Systems | Development | Analytics | API | Testing

A product analyst helps teams understand how users interact with a product — and turns that data into decisions that improve growth, retention, and user experience. They sit at the intersection of: Instead of guessing what users want, product analysts rely on behavioral data to guide decisions.

Remember when Jarvis ran Tony Stark’s entire life and helped him build a high-tech Iron Man suit? That’s pretty much AI in eCommerce now, but instead of building suits, it’s building smarter stores and smoother shopping experiences. While most people are still stuck using AI to write basic product descriptions or churn out ad copy, the real power of artificial intelligence in commerce lies under the hood.

Government organizations are under increasing pressure to act with agility, collaborate across departments, and make fast, data-driven decisions. In the defense sector, these challenges are heightened by the need to unify complex functions in support of national security. Internal processes must be as effective and responsive as the forces they serve.

Every Monday morning, e-commerce analysts face the same frustrating ritual: export CSVs from Shopify, merge them in spreadsheets, clean the data, and pray nothing breaks before the weekly revenue meeting. This manual process wastes hours weekly per analyst while delivering insights that are already days old. Meanwhile, your competitors make real-time decisions based on live data flowing automatically into their analytics platforms.

Your executive dashboard shows yesterday's data while your competitors make decisions with information that's minutes old. This gap isn't just an inconvenience—it's a competitive disadvantage costing businesses millions in missed opportunities, delayed responses, and stale insights. Service Level Agreements (SLAs) for real-time dashboards solve this problem by establishing measurable commitments for data freshness, accuracy, and availability.

If you’ve been on the fence about trying the Ash Framework, this post is for you. We’re going to explore why Ash is a game changer for Elixir developers by building something complex, real, but surprisingly elegant. We’ll create AshTherapy, an AI-powered therapeutic chat service that handles conversations, messages, and user interactions — all powered by Ash. Before we go further, let’s first answer the question: what exactly is Ash?

Picture this scenario: Your organization is three days away from a critical compliance audit. The auditors have requested comprehensive documentation of your API testing processes, including security testing results, change management logs, and validation records. As you and your QA team scramble to compile reports from multiple tools and spreadsheets, a sinking realization sets in.

Scaling REST APIs for databases with heavy traffic requires careful planning to ensure fast, reliable performance. Here’s the gist: -Stateless Design: APIs should process requests independently, enabling horizontal scaling and efficient load balancing. -Caching: Use client-side, server-side, and CDN caching to reduce database strain and improve response times. -Optimized Queries: Fine-tune database queries, use indexes, and implement pagination to handle large datasets effectively.

Most companies talk about being “AI-first.” At Ably, we decided to actually become one. We build realtime infrastructure for AI applications. To do that credibly, we need to live and breathe AI ourselves – not just in our product, but in how we work every day. Two years ago, we began a company-wide push for AI adoption.

Let’s be real for a moment. Modern engineering teams run so many automated test cases every day across CI/CD pipelines. Still, without the right visibility, those results are just noisy logs sitting inside Jenkins or GitHub Actions. This is exactly why test reporting tools have become a critical part of every serious QA, DevOps, and automation workflow.