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Where AI Media Actually Slows Teams Down - And It Isn't Generation

The constraint on AI-generated video and imagery inside most organisations is no longer the model. It is the review loop, the consistency of a set, and a cost model nobody agreed on in advance - and none of those three get solved by switching to a better generator. In short: budget for iteration rather than render time; build a reference library before the first deliverable; define what a project's generation allowance is up front; and evaluate models on how they respond to a single prompt edit rather than on peak output quality.

5 Best CAFM and Facilities Management Software Providers in 2026

Managing facilities across multiple businesses, sites, assets, and contractors can quickly become a full-time administrative job. That's where Computer-Aided Facilities Management (CAFM) software comes in. It gives teams a central place to manage reactive maintenance, planned preventative maintenance (PPM), assets, contractors, compliance, and more.

Garbage in, garbage out: Splunk's Steve Flanders on why AI can't fix your bad telemetry

Cortex co-founder and CTO Ganesh Datta sits down with Steve Flanders, who leads AI transformation at Splunk and wrote the book on OpenTelemetry, to talk about why AI acceleration without strong observability foundations creates more problems than it solves.

From Log Line to Merged Fix: AI SRE Agent AURA with GitHub MCP

Knowing why it broke is not the same as having it repaired. Point the agent at the repos behind the service and the change comes back as a pull request. A Govee integration crash-loops under Home Assistant because the container cannot write to a directory it does not own. That much was already established: the previous homelab video stopped at the root cause on purpose, so the next pass could improve the agent's configuration first.

AI budgeting: how to plan and forecast AI spend

AI budgeting is the process of planning, allocating, and forecasting an organization's AI spend: model and API costs, AI infrastructure, tooling, and the people running it all. It differs from traditional budgeting because AI spend is usage-based, scales with product success rather than headcount, and often spans multiple providers.