Operations | Monitoring | ITSM | DevOps | Cloud

As software teams race to integrate AI into their development workflows, we need to ask ourselves: are AI-powered tools actually making software better? The latest research from DORA confirms what many engineers have long suspected, and what we at Honeycomb have said for a long time: AI tools don’t magically lead to better software. In fact, without careful implementation, AI can introduce a whole slew of challenges, including decreased productivity and unreliable code.

Kubernetes has revolutionized how organizations deploy, scale, and manage containerized applications, offering unprecedented efficiency and flexibility. However, the very characteristics that make Kubernetes so powerful—its dynamic, distributed, and ephemeral nature—also create significant challenges for monitoring. Without robust monitoring capabilities, organizations struggle to identify and resolve performance bottlenecks, optimize resource utilization, and maintain security.

I first started using AI coding assistants in early 2021, with an invite code from a friend who worked on the original GitHub Copilot team. Back then, the workflow was just single-line tab completion, but you could also guide code generation with comments and it’d try its best to implement what you want. Fast forward to 2025. There’s now a wide range of coding assistants that are packed with features.

Shopify is a fantastic tool for organizations who want to sell products, but don’t want to build or maintain an e-commerce platform themselves. Even some of the largest brands that have built their own e-commerce platforms in the past have seen the value of using Shopify to accelerate their business. As your Shopify site scales and grows, however, you may need more insight into the performance of your store.

Today, a strong network forms the backbone of business success, making network visibility crucial. As modern networks continue their rapid evolution, it's essential to have an observability solution that is robust, resilient, and scalable. Teams need a solution that helps them enhance network performance and improve user experiences. They need a solution that enables them to confidently face current and future network operations challenges. Network Observability by Broadcom is that solution.

AI is everywhere, and its impressive claims are leading to rapid adoption. At this stage, I’d qualify it as charismatic technology—something that under-delivers on what it promises, but promises so much that the industry still leverages it because we believe it will eventually deliver on these claims. This is a known pattern.