Happy New Year! As we enter the new decade, I decided it was time to write my now annual blog post giving my thoughts on what this year might hold for Java. I’ll also look back on my predictions in the last post to see how accurate (or not) I was.
Obviously, the most significant thing this year will be the celebration of a whole quarter of a century since Java was launched. I plan to write a lengthy missive about the last 25 years of Java closer to the time.
With more and more organizations focusing on DevOps, it’s not surprising to see the number of toolchains — and the complexity of those toolchains — multiply. Afterall, automated testing for function, security, and deliverability is at the core of improving a team’s DevOps.
But, at what point are more tools and toolchains creating more distracting work for your team than they save? Is the complexity of these automated tools actually leading to a better development lifecycle, product, or service? Of course, there’s no simple answer to what the right number of toolchains or tools is, given the complicated and diverse circumstances different teams of developers face.
I work as a DevOps engineer for a large public-facing application, which has around 90+ microservices (Java-based). Consistently, we are hit by scenarios that we’d discover in the field, which were not caught in any of our testing. Despite improving our test strategies and code coverage assessments, we were unable to assess the "strength" of our test cases.
We looked around for options and solutions that could help us to be more sure about our test cases and the code we develop. As a DevOps engineer, my responsibility was to identify options and trial them to fix this problem and include it as part of our automated build process.
If you think about the World Wide Web, it’s easy to imagine it as a single software system. Once you do, you realize it’s the largest software system the world has ever created — probably by hundreds of orders of magnitude. It contains trillions of lines of code, hundreds of millions of servers, and billions of clients, running thousands of different programming languages. Still, it works more or less as we expect it to work. So, what made it possible for humans to create such an enormous software system? The answer is simple: HTTP!
The HTTP protocol allows us to create perfect encapsulation. The client and the server don’t need to know anything about each other, besides the URL, HTTP verb, and what parameters to pass in and expect as output. This allows billions of clients to interact with each other, without knowing (almost) anything about each other. If you reduce it down to its basic components, it becomes painfully obvious that the following is a recipe for winning.
In this article, I am going to explain how we integrate some deep learning models, in order to make an outfit recommendation system. We want to build an outfit recommendation system. We used four deep learning models to get some important characteristics of the clothing used by the user.
The recommendation systems can be classified into 4 groups:
Developers love to show off a solution they’ve come up with to solve a tricky problem (heck, I’m doing it right now). For that reason, you’ll probably create a developer blog at some point in your career to showcase your favorite hacks.
And, as a developer, you’ll no doubt irrationally build your blog from scratch rather than use a pre-made solution, because that’s just what we do!
During discussions with many of my colleagues, I realized the need for (a) clear demarcation between Application Portfolio Rationalization (APR) and R-Lane, (b) the association of R-Lane with APR, (c) right positioning of APR and R-Lane and (d) role of modernization in APR and R-Lane.
While all these topics spinning around rationalization, they differ only in when and where rationalization is taking place. Let us consider the following three scenarios to better understand these trending terminologies.
One of the most crucial features of Blockchain Technology is its decentralized nature. This means that the information is shared by all the parties of the networks. Hence, it eliminates the need for middlemen or intermediaries to facilitate operations. This feature is particularly useful because it saves one from the possibilities of hacks and fraudulent activities. Blockchain Technology offers a feature-rich, fast, cheap, and efficient mode of transactions. Thus, most organizations in the government and banking sectors have started adopting this top-notch technology.
This post deals with the importance and the need for Smart Contracts to create Blockchain applications in the market.
It’s a famous fact that bitcoin mining hardware has changed by leaps and bounds lately due to the growth of new central processing units in the marketplace. The new machines may conduct Bitcoin processing at a faster rate when compared with the computers of yesteryear.
Furthermore, they consume less power. Field programming team array processors are connected with CPUs to boost their computing power. While selecting hardware for Bitcoin processing, ensure it includes a large hash rate that would deliver spectacular results to your users. According to experts, the rate of data processing is measured in mega hash rates each second, or GIGA hash speeds per second.
The climate is a hot issue, and weather forecasting technology is suddenly cool. The urgent need for innovative weather and environmental conditions forecasting solutions is obvious whether you believe in climate change or not.
In fact, the climate is not only the topic of discussion around the water cooler. There are businesses, entire industries in fact, where weather conditions have a direct impact on day-to-day business operations. Industries like logistics, aviation, competitive outdoor sports, public safety, mining, and agriculture (to name just a few) are directly impacted by the weather. For these industries, weather forecasting accuracy can mean the difference between ending a fiscal year in profit or loss.
