The principles that guided, and are guiding, the architectural decisions for the vectoricons.net rebuild include: scalability, testability, observability, extensibility, and flexibility. These concrete design constraints are applied at every layer—from AWS infrastructure to application code. Here's how the service layer is designed to meet those principles.

In this post, we explore a practical application of the k-nearest neighbors (KNN) algorithm to solve a real-world problem: predicting service availability based on geographic proximity. By leveraging existing customer data, we can provide quick estimates of service eligibility, enhancing user experience despite the limitations of a slow third-party API.

In the rush to get to market, it's easy to overlook small details that turn out to not be small. When we launched Vectopus.com, our multi-vendor vector image marketplace, we omitted WebP image support. To be honest, as the technical lead / architect - this was on me. I was so focused on getting the site up and running, I was not thinking about the full range of performance issues. I considered the performance of the API code, EC2 instance sizes and configuration, ALBs, CDN, etc., but did not include the full breadth of things that impact performance and SEO.

I am starting a series of blog posts on how I built a multi-vendor marketplace for stock SVG images on AWS Cloud services. In the series I will talk about the architecture, tech stack, do some deep dives into specific problems and solutions, and share some of what I have learned. A big part of the challenge has been insuring reliability, scalability, and peformance on a limited budget.

I mentioned in yesterday's post that a lot of data manipulation involves lists, nested and otherwise. Another common task is sorting elements in a list. Day 6 of 30 Days of Algorithms is a Merge Sort, which is another "divide-and-conquer" algorithm.