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Model Context Protocol (MCP) is an open protocol that standardizes how applications provide context to large language models (LLMs). It’s becoming a foundational layer in many AI-native workflows, especially when working with real-time or continuously updating data sources. We're excited to announce that Choreo now supports the deployment of MCP servers, empowering developers to integrate AI capabilities more efficiently into their applications.

The crash-proof app doesn’t exist. It never has, and it probably never will. Because apps can crash for all kinds of reasons, some of them impossible to foresee. No matter how well we build them, crashes are going to happen to our apps. So, as devs, we need to know how to react to a crash when it happens. And in this context, understanding crash reports is crucial. They provide the clues we need to put the pieces together.

Automated testing with Cypress has revolutionized how developers ensure code quality, but writing comprehensive test suites remains time-consuming and often tedious. Artificial intelligence tools for Cypress testing are emerging as game-changers, dramatically reducing test creation time while maintaining quality. Let’s face it—writing tests can be a real drag. After you’ve spent days coding a beautiful feature, the last thing you want to do is spend another day writing tests for it.

Today, we are excited to announce our partnership with Pruna AI. Pruna AI is the optimization engine built to simplify and accelerate scalable inference. Koyeb offers a serverless cloud platform for teams to deploy ML and AI models on high-performance GPUs, CPUs, and accelerators - globally. By combining Pruna with Koyeb, you can speed up your model optimizations, achieve 5x faster inference speeds, and run them on scalable, high-performance serverless infrastructure.

Large language models are under threat from a tactic called LLM grooming, where bad actors flood public data sources with biased or misleading content to influence AI training behind the scenes. Hemraj Bedassee , Senior Solutions Manager, AI Testing,

Not long ago, I wrote about a growing problem in enterprise AI: agents that don’t talk to each other. You’ve got a customer relationship management (CRM) agent doing its thing, a data warehouse agent crunching numbers, a knowledge bot quietly surfacing documents—but none of them are sharing what they know. Instead of a smart, connected ecosystem, we’re stuck with isolated pockets of intelligence: an island of agents.

A recent study by PwC suggests that AI could contribute up to $15.7 trillion to the global economy by 2030, with automation playing a key role in boosting efficiency and innovation. AI agents are central to this transformation, streamlining workflows, handling repetitive tasks, and enabling data-driven decision-making. From virtual assistants in customer service to intelligent fraud detection in finance, these agents are reshaping industries and driving business growth.

AI code generators are revolutionizing the way software is developed. By leveraging the power of machine learning, these tools automate repetitive tasks, minimize human error, and accelerate the coding process. From GitHub Copilot to Keploy VS Code Extension, AI code generators are becoming essential for modern developers – whether they’re writing new features, debugging, or generating tests.