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Can't Plan Next Year or Even Next Week? Start With One Document
If you're struggling to plan next week let alone next year, stop and assess where you actually stand. Define your desired future state, then put it on paper — just start somewhere. That one document becomes the roadmap you break down into your next 5 years. Watch the full IT Leadership Lab session.#Shorts.
Grafana Tempo: Trace diff & span pruning (August 2026 Community Call)
We will look at some new features: trace diff and span pruning Can't comment in the chat? You may need to create a channel. Join us live for an introduction to flame graphs. We’ll cover what they are, how to read them, and how to use them to find performance bottlenecks in your applications. Bring your questions! Grafana Cloud is the easiest way to get started with Grafana dashboards, metrics, logs, traces, and profiles. Our forever-free tier includes access to 10k metrics, 50GB logs, 50GB traces and more.
Grafana Stack Overview - Basic Building Blocks
Understand the basic building blocks of the Grafana Stacks as how all the elements (tools) combine together to give you a complete visual to your platform Thanks for watching!
What Is DEX? | DEX Dictionary Ep. 1
Welcome to the DEX Dictionary, a series where we break down the terms you hear all the time in the world of Digital Employee Experience. In episode one, we’re starting at the beginning: What is DEX? Learn what Digital Employee Experience means, why it matters, and how it shapes the way employees experience technology at work.
Why Growth Leaders are Abandoning Effort-based Models, and What Comes Next
Every major enterprise has placed its AI chip. McKinsey pegs the annual economic potential of generative AI at $2.6 to $4.4 trillion. HFS Research sizes the Services-as-Software market at $1.5 trillion by 2035. Gartner forecasts that 40% of enterprise applications will embed task-specific AI agents by the end of this year, up from under 5% in 2025. These are the field reports of a reordering already underway.
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.
Shipped: Monthly cost comparison in Explorer gets a glow up
Months have different numbers of days, and a monthly cost chart built on raw totals mixes that calendar difference into the trend. A 28-day February next to a 31-day March shows a 10.7% increase even when daily spend never moved. The same math works in reverse: real growth in a short month can look flat, hiding an increase worth investigating. That costs you time in two places. The first is triage.
A Practical Guide to Embedded Linux Logging
This article will walk through various types of embedded Linux logging platforms and consider tradeoffs between logging storage media, compression, and verbosity to enhance device debugging without eating into your limited resource budget.