Operations | Monitoring | ITSM | DevOps | Cloud

From GPUs to Futures: The Financialization of AI Compute

The decision by CME Group and Silicon Data to create computing-power futures may become one of the most important infrastructural developments in the current stage of the artificial intelligence industry. While Nasdaq futures reflect expectations for technology-heavy growth stocks, including AI-related names, computing-power futures would track a more fundamental input: the cost of the infrastructure on which AI companies increasingly depend. In effect, for the first time, the market is beginning to formalize computing resources as an independent financial asset, comparable in function to oil, electricity, or industrial metals.

Nano Banana Three-Model Showdown: Which One Actually Fits Your Needs?

Not every image generation task is the same - and neither is every Nano Banana model. If you've landed on Kimg AI looking for the right tool, this breakdown is for you. Banana AI brings together multiple Nano Banana versions under one roof, so the only question left is: which model should you reach for first?

Innovation Week Day 2: Observability for AI, and Observability With AI

AI is reshaping the SDLC in two directions at once. AI-generated code is shipping faster and with less human supervision than ever before, while agents and LLMs are running directly in production, where they behave very differently from traditional software: non-deterministic, with a wider blast radius than any single function or component, with no stack trace to catch when something goes wrong.

Observability for the Agent Era: Day 2 | Launches

Honeycomb's Innovation Week: Observability for the Agent Era (May 12-14) For Day 2 of Innovation Week, Honeycomb's product and engineering teams will take you inside the new capabilities purpose-built for the agent era. Expect live demos, real scenarios, and a hands-on look at what it means to own observability for the Agentic era, with AI in Honeycomb to observe AI in production. A 3-Day Virtual Event for Teams Building the Future May 12: Get insights on how the best engineering teams are tackling the challenges of the agentic era.

#058 - The Future of AI and Platform Engineering with Blake Sherwood (Smarsh)

In this episode, special guest Blake Sherwood joins the show to discuss his unique career trajectory from tourism and coal mining to leading massive-scale Kubernetes migrations. Blake shares insights from his experience managing petabytes of data in high-compliance environments, delving into the practical realities of integrating AI into enterprise workflows and observability systems.

Claude Code Sandbox: The Complete Guide to Sandboxing AI Agents in Production

How to sandbox Claude Code, Codex, and other AI coding agents for production use. Compare local Docker, Daytona, E2B, and Qovery approaches - with architecture diagrams and real-world examples. Romaric founded Qovery to make Kubernetes accessible to every engineering team. He writes about platform strategy, developer experience, and the future of cloud infrastructure.

The AI Productivity Paradox: We're Measuring the Gains and Missing the Costs | Harness Blog

For the past year, I've been hearing a version of the same thing from engineering leaders: AI tools are working, productivity is up, the business case is there. And yet, something about the picture still feels incomplete. So we decided to go find out how widespread that feeling actually is. We surveyed 700 engineers and managers across five countries, and published the results in the State of Engineering Excellence 2026.

AI DevOps in 2026: How AI Coding Tools Are Breaking Your CI/CD Pipeline (and How to Fix It)

AI coding tools turned every engineer into a 10x developer. Now your CI/CD pipeline is the bottleneck. Learn how to handle 10x more deploys per engineer with Qovery's dual deployment model. Romaric founded Qovery to make Kubernetes accessible to every engineering team. He writes about platform strategy, developer experience, and the future of cloud infrastructure.