- How to Educate Users (Luke Wroblewski) — help new users in your app, not in a video.
- Hardware By The Numbers (Renee DiResta) — slides from her keynote at the Solid conference. The mean success rate across all sectors is 19.8%. On average, only 10% of hardware startups raise a second round.
- Humans Beating Computers (Wired) — Newman assembled a small team that became known as the “Air Divers”–the people who would dive deep into the individual complaints and surface with answers. Each was given a couple hundred support tickets connected to a specific issue that the data had identified as a hot-button topic. They would go off and read through each one, then come back and propose a fix. And in the end, this is what turned the situation around. Sometimes it’s easier to put people on the job than try to code the data analysis.
- Hadoop’s Uncomfortable Fit in HPC — Hadoop is being taken seriously only at a subset of supercomputing facilities in the US, and at a finer granularity, only by a subset of professionals within the HPC community.
Getting customers to use a service and then changing the rules isn't a decent way to treat people.
Let me start by saying that I’m not an Instagram user, and never have been. So I thought I could be somewhat dispassionate. But I’m finding that hard. The latest change to their terms of service is outrageous: their statement that, by signing up, you are allowing them to use your photographs without permission or compensation in any way they choose. This goes beyond some kind of privacy issue. What are they doing, turning the service into some kind of photographic agency with unpaid labor?
I’m also angered by the response that users should be willing to pay. Folks, Instagram doesn’t have a paid option. You can be as willing as you want to be, and you don’t have the opportunity. Saying that users should be willing to pay is both clueless and irrelevant. And even if users did pay, I don’t see any reason to assume that a hypothetical “Instagram Pro” would have terms of service significantly different.
It really doesn’t have to be this way. I’ve used Flickr for a number of years. I’m one of the few who thinks that Flickr is still pretty awesome, even if it isn’t as awesome as it was back in the day. And I’ve had a couple of offers from people who wanted to use my photographs in commercial publications. One I agreed to, one I refused. That’s how things should work. Read more…
Smart data scientists can make big problems small.
Having worked in academia, government and industry, I’ve had a unique opportunity to build products in each sector. Much of this product development has been around building data products. Just as methods for general product development have steadily improved, so have the ideas for developing data products. Thanks to large investments in the general area of data science, many major innovations (e.g., Hadoop, Voldemort, Cassandra, HBase, Pig, Hive, etc.) have made data products easier to build. Nonetheless, data products are unique in that they are often extremely difficult, and seemingly intractable for small teams with limited funds. Yet, they get solved every day.
How? Are the people who solve them superhuman data scientists who can come up with better ideas in five minutes than most people can in a lifetime? Are they magicians of applied math who can cobble together millions of lines of code for high-performance machine learning in a few hours? No. Many of them are incredibly smart, but meeting big problems head-on usually isn’t the winning approach. There’s a method to solving data problems that avoids the big, heavyweight solution, and instead, concentrates building something quickly and iterating. Smart data scientists don’t just solve big, hard problems; they also have an instinct for making big problems small.
We call this Data Jujitsu: the art of using multiple data elements in clever ways to solve iterative problems that, when combined, solve a data problem that might otherwise be intractable. It’s related to Wikipedia’s definition of the ancient martial art of jujitsu: “the art or technique of manipulating the opponent’s force against himself rather than confronting it with one’s own force.”
How do we apply this idea to data? What is a data problem’s “weight,” and how do we use that weight against itself? These are the questions that we’ll work through in the subsequent sections.
Agility, simplicity, and curiosity will define the next generation of apps and devices.
The speakers at the recent Webstock conference in New Zealand gravitated toward many of the same themes. Taken together, these themes create a framework for building the next generation of services, applications and devices.
Alistair Croll and Sean Power examine the impact of Facebook's embedded comments tool.
Facebook's new embedded comments option offers websites an additional social layer, but does it attract or drive away content engagement?