One of the most critical gaps in traditional Large Language Models (LLMs) is that they rely on static knowledge already contained within them. Basically, they might be very good at understanding and responding to prompts, but they often fall short in providing current or highly specific information.

Etcd plays a critical role in your Kubernetes setup: it stores the ever-changing state of your cluster and its objects, and the API server uses this data to manage cluster resources. As your applications thrive and your Kubernetes clusters see more traffic, etcd handles an increasing amount of data. But etcd’s storage space is limited: the recommended maximum is 8 GiB, and a large and dynamic cluster can easily generate enough data to reach that limit.

As cloud systems become increasingly sophisticated, you want a cloud monitoring platform that helps you identify, isolate, and fix root-cause issues. Meanwhile, engineering leaders are under increasing pressure to reduce technology costs as the global economic outlook remains uncertain. With Datadog, you can observe, monitor, analyze, and report on the health of your infrastructure, applications, and services in any cloud and at scale.

Large Language Models, or LLMs, have become a near-ubiquitous technology in recent years. Promising the ability to generate human-like content with simple and direct prompts, LLMs have been integrated across a diverse array of systems, purposes, and functions, including content generation, image identification and curation, and even heuristics-based performance testing for APIs and other software components.

Understanding what went wrong, what went right, and how to improve is crucial for IT teams striving for excellence. But as teams evaluate their processes and outcomes, they often encounter two tools for reflection: postmortems and retrospectives. While they may seem similar at first glance, their objectives and applications differ significantly. Let’s dive into the nuances of retrospective vs. post mortem and explore why both hold a pivotal place in team growth and project success.

When working with databases, we often encounter rows that lack data. This missing data may result from unknown or inapplicable values, errors during data import or input, or specific calculations involving non-existent values. In such cases, there are two ways to represent missing data: NULLs and empty (or blank) values. While these might seem identical at first glance, they are different and impact essential database operations in distinct ways.