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I'm the author of four nonfiction books, none about programming and none self-published, but I think some of my experience still holds. You're doing market res
by jawns 5y ago
I'm the author of four nonfiction books, none about programming and none self-published, but I think some of my experience still holds.
You're doing market research. That's great, and it's a step that a lot of people neglect to do before embarking on a book-length project. A traditional publisher would expect you to do a decent amount of this in your proposal, and the thing they're looking for is, "How is this different enough from everything that's already out there that it fills an unmet need, but similar enough to books that have sold well that we can expect it to sell well too?"
The other thing that a lot of would-be authors underestimate the importance of is their platform. Anybody can come up with a great book idea, far fewer can actually write it well... but the real differentiator is how big of an existing audience you have and how well you can sell it to them.
Finally, you've thrown out a grab bag of possible titles, but which of those are you truly passionate about? If you do commit to writing a book, it's like a marriage. It's going to be misery if you aren't genuinely interested in your partner.
- mjburgess 5y agoI have these issues in mind. The way i'm intending to solve them is via the self-publishing model. I have written large works before (including non-public books on Scala, R and effectively a couple textbooks on data science). What I'm aiming to do here is cautiously acquire a minor second income stream by taking a lot of my already well-formed thoughts on various areas, and create small and cheap PDFs that a few people would be interested in reading. I am very wary about "When Machine Learning Fails" as that feels like one of those magnum-opus books that takes years to write and a mental breakdown to finish. If I were to do that, I'd probably release a first chapter and mid-chapter as drafts to gauge interest. The first chapter would be a lighlty mathematical theory of learning & philosophy discussion, which should polarise enough people into like/dislike -- and a mid-section chapter would be a semi-math. semi-prog. case study popular write up of, probably, an academic paper.
- mkl 5y agoI think also the machine learning one is quite a bit riskier than the others due to how quickly the field is moving. Big failures you write about may be easily worked around by the time you finish, and even if everything is current when you finish, the book may have a pretty short shelf life.
- formerkrogemp 5y agoWhat do you mean by "non-public books?" Are these books that you haven't released or published but have already written?
- specialist 5y agoTangent: I really thought Bruce Eckel's strategy for writing would become the norm. Especially for technical topics. Especially with publishers like O'Reilly. (You may recall Eckel's Thinking in Java and Thinking in C++.) Eckel wrote in public, released early drafts, actively sought and incorporated feedback and errata. As a former author of shareware, this strategy resonated with me. As an author yourself, any thoughts on why Eckel's strategy didn't catch on? eg Too much effort, publishers wouldn't buy-in, just a weird idea...? FWIW, I consumed those drafts asap, and provided (very) modest feedback, so I was heavily emotionally invested in Eckel's success. Once available in paper, I bought many copies; at least 5 alone as gifts.