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In the world of enterprise software delivery, chaos isn’t a bug—it’s a feature. As a software engineering manager in a fast-paced, constantly shifting environment, I’ve lived through the fire drills, the last-minute pivots, and the invisible labor that keeps systems alive. That’s why The Phoenix Project: A Novel about IT, DevOps, and Helping Your Business Win by Gene Kim, Kevin Behr, and George Spafford felt less like a book and more like a documentary of my day-to-day. This isn’t just a story about DevOps—it’s a blueprint for survival when your team is small, your deadlines are tight, and your business priorities change faster than your sprint cycles. 📖 You can grab a copy here: Buy The Phoenix Project on Amazon What’s the Book About? The novel follows Bill Palmer, an IT manager unexpectedly promoted to save a failing initiative at Parts Unlimited. The company’s flagship project—“Phoenix”—is over budget, behind schedule, and threatening the entire business. Bill’s journey expo...

For subscription businesses — SaaS platforms, streaming services, membership programs — recurring revenue is the heartbeat of operations. Yet one persistent problem interrupts this rhythm: payment failures. Cards expire. Bank balances dip below the charge amount. Occasionally, a payment attempt is declined for reasons that are hard to pin down. Whatever the cause, failed payments create friction in the customer relationship and unpredictability in revenue. But predicting whether a failed payment will eventually recover isn’t straightforward. The probability of success doesn’t just depend on the most recent attempt — it depends on patterns. For example, someone who pays after a failure behaves differently from someone who fails twice in a row. That’s where Markov chains provide a structured way to model and forecast outcomes. What Exactly Is a Markov Chain? A Markov chain is a type of mathematical model used to describe systems that move between states over time, with each transit...

  Artificial Intelligence is everywhere. It’s writing our emails, diagnosing our illnesses, recommending our next binge-watch, and even negotiating car deals. The promise? Speed, precision, and superhuman efficiency. But before you hand over your trust to the machine, let’s take a closer look at what’s really under the hood—and what happens when it breaks. P.S  The image for this post was generete by AI. The Myth of Machine Wisdom AI doesn’t “think.” It doesn’t “understand.” It predicts patterns based on data. And when that data is flawed, biased, or incomplete, the results can be misleading—or even dangerous. We often mistake AI’s for intelligence. But behind the scenes, it’s a complex algorithms, trainer for data, and statistical guesswork. And when things go wrong, they go viral. AI Fails That Should Make You Pause These aren’t edge cases—they’re signals. AI is powerful, but it’s also unpredictable. Here are real-world examples that show why trusting AI blindly is a r...

Next time you’re out driving, take a moment to observe a UPS truck. Have you ever noticed that they mostly turn right? It’s not a coincidence. This small but clever habit is part of a carefully crafted strategy designed to optimize efficiency, reduce costs, and minimize environmental impact. UPS’s preference for right-hand turns is grounded in sophisticated operations research techniques, which aim to improve everything from fuel consumption to delivery speed. The Problem: Route Optimization for Efficiency UPS operates a massive fleet, making millions of deliveries each day. The challenge? Traditional shortest-path algorithms often don’t account for factors like traffic, stoplights, and fuel usage. Instead, UPS focuses on optimizing delivery routes to reduce idling, minimize congestion, and lower emissions. The Right-Turn Strategy UPS’s ORION (On-Road Integrated Optimization and Navigation) system prioritizes right turns for several reasons: Minimize Idling : Left turns often involve w...

In the ever-evolving world of artificial intelligence (AI), data serves as the foundation upon which machine learning models are built and refined. Data not only influences the accuracy and reliability of AI systems but also plays an important role in shaping their ethical standards, performance, and societal impact. Proper maintenance and evaluation of data is essential for ensuring the integrity, fairness, and effectiveness of AI systems. Here are the primary reasons why maintaining data for AI is so critical: 1. Ensuring Data Accuracy AI models depend on high-quality, accurate data to make informed decisions. Over time, data can become outdated or corrupted due to various factors such as changes in user behavior, environmental shifts, or even errors during data collection. If AI models are trained on inaccurate data, the resulting predictions or decisions may be unreliable, leading to poor outcomes and potential harm. Regularly maintaining and cleaning data ensures that AI systems ...

In today’s fast-paced and data-driven world, businesses, governments, and organizations rely heavily on structured methodologies to optimize their operations and make informed decisions. One such methodology is deterministic operations research (OR), a branch of applied mathematics that provides powerful tools to address complex problems in logistics, supply chains, task scheduling, workforce management, and resource allocation. What is Deterministic Operations Research? Deterministic operations research involves the study and application of mathematical models to optimize decision-making in systems where all parameters are known with certainty.  This predictability allows decision-makers to focus on finding the most efficient solutions to specific problems. The field encompasses a range of techniques, including linear programming, integer programming, network analysis, and dynamic programming. These methods enable practitioners to model problems mathematically and solve them using...