Operations | Monitoring | ITSM | DevOps | Cloud

As organizations continue to heavily invest in AI and build more agentic workflows, their telemetry data volumes can surge quickly, and the associated costs can become unpredictable. To regain control of their data, many AI-forward teams are turning to high-throughput, low-latency pipelines to collect and route data to tools such as OpenTelemetry (OTel) and ClickHouse. But these self-hosted solutions come with drawbacks.

Introducing GitKraken Desktop 12.0. At some point in the last year, the question shifted. It stopped being “should I use AI coding agents?” and became “how do I run more than one at a time without losing my mind?” If you’ve been there, you know what the management layer looks like. A terminal per agent. A worktree created by hand before each session.

Two big things in this release, a remote-updating CertKit agent Google Trust Store CA issuer support.

You can run the best debrief of your life. Honest timeline, blameless tone, real insights. People leave the room nodding. And then nothing happens. This is the last mile problem of post-mortems - and it's an easy trap to fall into. When you've just been through a stressful incident, getting it back up is the priority. Once it's over, the post-mortem itself can feel like the finish line. You've documented what happened, been honest about it, identified what went wrong. It feels like the work is done.

Last week was a great reminder for me about the challenges of the traditional model of observability defined by the “three pillars” of metrics, logs, and traces. One of the customers I’m currently working with is a large financial institution that has a robust three pillar implementation. Every critical application ships their telemetry to either or both their cloud-native tool and a central tool.

Why does your cloud cost visibility break down the moment someone spins up a Kubernetes cluster in a new region without telling anyone? You get the alert three weeks later when the bill arrives — and by then, nobody remembers which experiment justified the spend, or which team should own it. This scenario repeats constantly across platform teams managing multi-cloud environments at scale. Cloud cost visibility works fine when you have five services and one AWS account.

Technology evolves rapidly — but progress in tech isn’t driven by tools alone. It’s driven by people. By curiosity. By courage. By individuals who choose to step into complex systems and shape how they function. As an engineering leader driving application and API security, I have always believed that our industry is at its best when complex concepts are made accessible and practical for everyone.

If you’ve spent time in industrial environments, you know the problem isn’t a lack of data. It’s collecting it reliably, contextualizing it, and storing it at scale. Most stacks weren’t built to fight all three battles.

Key takeaway: Upsun eliminates the need for code freezes during ecommerce migrations by using instant, data-complete preview environments to validate replatforming efforts against production-grade data without interrupting the live store. Ecommerce replatforming is one of the highest-stakes decisions an online retailer makes, and for most, the biggest risk is what happens to revenue during the migration.

The relationship between product managers (PMs) and engineers is due for an upgrade. The division between these personas is responsible for a healthy, if laborious, collaboration when envisioning and building new products. A PM generates the vision; engineers translate it into an architectural approach, raising the technical questions that sharpen it along the way. This back-and-forth eventually produces tight alignment, a solid PRD, and functional code.