My name is Tim Gühnemann, and as an AI engineering working student at ilert, I had the privilege of developing and continuous improving ilert AI, ensuring it meets the needs of our customers and aligns with our vision. ‍ Our goal was to provide all our customers with access to ilert AI. We aimed to develop a solution that could adapt dynamically and function independently based on our use cases, similar to the OpenAI Assistant API.

As digital applications and infrastructures grow increasingly complex, managing and understanding log data has become increasingly vital in achieving practical observability, enabling organizations to detect, diagnose, and prevent issues across their systems. However, traditional log analysis methods often struggle with the volume and complexities of modern log data in cloud-native environments.

Artificial intelligence (AI) has been a part of technological innovation since the 1950s, but recent advances in large language models (LLMs) have accelerated its impact. These advancements have sparked widespread excitement, but many are missing the real disruption on the horizon: user experience (UX). As a company deeply rooted in UX innovation, we at InvGate are closely examining how this evolution will unfold.

The third article in our Data Economy series examines what it takes to unlock the opportunities with AI. Based on some of the key findings of our own research ‘Predicting the Data and AI Revolution’ and exclusive insights provided by leaders in the data economy, this piece explores how organizations are currently using AI.

Today’s IT teams face unprecedented challenges as they manage increasingly complex hybrid and multi-cloud environments and vast amounts of data. The pressure to maintain uptime, optimize performance, and ensure security – all while balancing limited resources – has become a daunting task for even the most seasoned professionals. So how can these organizations stay ahead of the curve?

This post was written by Charity Majors and Phillip Carter. In May of 2023, we released the Honeycomb Query Assistant, an LLM-backed feature that lets engineers use natural language to generate and execute queries against their telemetry data. Instead of having to master a domain-specific query language, you can simply type in things like “slow endpoints by status code” and the Query Assistant will generate a relevant Honeycomb query for you to iterate on.

From developers and tech leaders to start-ups and students, over 60,000 attendees from around the world gathered in Las Vegas for the 13th AWS re:Invent. This year’s event showcased continuing innovation from AWS, offered an abundance of content for every type of attendee, plus a sprawling Expo Hall hosting hundreds of AWS partners and industry vendors.

Large Language Models (LLMs) are now essential to modern artificial intelligence (AI), enabling machines to understand and generate human language. Their impact is evident across various industries, with estimates suggesting that by 2025, 750 million apps will integrate LLMs. From enhancing customer service with intelligent chatbots to advancing medical research, LLMs are driving efficiency in business operations.

Mike Hoy, CTO, Pulsant looks ahead to the next 12 months, identifying some of the challenges organisations are likely to face in delivering AI projects, and highlighting the importance of a resilient, connected infrastructure in meeting these. As AI proofs of concept evolve over the next 12–18 months, they will lay the groundwork for advances in technology.

When network issues strike, every second matters. Latency or packet loss can frustrate users and hurt revenue. Learn how Kentik AI uses natural language to speed up troubleshooting and isolate problems quickly.