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WebAssembly, or Wasm, is increasingly relevant in software development. It's a portable binary code format designed for efficient and fast execution on any platform, including web browsers.
Watch the hands-on tutorial:
The much-anticipated State of DevOps 2023 report is finally out. Developed by the DORA team at Google, this annual report is a synthesis of trends and insights collected from professionals and organizations working in the realm of DevOps.
I've published a video covering all the key insights. Scroll down to see a summary.
There is no doubt that the cloud has changed the way we run our software. Startups, for instance, can get started without buying expensive hardware and scale flexibly. Also, the cloud has enabled novel solutions such as serverless, managed Kubernetes and Docker, or edge functions. For a time, cloud-native applications seemed to be the way forward for most teams, big or small.
But in exchange for all this power, we pay a cost. And it can be a steep one. 37signals — the company behind HEY.com and Basecamp.com — has calculated that by buying a few servers and moving from the cloud to on-premise, they can save 7 million dollars over 5 years.
There is no doubt that OpenAI's ChatGPT is exceptionally intelligent — it has passed the lawyer's bar test, it possesses knowledge akin to a doctor, and some tests have clocked its IQ at 155. However, it tends to fabricate information instead of conceding ignorance. This tendency, coupled with the fact that its knowledge ceases in 2021, poses challenges in building specialized products using the GPT API.
How can we surmount these obstacles? How can we impart new knowledge to a model like GPT-3? My goal is to address these questions by constructing a question-answering bot employing Python, the OpenAI API, and word embeddings.
Imagine creating an AI assistant to which you can say something like, "Book me the earliest reservation for the nearest Thai restaurant and update my calendar." Language models continue to push boundaries and evolve. OpenAI, the company behind ChatGPT, recently introduced a powerful new feature called function calling in their GPT models. Function calling simplifies the creation of chatbots that communicate with external tools and APIs, opening up a new realm of possibilities for AI-powered applications.
In this article, we will delve into the concept of function calling, its implications, and its transformative impact on how we interact with AI systems by creating NewsGPT, a chatbot that brings you breaking news worldwide.
Relational databases have a long, long history. The first appeared in the 1970s, and while the technology has certainly evolved, the relational model has proved the most popular over the last 5 decades.
Is there room for innovation after 50 years of history? The folks at Neon are showing us that one can, in fact, teach an old dog new tricks.
Web development, like most technologies, moves in waves and cycles. Static websites were all we had in the beginning. But, pretty soon, developers were hacking together the first server-generated sites thanks to Perl and PHP. This was the advance that would eventually kickstart frameworks like Laravel, Django, or Rails.
Mobile devices would come to change how people consume the web. So long server-generated websites, hello client-rendered applications. The next wave brought frameworks that could give users a more app-like experience—without reloads—like React or AngularJS.
Ruby has joined the ranks of languages capable of targeting WebAssembly with its latest 3.2 release. This seemingly minor update might be the biggest thing that has happened to the language since Rails, as it lets Ruby developers go beyond the back end. By porting their code to WebAssembly, they can run it anywhere: on the front end, on embedded devices, as serverless functions, in place of containers, or on the edge. WebAssembly has the potential to make Ruby a universal language.
WebAssembly (commonly shortened as Wasm) is a binary low-level instruction format that runs on a virtual machine. The language was designed as an alternative to JavaScript. Its aim is to run applications on any browser at near-native speeds. Wasm can be targeted from any high-level language like C, Go, Rust, and now also Ruby.
You never get a second chance to make a first impression. So how do you make sure it's a good one? This matter is constantly in the mind of release managers and product owners because they know that once a product is set loose into the world, all bets are off.
Although releasing a product or new feature may be tempting as soon as it is functional, users expect (and deserve) quality and polish. Therefore, before even reaching the beta test stage, much non-technical work must be done: documentation must be written, security must be assessed, and business objectives must be established. These are some things we have learned about software releases at Semaphore.
Page speed matters more than you think. According to research by Google, the probability of users staying on your site plummets as the loading speed slows down. A site that loads in ten seconds increases the bounce rate by a whopping 123%. In other words, speed equals revenue.
Lighthouse is a page speed benchmark tool created by Google. It runs a battery of tests against your website and produces a report with detailed advice to improve performance.
When we combine the cloud with IaC tools like Terraform and continuous deployment we get the almost magical ability to create resources on demand. For all its benefits, however, the cloud has also introduced a set of difficulties, one of which is estimating cloud costs accurately.
Cloud providers have complex cost structures that are constantly changing. AWS, for example, offers 536 types of EC2 Linux machines. Many of them have similar names and features. Take for example "m6g.2xlarge" and "m6gd.2xlarge" — the only difference is that the second comes with an SSD drive, which will add $60 dollars to the bill. Often, making a mistake in defining your infrastructure can cause your bill to balloon at the end of the month.
Kubernetes keeps growing. According to a recent survey, its adoption among developers increased by a staggering 67% in 2021. Enterprises are migrating to Kubernetes to enjoy the flexibility and scalability of cloud-native applications.
There’s no doubt of Kubernetes’ value to enterprises, but it comes at a cost: due to its complexity, it’s remarkably easy to leave a cluster vulnerable to attacks. Enterprises know this; in its state of Kubernetes security report, Red Hat stated that 59% of respondents considered container security a threat.
Congrats on nailing that interview! You’ve prepared hard and aced all the questions. You can relax now.
“Do you have any questions for us?” says the interviewer . Oh! It's not over, you realize. You’re expected to have some questions.
Productivity in software development has always been tricky to measure. Unlike in other industries, the act of programming is not something that’s easy to parallelize. The development process is unique in that it requires a diverse mix of technical and communications skills, which calls for a set of specialized metrics to keep track of the team’s vitals.
Not all metrics were created equal. Depending on the context, some are more useful than others. The things we choose to measure can help us find problems or obscure them behind irrelevant data and non-productive goals.
Microservices are a popular way to build small, autonomous teams that can work independently. Unfortunately, by their very nature, microservices only work in the backend. Even with the best microservice architecture, frontend development still requires a high degree of interdependence, and this introduces coupling and communication overhead that can slow down everyone.
Can we take microservice architecture patterns and apply them to the frontend? It turns out we can. Companies such as Netflix, Zalando, and Capital One have pushed the pattern to the front, laying the groundwork for microfrontends. This article will explore microfrontends, their benefits and disadvantages, and how they differ from traditional microservices.
Organizations have been attempting to measure software development productivity for decades. Too often, attention has concentrated on easily-quantifiable metrics like person-hours, lines of code committed, function points, or features added per sprint. Sadly, none of these have proven adequate at predicting team productivity. It’s such a complex problem that some have declared it impossible to solve.
Despite these failed attempts, DORA set out to establish measures of development productivity.
I remember my first day as a junior dev. It’s still fresh in my mind like it was yesterday. I was terribly nervous and had no idea what I was doing. My anxiety must have been evident because a kind soul decided to take me under their wing. That day I learned how to write SQL in my PHP code to do interesting things with the database.
Before I could start, though, I had to ask the database administrator (DBA) to create a few tables. I quickly realized that the DBA was the go-to person if you wanted to get anything done. Need a new column? Call the DBA. Does a stored procedure have to be edited? It was a job for the DBA. I looked up to him. He was such a superstar that I went on to be a DBA myself for a spell later in my career.
Software is constantly changing — the moment it is released, it begins to grow obsolete. Users need a constant stream of patches and want new features. At the same time, people hate when an update introduces a breaking change, especially when they were not warned about it.
Forever it has been common practice to use version numbers and codenames to track releases. Many projects have incrementing sequences (MS-DOS 6.2, 6.21, 6.22); others use part of the release date (Ubuntu 18.04, 20.04, 22.04). A few have a more whimsical disposition: TeX, for example, uses a numbering scheme that asymptotically approaches π (the current version is 3.141592653), while Metafont does the same with e. Its current version sits at 2.71828182.