Deciding how many nodes and layers to use in a neutral network: many functions can be expressed in 1 or 2 hidden layers, more is often better and faster, and you usually have to experiment.
https://machinelearningmastery.com/how-to-configure-the-number-of-layers-and-nodes-in-a-neural-network/
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Go has an elegant approach to defining example functions, which are shown in docs as `main()` with the output: https://go.dev/blog/examples
LLMs seem to handle dependency upgrades really well.
The task is well-specified, there's usually a build/test suite to check correctness of the modifications, and there's often a changelog they can consume too.
I'm never sure what to name my remotes in git. I tend to use 'mine' so I can add other forks later, but sometimes I use 'gh' or the traditional 'origin'.
What do others use?