Operations | Monitoring | ITSM | DevOps | Cloud

Environment drift persists when teams standardize code but leave infrastructure, data, and access decisions to individual teams and manual setup. Most teams know their environments are not identical. What they underestimate is how quietly the gap widens. A database version is out of sync between production and staging; an environment variable is added manually to one server but never tracked; a cron job runs in production but was never captured in the dev config.

Discover the Megaport MCP Server and how it enables AI-powered, agentic networking through natural language access to network infrastructure. By Miwa Fujii, Community Manager - Terraform and Ryan Tucker, Solutions Architect In the cloud networking era, we’ve moved from manual configurations in the Portal to Infrastructure as Code (IaC), Terraform. But the next frontier isn’t just code, it’s intelligence. We are pleased to announce the release of the Megaport MCP Server (Open Beta).

Tokens per watt (TpW) – the measure of useful AI work produced per watt of energy consumed – is the metric at top of mind for CEOs, heads of AI, and infrastructure teams alike. With the tremendous cost of GPU clusters, extracting as much value as possible from the expense is critical. But in the pursuit of tokens, it’s important to remember that hardware efficiency isn’t the only factor influencing data center operating costs, or the output of useful, revenue-generating AI work.

SQL Prompt's May releases focused on a new Redgate Data Modeler integration, a unified Toolbelt Essentials installer flow, and several improvements to Prompt AI reliability.

Sometime in the last two years your repos quietly filled up with a new category of file. Not code, not config exactly: prompts. A.claude/skills/ directory here. A.cursor/rules/ folder there. A CLAUDE.md at the root, an AGENTS.md next to it, a.mcp.json listing the servers your agent is allowed to call. These are the things that make a coding agent useful on your codebase, and they're sprawling.

AI agents are becoming a standard part of how engineers write, deploy, and troubleshoot software. Getting observability data into those workflows, securely and without manual intervention, remains the harder problem.

The rise of AI-assisted coding has transformed how software is built. With tools generating entire features in seconds, the bottleneck is no longer writing code—it’s verifying it. Because AI can generate boilerplate and handle API integrations instantly, more service changes are being pushed into authentication logic, API calls, and configurations. Teams desperately need a way to verify these changes before merging, especially when the code touches external dependencies.