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I've been facing similar issues recently and have been slowly drifting towards similar ideas mentioned in the article. Though, coming from a more traditional software dev background, I'm still in the process of internalizing how the popular start-up mantra-'build fast and fail fast' doesn't really work in ML.

Also, logging every experiment is another discipline I'm teaching myself. And I fell in love with org-mode in the process.

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When I was doing my PhD, I once resorted to turning on all logging, printing about 60 pages of the resulting output, taping them to the floor of the lab, and crawling around on all fours with a printout of the source code tracing the execution to try and prove to myself that a result I was seeing wasn't due to just a bug in the source code. In an odd way, it's maybe my fondest memory from my dissertation work.

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