Gender Wars

The technology industry often takes credit for the changing world of work. One example is the model of remote employees working as digital nomads in their favourite coffee shop, connected via Slack and collaborating via the cloud to create products and services for consumption over the internet or on smartphones and tablets. But what about work within the technology industry itself? We take a look at the profile of women in technology and compare it with the profile of their male counterparts.

Developer Economics survey Q4 2018 prize draw winners

Here are the winners of the Developer Economics survey Q4 2018 prize draw! Congratulations to all the lucky ones! Stay tuned for the new survey announcements and new prizes coming in Q2 2019.

Dev Evolution: Meet Vasil from AndroidPal

In our latest installment of Dev Evolution interviews, we talked with AndroidPal on how their tool creates value for developers and what technologies they are using to create their tool.

The battle: Tensorflow vs Pytorch

From the 3,000 developers involved in ML or DS we saw that 43% of them use PyTorch or Tensorflow. This 43% is not equally distributed between the two frameworks. Tensorflow is 3.4 times bigger than PyTorch. A total of 86% of ML developers and data scientists, said they are currently using Tensorflow, while only 11%, were using PyTorch.

Virtual reality: Where did it all go wrong?

I worked in the smartphone industry before it came of age. Our mission was “a smartphone in every pocket” at a time when simple feature phones like the Motorola RAZR were the must-have communications device. Within a few years of our early projects, the competitor, Apple, launched the iPhone. The rest is history. The App Store opened its doors, the stars aligned, the technology dream was realised and smartphones went on to rule the world.

Evolving technology and new channels help more game developers make money

The Fortnite phenomenon has captured the attention of the gaming community and exemplifies many of the changes that have occurred over the past 18 months in the industry. Gaming has become much more social, and watching expert gamers execute perfect moves can be just as much fun as playing them. This creates a new channel for developers to promote their games and new ways to generate revenues. Fortnite first gained popularity when rock star streamer Ninja and rapper Drake streamed their game-play on Twitch. This shift is influencing revenue models and opportunities for developers. The trend is also helping to shift development to the web.

The largest developer community: a critical view

When developers evaluate new technologies, one of the elements they often look at is the size and strength of the community surrounding that technology. “Can I get help and support from peers when needed?” It’s one of the reasons why open source technologies tend to be so popular. Conversely, technology vendors regularly signal their virtue with community numbers: “Our product is used by millions of developers, choose us!”

Live now - new Developer Economics Survey Q4 2018!

The Developer Economics survey Q4 2018 is live and better than ever in the new 16th edition! Calling out all software developers to take part, tell us what’s relevant in 2019 and beyond and what keeps you on your toes! We have new features, prizes, sci-fi theme, and other surprises ready for you!

Donations, social good and tech: a modern placement for developers

Social good has many forms and thankfully there are several resources in the tech arena to support people and projects that most of us have not thought to do. We approached our two non-profit media partners whose support we had during our previous Developer Economics survey (Q2 2018) to answer a few questions about their work and give us a peek at not-for-profit activities in tech, from their perspective. Introducing James Sugrue, co-Founder of donate:code and Aggelina Mila, coordinating Business Development at Social Hackers Academy.

Data scientists need to make sense of the big picture, rather than the big data

The web echoes with cries for help with learning data science. “How do I get started?”. “Which are the must-know algorithms?”. “Can someone point me to best resources for deep learning?”. In response, a bustling ecosystem has sprung to life around learning resources of all shapes and sizes. Are the skills to unlock the deepest secrets of deep learning what emerging data scientists truly need though? Our research has consistently shown that only a minority of data scientists are in need of highly performing predictive models, while most would benefit from learning how to decide whether to build an algorithm or not and how to make sense of it, rather than how to actually build one.