take a *long* time.\n",
+    "\n",
+    "Instead I can solve each claw machine *mathematically* rather than by trial and error. I know that an equation over the integers is called a [Diophantine equation](https://en.wikipedia.org/wiki/Diophantine_equation), but I don't remember much about them.  What are the equations of a claw machine? For my first machine, \n",
+    "\n",
+    "     Button A: X+24, Y+19\n",
+    "     Button B: X+32, Y+86\n",
+    "     Prize: X=1072, Y=2062\n",
+    "\n",
+    "there are two linear equations to solve simultaneously:\n",
+    "\n",
+    "     24 A + 32 B = 1072\n",
+    "     19 A + 86 B = 2062\n",
+    "\n",
+    "In general, two linear equations in two unknowns can have zero solutions (if the lines are parallel), an infinite number of solutions (if the lines are the same), or exactly one solution (otherwise). The further constraint that *A* and *B* have to be non-negative integers may rule out some of these solutions, but mostly we are looking for the one point where two lines intersect.\n",
+    "\n",
+    "I defined `solve_claw` to yield multiple solutions, but let's see how many solutions each claw machine yields:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "id": "df8da2ae-52f9-409b-a54f-ad7d21b32e45",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Counter({0: 168, 1: 152})"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Counter(quantify(solve_claw(c)) for c in claws)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0f97d47-5741-4c6e-ae96-5b3161ee2e56",
+   "metadata": {},
+   "source": [
+    "So, what is the intersection point of two lines? You could [look it up](https://www.wolframalpha.com/input?i=solve+for+a%2C+b%3A+p+*+a+%2B+q+*+b+%3D+r++and+++s+*+a+%2B+t+*+b+%3D+u) or work it out by hand. Given the two equations over the two variables *a* and *b*:\n",
+    "\n",
+    "    p * a + q * b = r\n",
+    "    s * a + t * b = u\n",
+    "\n",
+    "The solution is:\n",
+    "\n",
+    "    a = (r * t - q * u) / (p * t - q * s)\n",
+    "    b = (r * s - p * u) / (q * s - p * t)\n",
+    "\n",
+    "So we can rewrite `solve_claw` to use this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "6bbd0934-d962-4c93-940b-810651e9e568",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def solve_claw(claw: Claw) -> Iterable[Tuple[int, int]]:\n",
+    "    \"\"\"All possible (A-presses, B-presses) solutions to reach the prize on this machine.\"\"\"\n",
+    "    ((p, s), (q, t), (r, u)) = claw\n",
+    "    a = (r * t - q * u) / (p * t - q * s)\n",
+    "    b = (r * s - p * u) / (q * s - p * t)\n",
+    "    if a >= 0 and b >= 0 and a.is_integer() and b.is_integer():\n",
+    "        yield (int(a), int(b))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af52d98e-50e9-4c4d-875f-1ed2b3dc8111",
+   "metadata": {},
+   "source": [
+    "We'll need a function to move the claws further away, then we're ready to answer Part 2:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "dd38ba4c-44ba-426b-b1c8-0e10adbdd642",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def move_prize(claw, delta=(10**13, 10**13)) -> Claw:\n",
+    "    \"\"\"Move the claw's prize by the given displacement, delta.\"\"\"\n",
+    "    return claw._replace(Prize=(add(claw.Prize, delta)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "id": "9f578b3e-6b6d-4eb0-9228-c98122a84747",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Puzzle 13.2:   .0004 seconds, answer 93217456941970  ok"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "answer(13.2, 93217456941970, lambda:\n",
+    "       sum(cheapest_solution(move_prize(claw)) for claw in claws))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db917eed-07e5-4409-a92d-b26534a1fcd4",
+   "metadata": {},
+   "source": [
+    "The refactored `solve_claw` is compatible with Part 1, and should be a bit faster:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "id": "609ed4ce-548c-4af4-8e09-c621aca0124e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Puzzle 13.1:   .0002 seconds, answer 29598           ok"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "answer(13.1, 29598, lambda:\n",
+    "       sum(map(cheapest_solution, claws)))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "c3317844-2b4a-4756-8a59-b765aa467445",
@@ -2122,7 +2425,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 130,
    "id": "34813fc9-a000-4cd8-88ae-692851b3242c",
    "metadata": {},
    "outputs": [
@@ -2130,30 +2433,32 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Puzzle  1.1:   .0002 seconds, answer 1830467         ok\n",
+      "Puzzle  1.1:   .0001 seconds, answer 1830467         ok\n",
       "Puzzle  1.2:   .0001 seconds, answer 26674158        ok\n",
       "Puzzle  2.1:   .0004 seconds, answer 257             ok\n",
       "Puzzle  2.2:   .0022 seconds, answer 328             ok\n",
-      "Puzzle  3.1:   .0006 seconds, answer 156388521       ok\n",
+      "Puzzle  3.1:   .0005 seconds, answer 156388521       ok\n",
       "Puzzle  3.2:   .0004 seconds, answer 75920122        ok\n",
-      "Puzzle  4.1:   .0338 seconds, answer 2401            ok\n",
-      "Puzzle  4.2:   .0259 seconds, answer 1822            ok\n",
-      "Puzzle  5.1:   .0006 seconds, answer 5762            ok\n",
-      "Puzzle  5.2:   .0008 seconds, answer 4130            ok\n",
-      "Puzzle  6.1:   .0014 seconds, answer 5329            ok\n",
-      "Puzzle  6.2:  1.9151 seconds, answer 2162            ok\n",
-      "Puzzle  7.1:   .0217 seconds, answer 1985268524462   ok\n",
-      "Puzzle  7.2:  1.1006 seconds, answer 150077710195188 ok\n",
-      "Puzzle  8.1:   .0025 seconds, answer 220             ok\n",
+      "Puzzle  4.1:   .0337 seconds, answer 2401            ok\n",
+      "Puzzle  4.2:   .0266 seconds, answer 1822            ok\n",
+      "Puzzle  5.1:   .0007 seconds, answer 5762            ok\n",
+      "Puzzle  5.2:   .0009 seconds, answer 4130            ok\n",
+      "Puzzle  6.1:   .0016 seconds, answer 5329            ok\n",
+      "Puzzle  6.2:  1.9691 seconds, answer 2162            ok\n",
+      "Puzzle  7.1:   .0211 seconds, answer 1985268524462   ok\n",
+      "Puzzle  7.2:  1.0677 seconds, answer 150077710195188 ok\n",
+      "Puzzle  8.1:   .0028 seconds, answer 220             ok\n",
       "Puzzle  8.2:   .0030 seconds, answer 813             ok\n",
-      "Puzzle  9.1:   .0193 seconds, answer 6332189866718   ok\n",
-      "Puzzle  9.2:  2.6956 seconds, answer 6353648390778   ok\n",
-      "Puzzle 10.1:   .0046 seconds, answer 744             ok\n",
-      "Puzzle 10.2:   .0057 seconds, answer 1651            ok\n",
-      "Puzzle 11.1:   .0015 seconds, answer 194482          ok\n",
-      "Puzzle 11.2:   .0548 seconds, answer 232454623677743 ok\n",
-      "Puzzle 12.1:   .0302 seconds, answer 1402544         ok\n",
-      "Puzzle 12.2:   .0426 seconds, answer 862486          ok\n"
+      "Puzzle  9.1:   .0200 seconds, answer 6332189866718   ok\n",
+      "Puzzle  9.2:  2.6991 seconds, answer 6353648390778   ok\n",
+      "Puzzle 10.1:   .0050 seconds, answer 744             ok\n",
+      "Puzzle 10.2:   .0059 seconds, answer 1651            ok\n",
+      "Puzzle 11.1:   .0016 seconds, answer 194482          ok\n",
+      "Puzzle 11.2:   .0609 seconds, answer 232454623677743 ok\n",
+      "Puzzle 12.1:   .0307 seconds, answer 1402544         ok\n",
+      "Puzzle 12.2:   .0436 seconds, answer 862486          ok\n",
+      "Puzzle 13.1:   .0002 seconds, answer 29598           ok\n",
+      "Puzzle 13.2:   .0004 seconds, answer 93217456941970  ok\n"
      ]
     }
    ],
diff --git a/ipynb/AdventUtils.ipynb b/ipynb/AdventUtils.ipynb
index c8f80d7..864f9a6 100644
--- a/ipynb/AdventUtils.ipynb
+++ b/ipynb/AdventUtils.ipynb
@@ -25,7 +25,7 @@
     "from itertools   import count as count_from, product as cross_product\n",
     "from typing      import *\n",
     "from statistics  import mean, median\n",
-    "from math        import ceil, floor, factorial, gcd, log, log2, log10, sqrt, inf\n",
+    "from math        import ceil, floor, factorial, gcd, log, log2, log10, sqrt, inf, atan2\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
     "import ast\n",
@@ -488,6 +488,10 @@
     "    \"\"\"Specialized version of point addition for 2D Points only. Faster.\"\"\"\n",
     "    return (p[0] + q[0], p[1] + q[1])\n",
     "\n",
+    "def sub2(p: Point, q: Point) -> Point: \n",
+    "    \"\"\"Specialized version of point subtraction for 2D Points only. Faster.\"\"\"\n",
+    "    return (p[0] - q[0], p[1] - q[1])\n",
+    "\n",
     "def taxi_distance(p: Point, q: Point) -> int:\n",
     "    \"\"\"Manhattan (L1) distance between two 2D Points.\"\"\"\n",
     "    return abs(p[0] - q[0]) + abs(p[1] - q[1])"
@@ -552,6 +556,11 @@
     "        return (0 <= X_(point) < X_(self.size) and\n",
     "                0 <= Y_(point) < Y_(self.size))\n",
     "\n",
+    "    def follow_line(self, start: Point, direction: Vector) -> Iterable[Point]:\n",
+    "        while self.in_range(start):\n",
+    "            yield start\n",
+    "            start = add2(start, direction)\n",
+    "\n",
     "    def copy(self): \n",
     "        return Grid(self, directions=self.directions, skip=self.skip, default=self.default)\n",
     "    \n",
@@ -878,7 +887,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.15"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,