Operations | Monitoring | ITSM | DevOps | Cloud

We’re excited to announce new native Bindplane destinations for the VictoriaMetrics ecosystem. It’s now easier to collect, process, and route OpenTelemetry metrics, traces, and logs at scale. You can directly connect VictoriaMetrics’ high-performance storage engines to Bindplane’s vendor-neutral, OpenTelemetry-native telemetry pipeline.

I recently wrote about a project I created on AWS Aurora PostgreSQL where I'm capturing APRS data from a radio. I focused on the ease of use, getting a database, some Lambda Functions, and a few schedulers working together with a web page. It was easy. However, I'd like to focus on a slightly different area now, performance.

At MWC Barcelona 2026, coRAN Labs and Canonical are presenting a working demonstration of a cloud-native, quantum-safe telecom platform for 5G and beyond 5G networks. This is not a conceptual exercise. It is a full 5G System (5GS) deployment with post-quantum cryptography embedded across the stack – from radio access to core, from transport interfaces to orchestration and public key infrastructure (PKI).

When a major incident strikes, every second becomes a decision point. Service degradations accelerate. Customers feel the impact. Revenue and reputation hang in the balance. In these moments, IT teams do not need abstractions or probabilistic guesses. They need guidance they can validate and decision paths they can explain with confidence long after the incident is resolved. Hybrid environments are too complex for intuition, and the repercussions of an incorrect action are significant.

In my time at Ivanti, I've witnessed firsthand how AI acts as a force multiplier across enterprise organizations. When deployed strategically, AI accelerates decision-making and operational execution at scale in a way that teams simply can't sustain manually. However, without clear and enforceable AI guardrails, implementing AI opens organizations up to serious new risks.

This post is also available in German and in French. The 2026 AI landscape has shifted from "Can we build it?" to "How much will it cost to run it?" For CTOs and engineering leaders, the challenge is no longer just model performance: it is the underlying infrastructure sprawl that silently erodes margins. When AI workloads scale, they often inherit the inefficiencies of legacy cloud models: over-provisioned instances, fragmented data pipelines, and a lack of unified context.

AI-assisted coding has evolved from a novelty into an industry standard. At ilert, we started our adoption in mid-2023, quickly realizing that success depends heavily on proper context and workflows. This is particularly acute with Rust. While the language is central to our backend infrastructure, its strict compiler rules and distinct idiomatic approaches make it notoriously difficult for modern LLMs to master.

Enterprise IT leaders face a persistent contradiction. Digital systems grow more complex each year, but operational stability and resilience do not improve at the same pace. Downtime costs are only the visible part of the problem. For large enterprises, unplanned outages can run into hundreds of thousands of dollars per hour in lost revenue, productivity, and remediation effort. The harder cost to quantify is the reputational damage when critical business services fail at the worst possible time.

Learn what causes high multicloud latency, and how you can reduce it with a few simple methods – no hardware deployment required. Latency is usually one of those problems that shows up before anyone has time to go looking for it – and troubleshooting it can feel like you’re aiming for a moving target.

In simple terms, predictive analytics is a form of analytics that tries to predict future events, trends, or behaviors based on historical and present data. You can achieve this goal in different ways, each involving trade-offs between accuracy and cost.