Scheduling background jobs on Android is a headache. Not only has Google introduced quite a few APIs over the years, but they’ve also changed their behavior. It’s difficult as a developer to pick the correct framework and to implement all necessary classes properly. In order to use all features from newer APIs and to support older devices at the same time, you need to write a lot of boilerplate code.
This article was written by Anirban Kundu, Anupom Syam, and Li Wang Evernote started with the aspiration of building a second brain for our users. The first step on this journey was enabling them to “remember everything” by capturing and accessing their ideas, thoughts, and memories at any time, anywhere. We’re now embarking on the next step of that journey by using Machine Learning (ML) to not only help people
If you follow this blog, you’re already well aware that the Evernote API is built on the Apache Thrift framework. Our client SDKs give you everything you need to use the API, so most developers don’t actually have to understand much about Thrift. From time to time, though, somebody wants use our Thrift IDLs to compile their own client-side code. Most of our engineers use Macs, and we’ve found that building the Thrift
When we started to plan the Evernote service in 2007, we knew that we would need to support both “thin” clients (like web browsers) and “thick” synchronizing clients on the day that we launched. This forced us us to think about remote protocols and client APIs before we built any web GUI, rather than waiting a few months to staple an API onto an existing web service. Our application forced