Computers on the internet are uniquely identified by an IP address. For decades the world has used Internet Protocol version 4 (IPv4), which allows for about 4 billion unique addresses. As more of the world has come online, and we carry internet-capable devices in our pockets, we have run out of IPv4 addresses. Layers and layers of workarounds have been built to mitigate the problem. The current protocol—Internet Protocol version 6 (IPv6)—fixes various problems with IPv4; it has a significantly expanded address space that allows for the creation of many more unique IP addresses. Unfortunately, IPv6 has suffered from lack of adoption.
In our previous blog post on investing in the Desktop Client platform at Dropbox, we discussed the challenges of trying to innovate and iterate on a product while maintaining high platform quality and low overhead. In 2016, Dropbox quadrupled the cadence at which we shipped the Desktop Client, releasing a new a major version every 2 weeks rather than every 8 weeks by investing in foundational improvements. These efforts tended to illustrate one or both of the following themes:
- Reduce KTLO work: “Keeping The Lights On,” or KTLO, includes manual tasks such as setting configuration parameters,
In this post we will take you behind the scenes on how we built a state-of-the-art Optical Character Recognition (OCR) pipeline for our mobile document scanner. We used computer vision and deep learning advances such as bi-directional Long Short Term Memory (LSTMs), Connectionist Temporal Classification (CTC), convolutional neural nets (CNNs), and more. In addition, we will also dive deep into what it took to actually make our OCR pipeline production-ready at Dropbox scale.
Most representations of data contain a lot of redundancy, which provides an opportunity for greater communication efficiency by compressing the content. Compression is either built-in into the data format — like in the case of images, fonts, and videos — or provided by the transportation medium, e.g. the HTTP protocol has the
Content-Encoding header pair that allows clients and servers to agree on a preferred compression method. In practice though, most servers today only support
In our previous blog posts on Dropbox’s document scanner (Part 1, Part 2 and Part 3), we focused on the algorithms that powered the scanner and on the optimizations that made them speedy. However, speed is not the only thing that matters in a mobile environment: what about memory? Bounding both peak memory usage and memory spikes is important, since the operating system may terminate the app outright when under memory pressure. In this blog post, we will discuss some tweaks we made to lower the memory usage of our iOS document scanner.
Imagine you’re an engineer working on a new product feature that is going to have a high impact on the end user, like the Dropbox Badge. You want to get quick validation on the functionality and utility of the feature. Each individual change you make might be relatively simple, like a tweak to the CSS changing the size of a font, or more substantial, like enabling the Badge on a new file type. You could set up user studies, but these are relatively expensive and slow, and are a statistically small sample size. Ideally,