I've been reading and admiring Nathan Ballingrud's short fiction since 1992, when we were both students at the Clarion workshop. Now, some of his very best work has been collected in a moving, sorrowful volume called
North American Lake Monsters, from the wonderful Small Beer Press.
Ballingrud's work isn't like any other. These stories are full of sadness and sorrow, but they're not merely sad. Like Tom Waits, Ballingrud is an expert at teasing out every delicious shade and nuance, every fine gradation of misery and pain. It's a heady and fantastic cocktail mixed from roughnecks and down-and-outers and flawed people who find in their ordinary and terrible world monsters, magic, and the strange. Ballingrud's fantastic elements are never seen full on, but always out of the corner of your eye, and it makes them all the more haunting.
This slim volume traces the fine veins of unhappiness in a way that no other writer of science fiction or fantasy I know of can match. If you've ever enjoyed a long cry, or come out of a deep funk to discover the joy of the contrast of the light and the sun, then you know why these stories are so powerful and moving.
If you'd like to get a taste of what I'm talking about, Tor.com has a excerpt from the collection, a story called "The Monsters of Heaven," about a missing child, broken angels, and a marriage in great ruin.
North American Lake Monsters: Stories
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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