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Python has its pros and cons, but it's nonetheless used extensively. For example, Python is frequently used in data crunching tasks even when there are more appropriate languages to choose from. Why? Well, Python is relatively easy to learn. Someone with a science background can pick up Python much more quickly than, say, C. However, Python's inherent approachability also creates a couple of problems. Whenever Python is updated, it means a big refactoring workload, which often gets dealt with poorly – or not at all. That leads to poor performance and security vulnerabilities. But maybe there is a better way: a tool to keep your Python tasks running smoothly and securely day in, day out. Let's take a look. It's slow, but it does the job Python isn't the fastest language around, but despite its comparative disadvantages, you'll often see it used for intensive data crunching operations. Think machine learning, computer vision, or even pure math in high-perform

Progress powers technology forward. But progress also has a cost: by adding new capabilities and features, the developer community is constantly adjusting the building blocks. That includes the fundamental languages used to code technology solutions. When the building blocks change, the code behind the technology solution must change too. It's a challenging and time-consuming exercise that drains resources. But what if there's an alternative? The problem: reading code someone else wrote Let's take a step back and take a look at one of the fundamental challenges in development: editing someone else's code. Editing code you just wrote, or wrote a couple of weeks ago, is just fine. But editing your own code written years ago – never mind someone else's code - that's a different story. In-house code style rules can help but there are always odd naming conventions for variables and functions, or unusual choices for algorithms. Arguably, a programmer's abilit

Imagine a world where the software that powers your favorite apps, secures your online transactions, and keeps your digital life could be outsmarted and taken over by a cleverly disguised piece of code. This isn't a plot from the latest cyber-thriller; it's actually been a reality for years now. How this will change – in a positive or negative direction – as artificial intelligence (AI) takes on a larger role in software development is one of the big uncertainties related to this brave new world. In an era where AI promises to revolutionize how we live and work, the conversation about its security implications cannot be sidelined. As we increasingly rely on AI for tasks ranging from mundane to mission-critical, the question is no longer just, "Can AI  boost cybersecurity ?" (sure!), but also "Can AI  be hacked? " (yes!), "Can one use AI  to hack? " (of course!), and "Will AI  produce secure software ?" (well…). This thought leadership article is about the latter. Cydrill  (a