Secrets. Security best-practices mandate that they stay away from the code—or else! And that’s what we did for a long time. But as CI/CD practices evolved, for a myriad of reasons, we now want to ship the code, the environment, and the secrets, all in one lump. So we can’t hide the secrets anymore… unless? Tools like HashiCorp Vault attempt to address this by managing secrets outside the delivery chain. Great!

MoneyBank Inc. is a fintech enterprise that recently made the jump to K8s and GitOps to cope with the shift of demands from cranking out features towards stability and scalability. Yet, even with a fully automated CICD and shiny new microservices, features still take weeks to be released. As teams keep waiting on each other, frustrations, resentment, and mistrust grow. MoneyBank’s situation is typical for organizations with enterprise processes and startup mindsets.

Kubernetes has become the new darling of the infrastructure world. Not only is it a great piece of technology for managing your containers, but in conjunction with the possibility to rent managed clusters from any of the big Cloud providers, Kubernetes offers organisations the possibility to get rid of the shackles of hosting and/or managing one’s infrastructure. But that does not mean that Kubernetes is a one-size-fits-all solution. Especially in big corporations, bigger, systemic and/or more pressing problems could be revealed while implementing a big shift like moving to the cloud.

Do you remember the game 1024? It was huge in the early 2010s. A simple game with only one input (up, down, left or right) which made you hate binary. I never made it to 1024, so I thought ‘Why should I spend my valuable time trying to win this game by manually using my brain? Why not let a machine do it?’ I want to take you on my journey from idea through frustrations and pitfalls to my working prototype: Alpha2048

Even if you’ve never done anything with machine learning, you have probably already heard that it’s very powerful, adaptive and will change our way of thinking about computing forever. But how can you, a web developer, who’s never been interested much in statistics benefit from the ML hype? In this talk I want to give you the tools to build a small self-learning application that runs completely in the browser with deeplearn.

Have you ever been in a betting pool for your local football league? Then you might know, how exhausting it is, to come up with sensible results for every single game and most of us are not using the scientific method… at all. No need to fret about it anymore! I’ll show you how to write a program that learns to predict football scores with the help of the nodejs library