Operations | Monitoring | ITSM | DevOps | Cloud

Digital trading firms operate in environments where milliseconds determine profit and loss. During volatile market conditions, platforms can appear fully operational while execution quality quietly degrades. When prices shift in so quickly, even a minor drift in your order-routing path means your competitors are exploiting the delta, while your platform appears perfectly green. For trading firms, observability is not just about uptime.

While AI have usage has been growing steadily for the last several years, the LLM models noticeably improved around the end of 2025. Specifically, they become more viable for software development. We are seeing the results. The feature and product delivery has picked up. One way to visualize this is by looking at the number of pull requests for your organization / software development teams. This chart shows the number of Github pull requests created by a team. Can you spot when AI usage increased?

A typical Next.js deployment can execute code in up to three different runtimes: Edge, Node.js, and the browser. You may already be capturing logs from server-side code, but if you are not capturing the full request from middleware through server rendering to the browser, you are missing a lot of debugging info when things go wrong. TL;DR: A typical Next.js deployment can run in up to three environments; Node, Edge, and the browser.

In a high-pressure incident, speed is everything. During incidents, engineers often jump between tools like traces, logs, and metrics, losing context at every step. This ruins the investigation state, slows down the process, and increases MTTR.

As we wrap up RSAC, one theme that repeatedly emerged in conversations with security leaders is that the modern enterprise has reached a critical inflection point where the velocity of machine-generated telemetry has outpaced the capacity of traditional architectures. This trend requires an approach that moves beyond the storage of information to the activation of it in ways that don’t simply exacerbate alert fatigue.

We’re proud to announce that Coralogix has earned 196 badges across 15 categories in the G2 Spring 2026 Reports, our strongest G2 performance to date. Placing in 369 reports, this represents a significant leap from Spring 2025, when we placed in 318 reports and earned 141 badges. These results are a direct reflection of the trust our customers place in Coralogix and their willingness to share honest feedback on the world’s largest software review platform.

For mobile-first organizations, the distance between a “slow app” and a “resolved ticket” is often filled with guesswork. Mobile performance is notoriously difficult to capture because it lives at the intersection of device hardware, network stability, and local code execution. Today, we are closing that gap with the launch of Coralogix Mobile Performance.

At VictoriaMetrics, we built vlagent as a high-performance log collector for VictoriaLogs. To validate its performance and correctness under a real production-like load, we developed a benchmark suite and ran it against 8 popular log collectors. This post covers the methodology, throughput results, resource usage, and delivery correctness. Collectors under the test: We’ve made all benchmark configurations and source code public, so you can reproduce and verify the results independently.