_wp_

There's quite a bit of overhead in execing and piping data from ffmpeg. If performance is a concern, then using the C ffmpeg API is a better bet(see pyav for a good example of this) . Cool project though!

I didn't say that the book's approach contradicted TDD, I'm merely quoting from the book and refuting one its claims (that TDD doesn't lead to good design). I agree that narrow and deep modules support refactoring internals if their unit tests are written to treat them as black boxes.

I strongly agree with your first two points. Good naming supplants the need for commenting. I find diagrams a far terser communication tool of architecture than comments.

I bought this book off the back of the referenced discussion trashing Clean Code. Whilst Clean Code has its problems, well articulated in that previous discussion, I am loathe to recommend this book. A large part of it is dedicated to commenting practices and seems a bit out of touch with the way software is developed today. There were some rather dubious claims on TDD as well, suggesting that it aims to 'get features working, rather than finding the best design' which seems to completely ignore the refactoring phase practised in a TDD cycle. A choice quote about comments that I strongly disagreed with: "without comments, you cannot hide complexity". The book also strongly advocates for large classes and considers small ones an anti-pattern called 'classitis'.

I'd say half the book contained good advice, the other half was mediocre or dubious at best.

I'm curious to hear what others think who've read both books.

> To use people's random images for training, they would have to be manually annotated by a human (e.g. facial boxes, eyes, nose, mouth, ears drawn in).

That's not true. There is a large and growing body of research on semi-supervised, self-supervised, and unsupervised learning that can take advantage of these unlabelled images.

Allegedly around $200 source - https://www.omgubuntu.co.uk/2019/05/pinebook-pro-video-demo

The Atavist magazine version of this is available at: https://magazine.atavist.com/the-mastermind It's a fantastic (but long) read, I highly recommend it.

Could you provide an equivalent Makefile to the one written in the article to demonstrate best practices?

The repository is here: https://gitlab.com/open_nsfw/open_nsfw.gitlab.io/tree/master

I don't think that's particularly fair on Julia. I have found her posts very useful, even the ones written in this style. Documenting her journey from newbie to proficiency is very useful to others wanting to learn the topic in question as they can relate to the questions in posts like these and see whether she found answers later on in her archive.