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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.

Learn how Sauce AI for Insights turns your extensive test history into instant answers and value.

Software testing was once a slow and repetitive process that developers accepted as unavoidable, often consuming significant time without delivering proportional value. Traditional manual testing struggled to scale with growing application complexity and rapid release cycles. In 2026, test generating tools have reshaped this landscape by introducing automated test generation, AI-driven logic, and intelligent coverage strategies.

Orchestrating distributed AI workloads Distributed (multi-node) training has become a requirement rather than an optimization for many modern AI workloads. As model sizes grow, datasets expand, and training timelines tighten, teams increasingly rely on multiple machines, often with multiple GPUs each, to complete training efficiently.

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.

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.