As today’s businesses increasingly rely on their digital services to drive revenue, the tolerance for software bugs, slow web experiences, crashed apps, and other digital service interruptions is next to zero. Developers and engineers bear the immense burden of quickly resolving production issues before they impact customer experience.

Large Language Models (LLMs) can give notoriously inconsistent responses when asked the same question multiple times. For example, if you ask for help writing an Elasticsearch query, sometimes the generated query may be wrapped by an API call, even though we didn’t ask for it. This sometimes subtle, other times dramatic variability adds complexity when integrating generative AI into analyst workflows that expect specifically-formatted responses, like queries.

A recently conducted survey of 51 CISOs and other security leaders a series of questions about the current demand for cybersecurity solutions, spending intentions, security posture strategies, tool preferences, and vendor consolidation expectations. While the report highlights the trends around platform consolidation over the short run, 82% of respondents stated they expect to increase the number of vendors in the next 2-3 years.

Logz.io is excited to announce Easy Connect, which will enable our customers to go from zero to full observability in minutes. By automating service discovery and application instrumentation, Easy Connect provides nearly instant visibility into any component in your Kubernetes-based environment – from your infrastructure to your applications. Since applications have been monitored, collecting logs, metrics, and traces have often been siloed and complex.

Logs and log management have been around far longer than monitoring and it is easy to forget just how useful and essential they can be for modern observability. Most of you will know us for VictoriaMetrics, our open source time series database and monitoring solution. Metrics are our “thing”; but as engineers, we’ve had our fair share of frustrations in the past caused by modern logging systems that tend to create further complexity, rather than removing it.

Preventing data loss for data in motion is a challenge that Cribl Stream Persistent Queues (PQ) can help prevent when the downstream Destination is unreachable. In this blog post, we’ll talk about how to configure and calculate PQ sizing to avoid disruption while the Destination is unreachable for a few minutes or a few hours. The example follows a real-world architecture, in which we have.