85% of enterprises are running agentic AI on a data foundation that isn't ready
Enterprises are pouring millions into agentic AI. New findings from Fivetran's “Agentic AI readiness index 2026” reveal why most won’t see the return.
Systems | Development | Analytics | API | Testing
Modern Software Development For Today's Systems & Applications
How Keploy Works with Claude: Generate, Fix & Heal API Tests Automatically
In this video, I walk through how Keploy works with Claude to make testing faster and more reliable for developers. You’ll see how to use Claude with Keploy to generate test cases for a new feature, based on your application context and API behavior. I also cover what happens when a Keploy test fails because of a breaking change, regression, or an expected product update.
How to scale Gen AI to billions of rows in BigQuery at a fraction of the cost
For many, running generative AI over massive datasets has felt out of reach due to costs and slow processing times. Others settle for traditional ML techniques that require specialized skill sets and often deliver lower-quality results. With optimized mode for BigQuery AI functions, you can now get LLM-quality results at a fraction of the cost and at BigQuery speeds. In this video, we’ll show you how BigQuery uses model distillation and embeddings to process massive datasets, reducing query latency and token consumption.
Automatically Provision Infrastructure for Kafka Migrations
KCP automatically generates custom Terraform modules, allowing you to provision your entire target infrastructure and networking in just a few minutes for Kafka migrations.
Enterprise AI Infrastructure Security Series - 7) Monitoring & Auditing
In this final video of our enterprise AI security series, we cover ClearML's monitoring and audit trail capabilities — the visibility layer that ties everything together. We walk through the platform's operational dashboards, task-level audit surfaces, cost attribution, and external integration points, showing how ClearML delivers live operations and compliance-ready audit out of the box.
Why RBAC Isn't Enough: Real Tenant Isolation in Kubernetes AI Environments
Role-based access control is essential, but it’s not isolation. When multiple AI teams share a Kubernetes cluster, RBAC controls what they can do; it doesn’t control what they can reach, what they can see, or what happens when something goes wrong in a neighboring workload. This is the first post in our four-part series on Kubernetes Security for Enterprise AI Environments.
From Kafka Chaos to Control: A Practical Guide to Governing Real-Time Data
Most engineering teams adopt Apache Kafka for one simple reason: it works. It scales effortlessly, it is incredibly reliable, and it powers real-time systems across almost every industry. But as your Kafka usage expands across different teams, regions, and external consumers, success creates a brand new problem. Kafka is a massive data firehose, and without the right nozzle, it quickly becomes unmanageable.
Building a Secure, Scalable AI Infrastructure with Kong and Akamai: A Technical Introduction
As organizations transition from experimental AI to production-grade systems, they often face a fragmented landscape of unmanaged LLM providers, complex tool integrations, and escalating security risks. This infrastructure gap leaves AI applications vulnerable to sophisticated threats like prompt injection and data exfiltration, necessitating a unified stack that secures the edge while streamlining the data plane..
React Native Over-the-Air Updates in 2026: Skip the App Store Wait with Codemagic CodePush
Tired of waiting days for App Store review every time you need to ship a fix? In this video we break down how Over-the-Air (OTA) updates work for React Native apps and how Codemagic CodePush lets you push hotfixes, run experiments, and do controlled rollouts without touching the App Store or Google Play.
Your AI Coding Assistant Can't See Production Errors. Here's How to Fix That.
You’ve connected your AI coding assistant to your codebase, your docs, maybe even your internal wiki. It can autocomplete functions, explain unfamiliar code, and scaffold new features. But ask it what’s actually breaking in production right now, and it has nothing. No stack traces, no error trends, no idea which deploy introduced the regression your on-call just got paged for.