Tobias Buckell's "How I used Kickstarter to reboot a book series, and my career (and maybe my life?)" is a fantastic, detailed postmortem on his experiment with continuing his commercially flagging science fiction series by raising money directly from his fans on Kickstarter. As always, the most important part is the mistakes made/lessons learned:
I launched the project at noon. Because I was writing and fixing things that morning. So I set it to go live. Rookie. That meant I missed four hours of first day, the biggest day, of word-of-mouth and fundraising. The momentum was slow from day one. People love piling onto a winning project. Mine did not come out the gate strong for The Apocalypse Ocean. Next time, I set it to go live at 7am.
I set the eBook price too high. $25. It worked, because fans backed the project and jumped aboard. I think I could risk focusing harder on a $10 eBook. Then maybe a $25 trade paper, and then move up.
While I got backers their eBooks as fast as I could, roughly by the deadline, I vastly underestimated how long the project management of creating a print book would take. Physical copies had to be mailed around. Proofed. Schedules had to be lined up. It was all… fiddly. I thought August/September I would have books delivered. Instead, it ended up being early December.
This is a must-read for anyone contemplating a similar audience-funded artistic project. Here's the book, which, knowing Toby's work, is bound to be excellent.
How I used Kickstarter to reboot a book series, and my career (and maybe my life?)
The Nightmare Machine is an MIT project to use machine learning image-processing to make imagery for Hallowe’en.
The Stormtrooper Decanter is on back-order, but you can pre-order one from the next batch for £22 — it’s based on Andrew Ainsworth’s original movie helmet moulds from 1976, and will provide endless opportunities to point to lowball glasses and say things like “aren’t you a little short for a Stormtrooper drink?” (via Bonnie Burton)
Yahoo has released a machine-learning model called open_nsfw that is designed to distinguish not-safe-for-work images from worksafe ones. By tweaking the model and combining it with places-CNN, MIT’s scene-recognition model, Gabriel Goh created a bunch of machine-generated scenes that score high for both models — things that aren’t porn, but look porny.
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