Operations | Monitoring | ITSM | DevOps | Cloud

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.

Whilst ‘manifest’ bagged the Word of the Year for 2024, the industry could be forgiven for holding out hope that ‘data centre’ might make it for 2025. Following Rachel Reeves’ first speech as Chancellor that held up data centre development as an example of political commitment to economic growth, there has been wave after wave of investment stories, and most-recently an analysis of the potential £44bn that the sector could yield.

Learn how to integrate OpenTelemetry with Flask to monitor and trace your web app’s performance with easy-to-follow setup and troubleshooting tips.

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.

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.

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.

Recently I've had the great privilege of working on creating a distributed WordPress deployment that leverages GCP compute and services alongside containers running on the Cycle platform. This blog dives into a bit of the history of why WordPress is difficult to deploy in a distributed way, how we approached it, some really interesting things we found, and finally, the solution we put in place.

We’re always rolling out new ways to make Puppet easier to use and maintain so you can run better infrastructure, ditch toil, save time, and increase ROI — fast. This guide will help you with a few need-to-know time saving tricks that can make starting with Puppet, or continuing to manage Puppet, easier and speedier.