- How to C in 2016 — straightforward recommendations for writing C if you have to.
- Using Deep Learning to Colorize Old Photos — comes with a trained TensorFlow model to play with.
- Open Source Firmware for Toy Drones — The Eachine H8 is a typical-looking mini-quadcopter of the kind that sell for under $20.[…] takes you through a step-by-step guide to re-flashing the device with a custom firmware to enable acrobatics, or simply to tweak the throttle-to-engine-speed mapping for the quad. (via DIY Drones)
- Mobile Web vs. Native Apps or Why You Want Both (Luke Wroblewski) — The Web is for audience reach and native apps are for rich experiences. Both are strategic. Both are valuable. So when it comes to mobile, it’s not Web vs. Native. It’s both. The graphs are impressive.
"open source" entries
The O'Reilly Radar Podcast: A special holiday cross-over of the O'Reilly Data Show Podcast.
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In this special holiday episode of the Radar Podcast, we’re featuring a cross-over of the O’Reilly Data Show Podcast, which you can find on iTunes, Stitcher, TuneIn, or SoundCloud. O’Reilly’s Ben Lorica hosts that podcast, and in this episode, he chats with Apache Spark release manager and Databricks co-founder Patrick Wendell about the roadmap of Spark and where it’s headed, and interesting applications he’s seeing in the growing Spark ecosystem.
Here are some highlights from their chat:
We were really trying to solve research problems, so we were trying to work with the early users of Spark, getting feedback on what issues it had and what types of problems they were trying to solve with Spark, and then use that to influence the roadmap. It was definitely a more informal process, but from the very beginning, we were expressly user driven in the way we thought about building Spark, which is quite different than a lot of other open source projects. … From the beginning, we were focused on empowering other people and building platforms for other developers.
One of the early users was Conviva, a company that does analytics for real-time video distribution. They were a very early user of Spark, they continue to use it today, and a lot of their feedback was incorporated into our roadmap, especially around the types of APIs they wanted to have that would make data processing really simple for them, and of course, performance was a big issue for them very early on because in the business of optimizing real-time video streams, you want to be able to react really quickly when conditions change. … Early on, things like latency and performance were pretty important.