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@ -23,6 +23,7 @@
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"- *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",
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"- *This is a huge improvement over just one year ago, when LLMs could not perform anywhere near this level.*\n",
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"- *The three LLMS seemed to be roughly equal in quality.*\n",
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"- *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",
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"- *The LLMs knew the things you would want an experienced software engineer to know:*\n",
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" - *How to see through the story about elves and christmas trees, etc. and get to the real programming issues*\n",
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" - *Standard Python syntax, builtin types, and basic modules (e.g. `collections`, `functools`, `typing`, `numpy`)*\n",
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@ -3955,7 +3956,7 @@
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"\n",
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"*The human-written code is about **five times more concise** than the LLM code.*\n",
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"\n",
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"*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.*"
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"*The LOC numbers are total lines of code, including blank lines, comments, and doc strings.*"
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]
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},
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{
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@ -3991,14 +3992,6 @@
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" | **TOTAL** | | **1732** | **356** | | \n",
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" | **MEAN** | | **75** | **14.5** | |"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "fdcde1d0-a999-4867-8b08-6e10a75fa385",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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