Systems | Development | Analytics | API | Testing

The Fastest Way to Generate SmartBear-Ready OpenAPI Specs from Real Backend Systems | DreamFactory

Executive Summary: Organizations spend weeks or months manually reverse-engineering legacy databases into OpenAPI specifications before they can leverage SmartBear's powerful API toolchain. DreamFactory eliminates this bottleneck entirely.

Build Agentic Workflows: Expose API Orchestration as MCP Tools with Kong AI Gateway

Learn how to expose an API orchestration workflow as an MCP server using Kong AI Gateway, configure semantic guardrails, and build an agent with the Volcano SDK. We onboard GPT-4 behind /llm, orchestrate with DataKit, and debug MCP tools in Insomnia—end-to-end without adding server code.

Query Optimization Strategies for Database APIs: A Complete Technical Guide

Database performance is often the primary bottleneck in API-driven applications. Whether you're serving a mobile app, powering a microservices architecture, or exposing enterprise data through REST APIs, slow queries translate directly to poor user experience, increased infrastructure costs, and system scalability challenges. This guide explores proven query optimization strategies that development teams can implement to dramatically improve API performance.

A Developer's Guide to MCP Servers: Bridging AI's Knowledge Gaps

Have you ever asked an AI assistant to generate code for a framework it doesn't quite understand? Maybe it produces something that looks right, but the syntax is slightly off, or it uses deprecated patterns. The AI is working hard, but it lacks the specific context it needs to truly help you. The Model Context Protocol (MCP) was designed to bridge this knowledge gap by giving AI assistants access to domain-specific knowledge and capabilities they don't have built in.

How to build a Copilot agent

A customer recently shared their debugging workflow with me. When an error shows up in Honeybadger, they import it to Linear, manually add context about where to look in the codebase, then assign GitHub Copilot to investigate. It works, but they asked a good question: could Copilot just access Honeybadger directly? The answer is yes—and it's easier than I expected.

Best 5 Tools for Monitoring AI-Generated Code in Production Environments

AI-generated code is no longer experimental. It is actively running in production environments across SaaS platforms, fintech systems, marketplaces, internal tools, and customer-facing applications. From AI copilots assisting developers to autonomous agents opening pull requests, the volume of machine-generated code entering production has increased dramatically. This shift has created a new operational challenge: how do you reliably monitor AI-generated code once it is live?

Top Microservices Examples & Guides - DreamFactory

DreamFactory is a secure, self-hosted enterprise data access platform that provides governed API access to any data source, connecting enterprise applications and on-prem LLMs with role-based access and identity passthrough. During the last 10 years, microservices-based applications have benefited global enterprises by providing them with massive scalability, greater agility, more highly available systems, and improved operational efficiency.

Running Kafka in Kubernetes: What We Learned

Apache Kafka is mission-critical for many organizations—but where you deploy it matters just as much as how you use it. In this video, two OpenLogic experts discuss why they increasingly encourage customers to move their Kafka clusters to Kubernetes and utilize the Strimzi operator, and what that shift unlocks from an operational, scalability, and resilience standpoint.

Common Kafka Anti Patterns and How to Avoid Them

Kafka is powerful—but common Kafka mistakes can quietly undermine performance, reliability, and scalability. In this video, two OpenLogic experts break down the most frequent Kafka anti-patterns they see in real customer environments—and how to avoid them. Based on hands-on experience fixing production Kafka clusters, this discussion covers: If you’re running Apache Kafka in production—or planning to—this video will help you spot Kafka mistakes early and apply proven best practices to build a more stable, scalable event streaming platform.