A Computer Science student took a minimum wage job sweeping floors at an Albert Heijn supermarket. Instead of simply doing the work, he decided to optimize his sweeping path using simulated annealing, a complex algorithmic approach. He modeled the supermarket floor as a grid graph and built a C++ program to find the most efficient sweeping path. Initially, the result was a path riddled with sharp turns, making it impractical for a human to follow. This highlighted a critical flaw: optimizing for distance alone doesn’t account for real-world usability. Realizing the limitations of his approach, he adjusted his cost function to include a “turn penalty.” This shift led to smoother, more walkable paths, even if they were slightly longer. By tweaking the penalty, he could balance efficiency and usability, illustrating a broader lesson about optimization. The author connects his experience to various algorithms in everyday life, such as those used by social media platforms and recommendation systems. These systems often optimize for engagement or click-through rates, neglecting user well-being or truthfulness. The key takeaway emphasizes that technical accuracy is meaningless if the problem being solved is misguided. Many systems, whether in tech or business, prioritize metrics that don’t account for the bigger picture—like ethics or environmental impact. The author’s journey illustrates the need to question what we’re optimizing for, as the right metrics can lead to better outcomes, both in algorithms and in life.