jonbruner

Scan time depends on material composition in the object you're scanning and your requirements for resolution. You can scan a dense steel object overnight to capture micron-level detail, or you can scan a plastic object in a few seconds to search for a known issue like a crack.

Battery scans are very fast; the scans in the report took less than a second. Total cycle time on a Triton CT scanner is under 5 seconds when you account for part handling.

Ha! I'm with Lumafield--we've actually done the same thing, scanning bags of Doritos, Cheetos, and Ruffles: https://www.lumafield.com/article/bite-into-doritos-ruffles-...

To answer the question about whether these are cleaned up, these scans aren't processed beyond what our software does automatically during the reconstruction. Industrial CT scanners are designed to scan a wider range of material densities than medical scanners. We use some copper filtration to scan parts with lots of dense materials, but no extra processing is required once we've reconstructed the model.

Also, the difference between a full cartridge and an empty cartridge is minimal; about 20% of the toner reservoir is filled in a new cartridge, dropping to 15% when the printer says the cartridge is empty.

Thanks! Hope it's helpful.

This is on /r/machinelearning, but I'll post it on /r/learnmachinelearning as well.

https://www.reddit.com/r/MachineLearning/comments/6fxbcr/int...

Code from the tutorial, including the notebook itself, is available on GitHub here: https://github.com/jonbruner/generative-adversarial-networks

In 2013 I wrote an article called "Tweets Loud and Quiet" (https://www.oreilly.com/ideas/tweets-loud-and-quiet) based on a surprising realization: the median Twitter account has one follower, and the median active account has 61 followers.

In order to write the article, I compiled metadata from a random sample of roughly 400,000 Twitter accounts. The entire sample is now available in this GitHub repo.

Although it's almost four years old now, I get requests for access to the dataset on an almost weekly basis. It's no longer a contemporary snapshot of Twitter usage patterns, but it supports interesting research on social dynamics.

There's a stripped-down, e-ink word processor similar to what you're describing called Freewrite (https://getfreewrite.com/). It launched on Kickstarter as Hemingwrite a couple of years ago. It's got an e-ink display and syncs documents over WiFi.

I think the real selling point is a distraction-free venue for writing, but the e-ink display could be a plus as well.