The great thing about the software tools today is, that most of them provide an API for developers. People can enhance, connect or create new products with the API. But how can you support developers and build a developer community or developer ecosystem? This Do’s and Dont’s list will help you to navigate through the unlimited possibilities of activities.
In this category I file blog posts about technology. If it is about the Internet of Things, agile development, a framework, a method, a pattern, or everything els technology relevant, I will post it here.
Every year, the innovation cycle gets shorter and shorter. Who know’s what technologies will emerge in 2018. But some big technology trends have recognizable growing attention. I predict that the following selected 6 technology trends will become sustainable in 2018.
Drones are still a cutting-edge technology, apart from multi-copters for filming purposes. Fully autonomous drones can be observed in closed areas, like logistic centers. Some prove of concepts already have been done in public for pizza delivery, postal service, or parcel delivery. In 2018, artificial intelligence and robotics will mature to commercial readiness.
Multi-Access Edge Computing (or Mobile Edge Computing; MEC) will come commercially available in carrier networks. Even before the 5G standard will be finished, carriers will implement cloud computing capabilities in their networks. The question is, do they have the customer in mind and deploy a common system or do they create isolated solutions? Anyway, application builders can improve their customer experience with low latency or compute offloading solutions.
Effective training is key to build a strong foundation for competition. That’s why I am using the Strava API with Python to analyze Strava training activities. I started my winter training recently and wanted to make sure I’m training effectively. Especially, I want to see if I stay in the right zones during the training. I have set up my Forerunner alarms appropriate, but I train outdoor, so it goes up and down during the run and I can’t control everything during the ride. Human. Not robot.
I started with the stravalib library, for reading the data from the API. As usual in data science, I need to transform and prepare the data for visualization. I end up storing the generated plots and build a quick & dirty HTML-file to display the stuff. I will now guide you through the example. I will explain the important steps. You can find the complete code on https://github.com/marquies/strava-viz.
What have I done:
- The first quarter was much about migration from OpenShift PaaS to IaaS operating platform. We migrated all existing staging and production environments, with all customer data. A simple lesson learned: doing something over and over again trains you and you can perform much better if it counts.
- In March Cloud of Things Starter Kit was launched.
- Offsite, Offsite, Offsite. The growing demand of leaving the office building to getting to know each other, re-organize work and getting things done looks like an anti-pattern of work culture.
- Late start of training for the upcoming MTB season.
- Started programming an iOS App to refresh my developer skills.
- Thought about some blog posts, but deleted them again. Some things should be untold.
- SMART defined goals are key.
- Visualize your work.
- Stop arguing about tools, start using one of them!
- If you have more meetings than working hours a day, something is wrong.
- Do you even have a target picture, bro?
- You don’t need status meetings
- IoT and the german telco players
- [GER] Cash Cow or White Elephant: IoT platforms of Deutsche Telekom, SAP, Bosch and Siemens
Slogan of the month: Stop starting and start finishing.
What have I done:
- Re-created the CoT project portfolio, now in a digital version (experimenting with Excel). It is good for sharing in our digital and distributed working environment, but a bit harder to update.
- I attended the Bonn Agile Meetup January: “IoT und Bastelprojekte” and talked about my AirQuality Lab project
- I attended a lot of Kick-Off-events (unit, department, CoT-team, Service Development team). Some were useful, some had good food.
- I worked on our first goals for 2017. First results in February 2017.
- I completed a course on my Coursera data science specialization course (Reproducible research)
- Watched the NFL playoffs
- Clemens Vasters definitions of elements of Internet of Things
- The current state of machine intelligence 3.0
- 7 Data Visualization Types You Should be Using More (and How to Start)
- IoT-Trends in 2017
- Data analytics projects: From theory to practice
- I said it more than once, starting IoT prototypes is quick and easy, but the rest is still hard
- A better way to visualize pipeline development? (WIP)
Want to learn about the Internet of Things and how to work with a sensor? I did too! I’ve been experimenting with the “Internet of Things” (IoT) since 2014 and have learned about the challenges with my sensor project: the AirQuality Lab. In the beginning, I just wanted to create something and work with the components. The scope of my side project was to read values from a sensor, transfer it to a thing-backend and then learning from the data. This post starts with the basic setup of the Thing and the following posts will cover further points.
After finishing a project in late 2014, I played around to learn a bit more about the Internet of Things (IoT) stuff for my next project. So, it was (and is) proposed, that “everything is connected in 2020” and I had to think about a product, product strategy and technical implementation in this area. Our thoughts about a domain model for IoT at this time was, to reduce everything to a source and a drain. Well, as hardware is cheap (but this is not all) I bought a Raspberry PI, a bunch of sensors and a small LCD display. Inspired by the CubeSensors, I wanted to measure air quality with the sensors and work with the derived data. This should help to understand how things process data, how to transfer it, how to analyze and derive information from it. I started the AirQuality Lab.
Slogan of the month: The intellectual capital of your business has two legs and walks home every day.
What have I done:
- Had nice workshops at Fraunhofer Institute for Big Data Analytics
- Successfully finished hard work on a internal data center migration with the Cloud of Things team
- Planted the seeds for the hosting quality improvements
- Worked on the performance of my blog. Improved site performance by 40%. PHP upgrade to v7 was the most gain. Performance could be better but for my kind of hosting it’s very good.
- Designed strategy, corner milestones and goals for 2017
- Prepared my talk for Bonn Agile Meetup January: IoT und Bastelprojekte (more on this later in this blog)
- Enjoyed Christmas and New Year’s Eve vacation
As a recap of 2016, I have created a list of the 2016 books I have bought and read. At least a serious bit. Maybe you are also interested in these books. Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers Mariette Awad, Rahul Khanna This book is more a reference of Machine Learning and I use it this way. The book contains much mathematical stuff to explain the algorithms. So if you like Agile IT Organization Design: For Digital Transformation and Continuous Delivery Sriram Narayan I choose this book as a resource for organizing my teams and make the organization more agile. While carefully selecting the methods to apply for me, I liked one the most: Alignment Maps. This concept helped my team to understand which business goals we have and why they have to do everything. A great way to show the team why their…
If you are like me preparing the new year between the holidays, maybe these resources help you like me.
Brian Tracy Goal Setting Guide
Brian Tracy has written down a good guide for goal setting. He hasn’t added anything which is not common sense, if you have already read the basics of this topic. You can get his article for 0$ here, you just need to subscribe to the newsletter. Of course you can use trash-mail.com or so, but he is an excellent source for information about personal development and productivity. I just learned he has an app for this process too, but I haven’t tried yet.
In the early days of the Internet of Things hype, the industry stated that computing power and accessories are cheap and ubiquitous. Read: available and ready to use. The good part of this statement is, that it is true. Partly. RaspberryPi, Arduino, Seed Studio Groove, Adafruit Feather and so on are cheap and versatile computing devices, available on many online retailers. Several toolkits and online resources help the hackers and makers to prototype and develop products. Also many online platforms are ready for the users to connect and manage the things.
Nonetheless is the Internet of Things not as ready as the industry states. You can prototype fast, but if you would like to adapt the Internet of Things for your business. If you compare the Gartner Hype Cycle for Emerging Technologies from 2015 and 2016 you may see, that the Internet of Things was dropped. While not sure if intentionally or not, it can’t be found on the plateau of productivity. I would place it in the trough of disillusion. For example, here are some topics, the industry is struggling with.