Tech layoffs are tough enough without companies adding insult to injury by framing them as a purge of 'low performers.' This harmful narrative unfairly stigmatizes employees, making their job search harder while protecting executives from accountability. Worse yet, companies that take this approach risk damaging their reputation and future hiring prospects.

AI is transforming coding by redefining what it means to code. Writing prompts for LLMs is more about structured shorthand than perfect sentences, resembling programming itself. Like higher-level languages, LLMs shift the focus from syntax to architecture and validation. The challenge is not just generating code, but ensuring it integrates into a well-designed, high-quality system.

Tech interviews are broken, and it's time for a change. Expecting candidates to solve complex coding problems from memory without access to modern tools like Google and ChatGPT doesn't reflect how engineers actually work. In the real world, problem-solving, critical thinking, and leveraging available resources are key to success. Read on to discover why allowing candidates to use these tools can lead to better hires and a more effective interview process.

AI can be more than just a tool for efficiency—it’s a game-changer for creativity and innovation. Instead of fearing job loss or focusing on cost-cutting, developers and businesses can use AI to amplify their capabilities and tackle challenges in new ways.

"DevOps" is often misunderstood as a job role. This misconception can lead to stress, burnout, and suboptimal outcomes for both individuals and businesses. By treating DevOps as a set of practices, organizations can foster better collaboration, reduce stress, and achieve more sustainable results.

Who will win the AI revolution? History shows the real value lies not in hardware but in software innovation. Discover why user experience and product thinking will define the future—and which companies might lead the charge.

Explore the intricate journey of building your own cloud with CNCF tools. Discover the balance between control and complexity, and decide if the cloud-native path is the right choice for your organization.

Cloud computing has been a game-changer for over a decade, offering cost savings, scalability, and freedom from hardware headaches. So why are some folks still surprised when a high-profile organization moves to the cloud?

How might bad actors exploit AI coding assistants to induce vulnerabilities in your organization's code?

Discover why prioritizing test design over automation tools is the key to delivering high-quality software.

If the tech interview is failing us, what should we do instead?

Your code being freely available on the Internet is probably helping advance systems that may actively threaten your livelihood.

How can we continue to get most of the benefits from daily standup for a significantly lower cost in terms of engineering team productivity?

AI means you can’t “exit” the cloud now even if you want to.

If meetings impact developer productivity as much as the developers themselves are telling us, then it only stands to reason that one of the best ways to improve productivity would be to start reducing the number of meetings — and the Agile cadence is a prime target.

You thought you got into software engineering to avoid this stuff.

Tech interviews aren’t broken, they are merely obsolete, 30 year or so past their prime.

How do you take a classically trained and experienced IT organization and “lift and shift” the engineers to the cloud?

Considering all the factors when making the build vs. buy decision is critical in chicken sandwiches and enterprise software.

Tearing down code siloes is about more than just creating an efficient development process; it extends its impact to cultivate a culture of continuous learning and collaboration.