Moore’s Law in Action: You’ll probably remember the prediction back from your days in University. In essence, Mr. Moore, founder of Fairchild Semi and CEO of Intel, predicted the density of transistors in modern integrated systems will double about every 18 months. He was right for a long time, while many predicted the end of his law. Visual Capitalist today linked a illustration showing the law in Action up to 2019.
Moore’s Law
Can the predictions from Moore’s Law keep up with technological innovation spanning almost 50 years? Watch this stunning animation to find out.
Today Kubernetes released it’s version 1.17. The software is one of the most popular open source projects ever. It allows managing containerised applications and micro-services. The release arrives at the end of a regular development cycle.
After the project was announced in 2014 by two Google employees, it hit a first 1.0 milestone on July 2015. The project gained massive popularity in the cloud world because it enables scalable infrastructures and service. With the Kubernetes 1.0 release, Google partnered with the Linux Foundation to form the Cloud Native Computing Foundation (CNCF) as a new home for the technology.
Since Kubernetes became publicly available, it gained popularity quickly and today is commonly used as the main way to host microservice-based implementations, mostly because Kubernetes and its associated ecosystem provide a rich choice of tools with all the capabilities that are needed to address key concerns of any modern software architectures.
With Kubernetes 1.17 released today, the package comes with more details on the release in the Release Schedule or in particular on the Changelog.
Python, the programming language, gained lot’s of popularity only in the past decade. In particular for big data applications, machine learning and data science the language is almost without alternative. But also for tool development or web applications backends, Python has huge adoption. Reasons are it’s huge ecosystem and a friendly, constructive community. Despite it’s newer competitors it has been around for 30 years. One of the most appreciated benefits is the steep learning curve, that allows virtually everyone to understand Python code.
Dropbox has an interview with Guido van Rossum, who published the first version of the language in 1989. The conversation revolves around the purpose of code and how python helps improve cooperation and productivity.
Guido van Rossum
“You primarily write your code to communicate with other coders, and, to a lesser extent, to impose your will on the computer.”
Technical University Munich Institute for Ethics in Artificial Intelligence launches a speaker series to bring experts from all over the world to Munich and talk about Ethics and Governance for Artificial Intelligence. The Series kicks off with an Inaugural Session with Lionel P. Robert on December 13 – 10:00 am – 11:30 am.
TUM Institute for Ethics in Artificial Intelligence
With its new Speaker Series, the TUM Institute for Ethics in Artificial Intelligence is bringing experts from all over the world to Munich to talk about Ethics and Governance of […]
Part of the compelling nature of SaaS Products is the possibility to understand the user and improve on the go. Any Product Manager will literally have to understand what are the use-cases for customers and how to focus on the important areas. Just recently our team led the debate which metrics would be the right ones to focus on.
Nancy Wang, Head of Product Management at Amazon Web Services, highlights six product metrics enterprise SaaS companies should track.
In this Article, Nancy Wang, head of Product Management at the most successful cloud service providers, shares her insights on important metrics to keep an eye on. The possibility to understand often goes overboard and requires focus.
The case under discussion in the article revolves around paid products. Derived metrics are a foundation that serves as a blueprint to other products in the SaaS space. Goals differ, but ultimately, to make a product successful, it requires an understanding of how successful customers were, using the product. Following the established funnel pattern, users are being segmented into funnel. Along that funnel, the metrics acquired need to reflect the stage of the journey the user is on.
At the top of the funnel, most often the interaction is anonymous and requires profiling to understand the audience coming in. Further down in the funnel, metrics capture engagement and transaction. Towards the end of the funnel, the metric needs to relate to retention.
