Systems | Development | Analytics | API | Testing

What if you could ask ChatGPT questions about your own company data and get instant answers? No SQL. No waiting for IT. No learning PowerBI. Just type a question like "What were our top 10 customers last quarter?" and get the answer in seconds. This isn't science fiction—it's something you can set up today. And here's the surprising part: this capability is actually more valuable for non-developers than developers.

At Koyeb, we are building high-performance serverless infrastrcture for AI. Run workloads on serverless GPUs, next-generation AI accelerators, and CPUs. Our platform runs fully isolated, secure microVMs on bare-metal servers around the world with autoscaling, scale-to-zero, and cold starts as low as 250ms. Just like everyone building in AI, 2025 was a busy year for us. We shipped a lot of features and improvements designed to make your AI deployments experience faster, smoother, and more cost effective.

When you’re building analytics for users, you quickly realize something: not every definition belongs on the Model. A lot of business logic sits in an awkward middle ground, too context-specific to hardcode into the Model but too important to leave scattered across one-off formulas. And in most tools, if the logic doesn’t live on the Model, every team ends up rebuilding the same thing over and over again. That’s where Query Sets and Column Sets in ThoughtSpot come in.

AI is fundamentally reshaping software quality, and the organizations leading this shift aren’t waiting to adapt. In October 2025, we brought together over 1,000 quality engineering leaders, practitioners, and innovators for Transform, our annual conference exploring what’s next in software delivery.

In September 2015, CareFusion issued emergency Class 1 recalls for its Alaris Syringe pumps. The pump was supposedly programmed to administer scheduled medical infusions to patients. As per official reports, due to a software code error (leading to a malfunction), the infusion pump could (or might already) have wrongly administered the scheduled medication, putting patient lives at risk. In response, the company issued recalls, regulators got involved, and the reputation damage was immediate.

‍ In an era where one-size-fits-all approaches in healthcare are increasingly falling short, personalisation stands out as a powerful transformation. Personalisation in healthcare holds the promise of delivering the proper treatment to the right patient at the right time. With rapid advancements in data collection, computing power and artificial intelligence (AI), what was once a futuristic vision is steadily becoming today's reality.

‍ Artificial Intelligence is unequivocally becoming the essential driver of business growth, a mandate clearly expressed by the market. But if you’re a U.S.-based enterprise leader, you know the reality feels a lot messier than the headlines suggest. Everyone is talking about AI transformation, but for many, it turns into a maze of complex data science, over-budget projects, and Proofs of Concept (PoCs) that never make it to production.

In the fast-paced world of API development, understanding the distinction between stateful and stateless applications can be a game-changer. Did you know that stateless REST APIs are the backbone of scalable web applications, handling millions of requests with ease? Yet, for many developers, the nuances of these architectures remain a mystery. As the demand for efficient and scalable systems grows, grasping the fundamentals of stateful vs. stateless applications becomes increasingly vital.

API endpoints process requests that power our digital world—from social feeds updating in real-time to enterprise systems syncing global inventories across continents. APIs comprise over 57% of Internet traffic, yet API endpoints often remain misunderstood. This leads to design inconsistencies, brittle integrations, and costly maintenance issues. Sound familiar? If you've merged microservices and untangled mismatched status codes, you know the pain.

While AI is revolutionizing the future of nearly every industry, it’s also created a unique set of challenges and liabilities that will need to be addressed as the area grows. Enter AI governance: a set of rules and best practices to ensure that AI is used effectively, securely, and responsibly. But what exactly does that mean, and why is it so crucial for businesses?