Development

In this video we go through how to set up our device detection solution on a Linux machine using NGINX, add properties to call from the data file and finally check that the results are being returned as response headers on the site.

This essential blog compares the approach used by 51Degrees and DeviceAtlas. It describes the high-level operation of each product, areas of difference and associated intellectual property. In simple terms, it will help readers match products to common use cases and formulate tests for evaluation.

51Degrees is regularly evaluated against competitors DeviceAtlas and WURFL from ScientiaMobile for performance, accuracy and memory consumption. This blog post builds on the previous performance blog from 2016 with the same commodity hardware; 1) a Raspberry Pi, 2) a low-end desktop PC and 3) a high-end multi CPU server, but using an all new algorithm.

In 2015 we released a detailed blog on Device Detection for Apple iPhone and iPad. Since then Apple has released many more devices. Identifying Apple devices can be challenging and when putting plans in place to optimise websites for mobile devices like Apple, companies have two choices. Try and run a solution internally or simply have 51Degrees do the work for you with the added benefits of the Apple device properties we provide in our data files.

Because we endeavour to support companies and hobbyists with our unique open source solutions, we are sharing some JavaScript that will enable you to set up an internal solution if you wish. We would, however, encourage you to save yourself the time and effort and use our data files to support your strategic website optimisation decisions.

This article shows how the new, faster, Hash Trie algorithm can be used in a Java environment. This blog will go through the steps required to create an illustrative example of how to call the C Hash Trie API within Java. Before starting, you will need to get hold of the new Hash Trie source which is available on GitHub.

51Degrees will be switching to using Git LFS to store our data files.

Using the 51Degrees Java API, we show you how you can use Native Google App data and enrich it with 51Degrees Device Intellegence.

Device detection databases only provide useful insight if they contain up-to-date combinations of hardware models, operating systems and web browsers. “Device Combinations” is the principle metric used to measure accuracy and breadth. 51Degrees contains over 600,000 currently active and unique permutations of device, OS and browser indexed for web, native apps and telecoms operators

51Degrees is regularly evaluated against competitors DeviceAtlas and WURFL from ScientiaMobile for performance, accuracy and memory consumption. This blog post builds on a 2015 performance blog with three new commodity hardware benchmarks on; 1) a Raspberry Pi, 2) a low-end desktop PC and 3) a high-end multi CPU server to provide information engineers will find helpful when selecting a device intelligence solution.

Large bag of crisps at my side and headset on (‘HMD’ if you must), I nestle into the comfiest armchair in the house.  The soft padding of the headgear feels secure and reassuringly weighty as it encloses my skull, and with all peripheral vision also blocked off, I feel I have just announced “Do Not Disturb” to the rest of the household.