This article was originally written by Joff Redfern.

When we announced Stride in September 2017, we said, “It’s time we rethink the way we’re working. We believe that teams can stay connected and keep moving forward.” We still believe that. We knew we were taking a risk by entering an already competitive real-time team communications market, but we were willing to do the hard work necessary to build a great product. And we believe we were on that path. Stride was a bold project, and we’re very proud of the product we created and the team that created it.

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Co-working spaces are booming, with some 2.5m sq ft of co-working space in London alone at the moment. That’s an increase of 190% in the last year. Whilst the rise in the gig economy is an undeniable factor in this trend, there is also the promise of co-working providers that working in their facilities will make your work more creative and more effective. Great effort is put into designing spaces that are built to support innovation.

It’s a concept that has a lot to rationally support it. There is a strong need for flexible workspaces, and most facilities do look like superb places to plug into and work. Do they actually deliver on their promise though? Is the hype in the marketing material matched by reality on the ground? That was the question recently posed in a new paper by Creative Friction and the London School of Economics.

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These days, there is no part of our lives that is unaffected via computerization. A few illustrations incorporate clothes washers, microwaves, autopilot mode for autos and planes, Nestlé utilizing Robots to offer espresso units in stores in Japan, Walmart testing automatons to convey items in the US, our bank checks being arranged to utilize Optical Character Recognition (OCR), and ATMs.

Automation, in basic words, is innovation that arrangements with the utilization of machines and PCs to the generation of merchandise and enterprises. This aids in completing works with practically no human help.


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In the days before web applications, programmers wrote applications for a specific platform and used that platform’s native development environment and user interface controls. Before the application’s final release, a tester would check that the application was ready. Some testers wrote elaborate documents that described complicated scenarios they performed on the software manually. Other, more adventurous, testers used fancy tools that were the quality assurance version of Microsoft’s Word and Excel macro recorders. A tester would record a series of actions on the software under test. These steps were recorded in a high-level language in which the tester could edit the code and add test conditions that would verify that the actual results of the test matched the expected results. Each time a test was run, a report was generated that showed which conditions had been passed or failed.

With web applications, the time it takes to write and deploy code has been reduced to seconds. In this environment, much of the responsibility for testing is now in the hands of developers, who have since created many code-based test automation platforms.

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It all started when software development teams were physically present in the same office and interacting with each other face-to-face. At that time, this was thought to be the best way to get work done. Not many employees worked remotely. But, that era is long gone. By comparison, most of the organizations today have distributed teams. They ease the burden on project infrastructure and improve the employee’s comfort level.

A good infrastructure cannot have the dependency on only one system or person. Infrastructure needs to be distributed to avoid SPOF (Single Point of Failure). Similarly, it makes more sense to have distributed teams. This helps in a DR (Disaster Recovery) situation and also in hiring talented people without the time or location constraints.

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This title probably looks contrarian at a glance (so is my last post), but I truly believe we are largely misunderstanding what a natural language interface to our applications should look like. Here are my thoughts on the role of conversation in NLU/P systems.

What Is the Conversation?

Let’s define what we mean by conversation in the context of NLU/P systems. First off, conversation happens between two or more participants (computers talking to themselves at night is outside of the scope of this blog). Second, the conversation is a sequence of two or more sentences that are tightly coupled to each other by their context and time.


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As mentioned in the previous article, Vert.x is an event-driven, non-blocking toolkit. It was designed for asynchronous communications. In most cases, you need to work with multiple verticles in your applications. You also need communication between these verticles in these cases. In this article, I will attempt to explain how you can do this — let’s get started!

Use of Multiple Verticles

An important note to remember is that a verticle is a unit of deployment. It allows you to encapsulate your code for different needs. In addition, they can be run independently of each other. Verticles are communicated with each other by sending messages on the event bus. For this example, we have developed two verticles. One is the sender and the other is the receiver. You can access code for this example from this repository. The start method in the SenderVerticle class is as follows:

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Have you ever thought about how your mail inbox is so smart that it can filter Spams, label important emails or conversations, and segregate promotional, social, and primary messages? There is a complex algorithm in place for this kind of prediction and this algorithm comes under the wide umbrella of Machine Learning. The formula looks at the words in the subject line, the links included in the mail, and/or patterns in the recipient’s list. Now, this method is definitely helping the business of email providers and such predictive (as well as prescriptive) algorithms can help all kinds of businesses. But first, let’s define exactly what Machine Learning (ML) is.

What Is Machine Learning?

Simply put, ML is all about understanding, mostly hidden, data and statistics and then mining meaningful insights from this raw dataset. The analytical method that uses algorithms can help solve intricate data-rich business problems.


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The freedom and flexibility to work on your own terms add to employees’ happiness. Technology and the internet have made working from home possible for many employees. This option not only eliminates the need for travel, but also offers more flexibility.

Some companies still believe that employees are more productive in the office, a notion challenged by many companies today; some firms are going 100% remote with a distributed team. Even without a physical office, they achieve productivity and success.

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In this blog post, we will follow up the "Linux Namespaces and Go Don’t Mix" post, and we will show how the problem mentioned in the previous post was resolved in the recent release of Go 1.10.

Problem

To recap, the main problem was that the Go runtime did not allow us to safely change a local state of an OS thread ("M" in the Go notation) scheduling a goroutine, even if the thread had been locked with runtime.LockOSThread.

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