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How Cybersecurity Pros Can Use AI Skills to Reduce Alert Fatigue
For cybersecurity teams, security alerts continue to multiply. In 2024, cyber researchers published more than 40,000 CVEs warning of hardware and application bugs. In the first half of 2024, an average of 113 CVEs were published per day. By comparison, the first half of 2025 is running at a rate of 131 CVEs per day, according to Cisco Talos. This constant activity within security operations centers (SOCs) often leads to alert fatigue for cybersecurity and technology professionals who are charged with responding to these alerts, judging their danger to the organization, and working on patching vulnerabilities in hardware and software applications. After some time, these alerts affect security teams. A Splunk report of more than 2,000 security professionals and executives found that 59% of study participants reported having too many alerts, and 55 percent reported having to address too many false positives. At this year’s RSA Conference in San Francisco, numerous discussions involving in

Managing Complex AI Model Deployment Infrastructures
The artificial intelligence revolution has moved beyond experimental phases. AI now powers production-ready systems from recommendation engines to autonomous vehicles. However, moving from a trained AI model to a deployed solution presents unique challenges. These systems demand specialized knowledge and strategic planning. Managing complex AI model deployment infrastructures requires understanding intricate technical components. Teams must navigate security concerns and orchestrate resources that handle unpredictable workloads while maintaining optimal performance. Introduction to Managing Complex AI Model Deployment Infrastructures AI model deployment infrastructure represents the foundational technology stack that enables artificial intelligence models to operate reliably in production environments. Unlike traditional software deployment, AI infrastructure must handle dynamic computational requirements. These systems process massive data throughput and manage real-time inference dem

5 Tips for Getting Accurate Results When Coding with AI
Artificial Intelligence (AI) has woven its way into many of our tech stacks. In the past few years, an idea has become a foundational part of the day-to-day coding experience for many technologists. IDEs of all kinds are using AI features like autocomplete, and some platforms can create entire blocks of code without human interaction. For some, coding with AI is a stark reminder of the fragility this career path presents, and a threat to their professional existence. To others, AI is a means to an end, helping them be more productive and write better, cleaner code. AI may be concerning, but it’s nascent and requires human interaction and oversight once you get beyond the simplest code. AI is also not all-knowing. When coding with help from AI, you still have to ensure that what it returns is accurate and will compile effectively. If left to code haphazardly, AI will create more problems than it solves. To help those interested in using AI while coding, we spoke with a few experienced t

Simple Ways to Turn Finished Projects into Portfolio Gold
Turning completed projects into portfolio standouts is more than uploading screenshots or dropping a GitHub link. It’s a competitive IT jobs market out there, and portfolios must do more than show work—they must tell a clear, compelling story of challenges solved, technologies mastered, and business impact delivered. The strongest portfolios give hiring managers not just proof of skill, but a reason to believe you can replicate that success for them. That means thinking about your portfolio while you’re working—not after. “Far too often, folks will do the work, and when it’s time to put something in a portfolio, they aren’t sure what the story was along the way,” said Jason Schoch, global head of student experience and career service at General Assembly. He recommends keeping a journal of what’s happening as you’re going through the process of building out a product or project. “Keep track of the challenges in the moment,” he said. Those real-time notes capture the decisions, roadblock

Ladder vs. Lattice: Exploring Different Types of Career Mobility
Evidence suggests that upward mobility has slowed to a crawl as companies hire fewer managers and move towards flatter organizational structures. Plus, job hopping for career advancement has become increasingly challenging due to the slowing job market. “In today’s matrix organizational structure, a linear career track is more difficult to see,” noted Brett Coakley, founding manager and principal executive coach for Close Cohen career consulting. Instead of relying solely on promotions for career advancement, embracing a career lattice offers a more flexible and diverse approach to professional growth. A career lattice is a framework that promotes movement in multiple directions- lateral, diagonal, vertical or even downward to acquire skills and experience. When the moves are planned and strategic, they can accelerate career progression instead of waiting for promotions. Here’s a how to weave together various types of “moves” to achieve professional growth. Vertical Mobility Vertical c