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Observability Lessons NVIDIA and CoreWeave

The best thing that can happen to an open-source project is to find its way into some of the world’s largest technology companies. In September 2026, this happened twice in one week: VictoriaMetrics is now officially bundled in the self-managed edition of NVIDIA’s Cloud Functions, and I learned that CoreWeave is using VictoriaMetrics as the metrics backend for all of its infrastructure customers. Both companies adopted VictoriaMetrics independently.

YouTube Let AI Prototypes Read Production. The Token Did the Work.

TL;DR: Google Cloud’s post of 22nd July 2026 on why AI apps fail in production describes how a YouTube team gets AI prototypes in front of user research in weeks instead of quarters: a prototyping stack that hands each prototype read-only access to live production data through a proxy, keeps its code out of the production binary, and throws the prototype away once the idea is proven.

6 improvements that make Grafana Assistant better at helping your workflows

When you ask Grafana Assistant to build a dashboard or investigate an alert, you're doing so because there's work to be done. To do that work effectively, you need the right data, changes you can inspect, and conclusions supported by evidence. You also want Assistant to avoid mistakes and act quickly. Over the past six months, our team has worked hard to improve what makes those outcomes possible.

How SGP.32 unlocks next-generation IoT deployments

Every time a smart utility meter transmits consumption data, a connected vehicle updates its telemetry, or a fleet of logistics trackers crosses international borders, seamless and uninterrupted connectivity is vital. Yet for years, managing Internet of Things (IoT) devices at scale has been constrained by physical SIM logistics, operator lock-in and remote SIM provisioning standards that were simply never built for autonomous, resource-constrained hardware.

AI wrote the bug. You need production data to reproduce it

A pull request lands with a function an AI assistant generated. It reads cleanly, passes the existing tests, and gets merged. Three weeks later it breaks, but only for accounts with a specific billing history. Nobody wrote a test for that condition because nobody was thinking about it when the code went in. Now someone has to debug logic they didn't reason through, against a failure they can't picture without the exact data that triggered it. Debugging someone else's code has always worked this way.

Comparing Coralogix with open source observability backends

We put the same OpenTelemetry data into four backends, Coralogix, ClickHouse, OpenSearch and a Loki, Tempo and Prometheus stack, then set an agent twelve questions against each one, from counting spans to working out what is wrong in a system it has never seen. Then we forced the agents to continue investigating until they got to the right answer. In this experiment, what separated them was the time, tokens, tool calls and turns it took to get there.
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AI Is Making Infrastructure Easier to Create and Harder to Operate

AI is rapidly changing the economics of software engineering. Tasks that once required hours of engineering effort can increasingly be completed in minutes. An AI agent can write an application, generate Terraform, produce Kubernetes manifests, configure a CI/CD pipeline, and interact directly with cloud APIs. For infrastructure teams, this sounds like an enormous productivity win. And on Day 1, it is. But generating infrastructure has never been the hardest part of running it.

6 Best Jibble Alternatives for Contract and Blue-Collar Workforce Attendance (2026)

Most "best Jibble alternatives" roundups rank tools by G2 review count and feature checkboxes, which works fine for a remote consulting team deciding between Clockify and Toggl Track. It breaks down fast for a UAE facility management company running cleaning crews across a dozen client sites through two different manpower agencies, where the actual question is who gets verified at the punch, what happens when a site loses signal, and whether one platform can keep both agencies' workers straight without a supervisor cross-checking a spreadsheet by hand.

What Is a White Label SSP and Who Should Use One?

A white label SSP platform lets publishers, ad networks, and programmatic businesses run sell-side advertising infrastructure under their brand. Rather than build an entire supply-side platform from scratch, they can use existing technology to manage inventory, connect demand, run auctions, and track revenue. This model suits companies that need more control than a standard hosted platform can provide. It works particularly well for ad networks creating branded monetization products, publishers managing complex supply, and AdTech teams looking to expand their technology stack.