Systems | Development | Analytics | API | Testing

Services are the core component of service meshes. But what does it mean to send traffic from a workload to a service? What is the identity of a service? How does configuration apply to services? How do services interact when your cluster is multi-zone? Kong Mesh recently accomplished a major overhaul of its service to make them more intuitive and easier to manage that we want to share.

This week, we’re excited to introduce Data Flow—a powerful no-code, data flow designer that simplifies data preparation for analytics, predictive modeling, and AI use cases.

How often have you sat in meetings trying to answer seemingly basic questions about your business, only to realize you don't have the data to answer them? Questions like: How many touches does it typically take to close a deal? Which lead sources are actually working? Why are some reps consistently outperforming others? If you've struggled to answer questions like these, you're not alone. I recently sat down with Noah Burkholz, Director of RevOps at demandDrive, to learn how they tackled this exact problem.

Have large language models hit their ceiling? Training GPT-5 might cost billions—for what? Marginal improvements? The next frontier is looking like autonomous models, RAG systems, and agentic AI are stealing the spotlight While LLMs may be nearing their peak, innovation is moving in new directions. Learn more in the latest episode of Test Case Scenario.

Learn how to migrate effortlessly from Cypress to Keploy for API testing in this step-by-step guide. Keploy is an open-source testing platform that simplifies API testing by enabling zero-code test generation and mocking, saving time and effort.

Welcome to the very first episode of! In this series, our Co-founder & COO, Rajesh Kumar, sits down for honest conversations with the people shaping Antino's journey. This episode features Naman Srivastava, our VP of Technology, as they discuss something we’re super passionate about, AI at Antino. They talk about how we’re integrating AI into our day-to-day work, how the team is adapting, and why embracing AI is so important for the future.

Depending on who you ask, ChatGPT is either a game-changer or just an incremental upgrade. On Test Case Scenario, we unpack how perceptions of ChatGPT have evolved and why it matters. Early adopters are immersed in its evolution and may overlook how far it’s come. Occasional users are blown away by improvements that feel like a leap forward. Here’s the real question, though. Are we celebrating meaningful progress or are we just too dazzled by small wins to demand true innovation?

In this video, we dive into Projection and Aggregation Policies in Snowflake, two powerful features that help you manage and secure sensitive data while unlocking new use cases. What are Projection and Aggregation Policies? Projection Policies define whether a column can be included in the output of a SQL query. Aggregation Policies enforce that queries against aggregation-constrained tables must group data into minimum-sized chunks to prevent the return of individual records, helping protect privacy.

Is AI development living up to the hype, or are we stuck in a loop of promises without delivery? Sure, Copilot can smooth your workflow—but is it a game-changer or just a convenience? And ChatGPT, is it a genius tool or just a glorified copy-paste assistant exposing the cracks in usability? AI tools might be shiny and new, but until they fit seamlessly into a developer's IDE and actually enhance flow, we’re stuck navigating buzzwords over breakthroughs.