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Every new AI coding agent comes with the same pitch: write code faster. For most devs, that part already checks out. Codex writes a function in seconds. Claude Code refactors a file mid-meeting. Copilot fills in a test before you finish describing it. None of that touches the problem that shows up an hour later: five agents running across three repositories, each with its own diff, and no single place to see what’s stuck, what’s done, and what’s actually safe to ship.

How to monitor a PHP application in production: reading the PHP-FPM status page, checking OPcache health, and finding the request that is actually slow. Sejal Pandey works on content and growth at Last9, writing about observability, reliability, and SRE practices.

Table of Contents GPU sharing with NVIDIA hardware is becoming easier to adopt in Kubernetes but it hasn’t been easier to observe. Time-slicing lets multiple workloads share the same GPU. MPS allows CUDA workloads to execute concurrently. Schedulers like KAI make it easier to manage these shared environments. But sharing a GPU introduces a problem that is easy to underestimate.

Language servers give editors structured, real-time feedback: diagnostics, hover docs, autocomplete, and other guidance that would otherwise surface later. Language servers already exist for many of the languages and tools developers use every day, but Claude Code doesn’t automatically receive their feedback.

Based on the LeadDev panel discussion "Governance Is the Platform Problem Worth Solving," hosted in partnership with Harness, August 5, 2026. AI agents are no longer waiting for a human to approve their next move. They open pull requests, adjust configurations, and act on behalf of the people who deployed them — often faster than any review cycle can keep up.

94% of engineering leaders say their AI metrics are missing. Here's how platform engineering is changing to close that gap. Based on the InfoQ webinar "Platform Engineering in the Age of AI," featuring panelists from Harness, DKB, and Shine, August 18, 2026. 94% of engineering leaders say the AI metrics that matter most to them are missing.