Zebra Technologies Researchers Use AI and Physics to Replicate Warehouse Imperfection
Industry insight and Generative AI address the "imperfection problem".
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Platform engineering in the age of AI
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.
How to build a Language Server Protocol (LSP) plugin for Claude Code
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.
Log Analysis with Machine Learning: An Automated Approach to Analyzing Logs Using ML/AI
AI log analysis helps IT teams turn massive volumes of operational data into actionable insight. By applying statistical methods, machine learning (ML), semantic analysis, and generative AI, organizations can identify unusual behavior, connect related signals, and investigate probable root causes faster. But AI-generated answers should not be mistaken for proof.
Best AI Infrastructure Providers for Power, Cooling, and Compute
AI infrastructure is becoming a facilities problem as much as a compute problem. Adding accelerators is only useful when the surrounding environment can support them. Power has to reach the rack reliably. Cooling has to remove the heat produced under sustained load. The network fabric has to keep accelerators communicating. Storage has to feed the workload. Orchestration and monitoring then determine whether expensive capacity spends its time doing useful work.
Day 2 Operations for AI-Generated Code: What Changes When You Didn't Write It
We have all watched AI speed up the way we write software. With tools like Copilot and ChatGPT, developers can spin up boilerplate, write complex functions, or draft entire micro services in minutes instead of days. It feels like magic. But there is a silent catch that we do not talk about enough: writing the code is only Day 1. The real challenge is Day 2 operations, which is everything that happens after that code is deployed.
Trust AI at scale
What exactly are your agents up to? See how Cisco and Splunk help you trace every decision and control your costs.
HITL for autonomous agents: Where does the human go?
Human approval is easy when you are sitting in front of the agent. For an agent running by itself in a cluster, almost none of that holds. You’re in a meeting and your agent is running in a cluster. It has a service account, it has been asked to keep a service healthy, and it has just worked out that the right fix is to roll back a database migration. Nobody is watching it. That was rather the point of deploying it. You want to get notified to approve such an important action.
Why AI Creative Workflows Are Moving Beyond Single-Purpose Generators
AI generation is no longer the difficult part of creating digital content. Generating an image from a prompt can take seconds. Creating a short AI video is also becoming increasingly accessible. Editing a background, modifying an object, or producing another visual variation can often be handled with a few instructions. The harder problem appears after the first generation.
Broadcom Unveils AAI v26, Bringing Financial Accountability and AI-Driven Insights to Workload Automation
New release turns automation into a measurable business service with precise cost attribution, a conversational data interface, and AI-hardened security.