Systems | Development | Analytics | API | Testing

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.

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.

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.

Many AI analytics platforms force enterprises into an impossible choice: adopt cloud-only solutions that compromise data governance and security policies or forgo AI capabilities entirely. But there’s a significant problem with that: most companies aren’t 100% cloud-based, and those that are vary between whether they operate in the public cloud, private cloud, or a hybrid environment.

At Bitrise, we work with thousands of mobile engineering teams, from high-growth startups to some of the world’s biggest brands. Having that unique vantage point gives us a deep understanding of the challenges teams face day to day, and the strategies that deliver the best results.

A Software Requirements Specification (SRS) is a detailed document that defines how a software system should behave, what features it must include, and the constraints under which it operates, before development begins. In simple terms, an SRS acts as a single source of truth for everyone involved in building the software.

‍ 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 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.

Looking for a managed PostgreSQL database with a free tier? We've got you covered. In this article, we'll explore the top PostgreSQL databases with free tiers to use for your applications in 2026. These free-tier options are perfect for side projects, MVPs, and learning how to use cloud databases. All these database solutions integrate with Koyeb, enabling you to seamlessly connect databases hosted by third-party providers to your applications running on the Koyeb platform.

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.