Project Naptha — automatically applies state-of-the-art computer vision algorithms on every image you see while browsing the web. The result is a seamless and intuitive experience, where you can highlight as well as copy and paste and even edit and translate the text formerly trapped within an image. Chrome extension. (via Anil Dash)
Garbage Trucks and FedEx Vans (IEEE) — Foo alum, Ian Wright, found traction for his electric car biz by selling powertrains for garbage trucks and Fedex vans. Trucks have 20-30y lifetime, but powertrains are replaced several times; the trucks for fleets are custom; and “The average garbage truck in the U.S. spends $55,000 a year on fuel, and up to $30,000 a year on maintenance, mostly brake replacements.”
Microsoft’s Quantum Mechanics (MIT TR) — the race for the “topological qubit”, involving newly-discovered fundamental particles and large technology companies racing to be the first to make something that works.
Floodwatch — a Chrome extension that tracks the ads you see as you browse the internet. It offers tools to help you understand both the volume and the types of ads you’re being served during the course of normal browsing, with the goal of increasing awareness of how advertisers track your browsing behavior, build their version of your online identity, and target their ads to you as an individual.
slfsrv — create simple, cross-platform GUI applications, or wrap GUIs around command-line applications, using HTML/JS/CSS and your own browser.
Review Ninja — a lightweight code review tool that works with GitHub, providing a more structured way to use pull requests for code review. ReviewNinja dispenses with elaborate voting systems, and supports hassle-free committing and merging for acceptable changes.
Liquibase — source control for your database. Apache 2.0 licensed.
A Few Useful Things to Know About Machine Learning (PDF) — This article summarizes twelve key lessons that machine learning researchers and practitioners have learned. These include pitfalls to avoid, important issues to focus on, and answers to common questions. My fave: First-timers are often surprised by how little time in a machine learning project is spent actually doing machine learning. But it makes sense if you consider how time-consuming it is to gather data, integrate it, clean it and pre-process it, and how much trial and error can go into feature design.
The Poisoned NUL Byte, 2014 Edition (Project Zero) — from Google’s public security efforts, this detailed public description of how an exploit was constructed from a found vulnerability. They’re helping. Kudos!
Myths About the Coming Robot Economy (Eric Sofge) — the entire discussion of the so-called robot economy, with its predictions of vast, permanent employment rates and glacial productivity gains, is nothing more than a wild guess. A strong pushback on the Pew Report (PDF): Frey and Osborne’s analysis is full of logical leaps, and far-reaching conclusions drawn from cursory observations about robots that have yet to replace humans.
Content for Sensitive Situations (Luke Wroblewski) — People have all kinds of feelings when interacting with your content. When someone’s needs are being met they may feel very different then when their needs are not being met. How can you meet people’s needs?
Urban Villages (Senseable City at MIT) — People who live in a larger town make more calls and call a larger number of different people. The scaling of this relation is ‘superlinear,’ meaning that on average, if the size of a town doubles, the sum of phone contacts in the city will more than double – in a mathematically predictable way. Surprisingly, however, group clustering (the odds that your friends mutually know one another) does not change with city size. It seems that even in large cities we tend to build tightly knit communities, or ‘villages,’ around ourselves. There is an important difference, though: if in a real village our connections might simply be defined by proximity, in a large city we can elect a community based on any number of factors, from affinity to interest to sexual preference. (via Flowing Data)
Grasping with Robots: Which Object is in Reach? (Robohub) — This post is part of our ongoing efforts to make the latest papers in robotics accessible to a general audience. … a new approach to build a comprehensive representation of the capabilities of a robot related to reaching and grasping. Very short, very readable, as promised.
Failing at Microservices — deconstructed a failed stab at microservices. Category three engineers also presented a significant problem to our implementation. In many cases, these engineers implemented services incorrectly; in one example, an engineer had literally wrapped and hosted one microservice within another because he didn’t understand how the services were supposed to communicate if they were in separate processes (or on separate machines). These engineers also had a tough time understanding how services should be tested, deployed, and monitored because they were so used to the traditional “throw the service over the fence”to an admin approach to deployment. This basically lead to huge amounts of churn and loss of productivity.