AI

When I started software engineering, Waterfall was the typical process. A team would spend months building a large feature set, then deploy it. There were lots of reasons for it. Hardware acquisition looked nothing like it does today. Deployments took hours, not minutes. There were far fewer frameworks and almost no standardized automation. It was costly to do individual releases and thus batching was preferred. The batching had a time cost, though. User feedback was slower, patches took longer, and project management was more complex. ...

Barnes and Nobles and Borders were considered safe. Toys ‘R Us was untouchable. Large numbers of people believed that brick and mortar would remain. Others argued it wouldn’t. The end result, massive change. Many of those stores shuttered. Some still remain. Today, some argue that Artificial Intelligence isn’t intelligent. It is simply a token generation. It hallucinates. We’ll disregard the notion that LLMs are only one type of AI, for now. This semantic argument misses the point entirely and leaves us unprepared for what’s already happening. ...

Posted on June 22, 2024 The alarm sounds. You sleepily ask your AI assistant for the weather. Wearily grab breakfast while scrolling through social media. With a few taps, you reserve a self-driving car to pick up your perfectly tailored meal – one that matches your DNA-based dietary needs, as predicted by your medical history. Sounds perfect, right? In our rush to make life easier, we might be losing what makes us human. Our shiny gadgets promise a brighter future, but at what cost? ...

“AI created software is coming faster and faster. And the technology to do it is already here. All that is needed is training data and a focus.” Artificial Intelligence will create, test, and deliver software products with accuracy, efficiency, and security. Specialized AI agents will coordinate development across distinct lifecycle phases at inhuman speeds. How Will It Work? A software creator describes their product to an Application Generator (AG), which uses large language models to analyze requirements and interact with the creator to refine them. The AG works alongside a Domain Modeler to establish specifications using domain-specific language. ...

“Automation does not need to be our enemy. I think machines can make life easier for men, if men do not let the machines dominate them.” — John F Kennedy The Evolution of Developer Automation Software engineers have long embraced the philosophy of automating repetitive tasks. The industry has progressively introduced tools that reduce manual work: Early era: Manual debugging through console logging and frequent server restarts IDE advancement: Integrated Development Environments eliminated many routine tasks Modern infrastructure: Docker, Kubernetes, and Terraform automate deployment processes Framework libraries: Open-source solutions handle common functions like JSON parsing and database management These innovations increased developer productivity and lowered barriers to entry, yet professional demand remained stable with salaries exceeding six figures and 150,000+ open positions. ...

Note: This content was AI-generated as part of a test. In an era of mounting technological dependence and accelerating cyber threats, security professionals frequently encounter the excuse that vulnerabilities need not be addressed if they appear unreachable or difficult to exploit. This perspective is fundamentally flawed. The Problem with “Not Reachable, Not Exploitable” This reasoning creates a false sense of security and masks deeper vulnerabilities in an organization’s security posture. ...

Introduction In software development and security, reachability and exploitability often influence vulnerability management decisions. However, relying on these factors to delay prioritizing or reporting security findings is fundamentally flawed. Using such justifications amounts to “lying to those who want to be lied to.” The Flawed Test of Reachability and Exploitability Testing methodologies based on immediate reachability or exploitability are inherently flawed. The Log4Shell incident exemplifies how dynamic code execution creates false negatives. Modern software’s complex data flows obscure attack pathways, leading to underestimating vulnerability reach. Dismissing findings based on these tests merely obscures risk rather than eliminating it. ...