Systems | Development | Analytics | API | Testing

In 2026, as AI models increasingly generate, refactor, and deploy code on their own, developers face a new challenge: how to safely run code they didn’t write. Sandboxes have become the backbone of this new workflow because they are lightweight, secure environments that let teams test, validate, and monitor code without risking production systems.

Agentic AI enables you to do what you simply couldn’t before, like making sense of and responding to everything ever said about your product.

The SDLC, or software development lifecycle, is undergoing a radical change. Engineering teams have been using conventional, frequently reactive procedures for decades. We construct, test, correct, and implement. However, in today's fiercely competitive digital world, this traditional strategy is insufficient. It can't keep up with the complexity of contemporary applications and is too sluggish and prone to human mistakes.

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.

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 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.

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?

The performance of AI applications depends on its underlying infrastructure. Whether its fine-tuning custom models, performing real-time inference, deploying AI agents, AI workloads require high-performance hardware like Nvidia GPUs or next-gen AI accelerators from Tenstorrent. On top of performance, efficiently running AI workloads in production and at scale is a challenge.