The limitations of the NLP tech stack gave birth to the modern LLM tech stack. As businesses evolve as connected, interactive, and data-intensive, business products are powered by a new layer of this modern technology.

Organizations — particularly those with high security and compliance requirements — need a customizable way to explore and innovate with artificial intelligence and other new technologies. This is why we’re happy to introduce OpenOps, an open source platform designed to help organizations experiment with AI in a secure environment they control. OpenOps is a framework of several tools that enables you to test open source AI models in a sandbox.

Katie Schmidt, senior manager of AI/machine learning user experience research, contributed to this blog. Years of user experience (UX) research and customer feedback have shown that building trust is integral to success with AI. With generative AI (GenAI), trust is more important than ever.

Interest in generative AI and large language models (LLMs) has exploded in popularity thanks to a slew of announcements and product releases, such as Stable Diffusion, Midjourney, OpenAI’s DALL-E, and ChatGPT. The arrival of ChatGPT in particular was a bellwether moment, especially for developers. For the first time, an LLM was readily available and good enough that even non-technical people could use it to generate prose, re-write emails, and generate code in seconds.

We recently ran our first hackathon in quite some time. Over two days, our team collaborated in groups on various topics. By the end of it, we had 12 demos to share with the rest of the team. These ranged from improvements in debugging HTTP request responses to the delightful “automatic swag sharer.” Within our groups, a number of us tried integrating with OpenAI’s GPT to see what smarts we could bring to our product.

D2iQ has introduced an AI assistant that helps organizations resolve problems more quickly and easily, including coding errors and system failures. The new DKP AI Navigator can help reduce the duration and cost of system and application bottlenecks, misconfigurations, and downtime, and ultimately help organizations overcome the Kubernetes skills gap.

In a fast-evolving world, adopting a hyperautomation strategy is the best way to future-proof your workplace. But how does hyperautomation affect your workforce, and what will it mean for the future of your business? Hyperautomation is the combination of intelligent technologies to expand automation. It incorporates AI, machine learning, robotic process automation (RPA), integration, document processing, and more.

Last month, at N-able’s Empower partner event in Prague, Chief Security Officer, Dave MacKinnon conducted a roundtable with executives from MSPs from the UK, Germany, Poland, and South Africa. The topic centered around artificial intelligence (AI) and initiated dialogue around whether they were planning to embrace AI and their concerns around the impact it could have on technology as a whole. After the event, I sat down with Dave to discuss some of the insights gained from the roundtable.

Each day the news assails us with a jumbled wave of trends, hype, provocative claims, and skirmishes. From news venues around the globe, the D2iQ brain trust is called upon to provide insights and commentary to help make sense of the hot topics and controversies affecting the cloud-native and Kubernetes communities.

Data centers are the backbone of the digital economy, hosting the servers, networks, and software that power the internet and cloud services. Data centers consume a lot of electricity, accounting for about 1% of the global energy demand in 2019. As AI becomes more widespread and complex, data centers will need to upgrade their hardware and infrastructure to meet the growing demand for AI processing. This will increase the cost of operating data centers in several ways.

In the ever-changing world of IT and digitalization, we’ve all probably pondered the same thought at least once: “Can AI ever replace me?” An example is the Tom and Jerry cartoon episode with the robot cat meant to replace Tom. Another is the video game Detroit: Become Human that navigates a world where self-aware androids surpass humans in intelligence. Fiction aside, we’re already seeing AI’s true capabilities with tools like Chat GPT and Bard.

Hey there! We're excited to share a new troubleshooting feature we have added to Netdata, the Netdata Assistant. We've built this tool to help you troubleshoot more effectively and with less stress. Let's dive in.

IT response teams find themselves battling against an overwhelming onslaught of incidents. Frustratingly long response times, challenges with prioritization, and the relentless pursuit of root cause are formidable adversaries that test even the most skilled teams. I remember customers’ electrifying anticipation with AI and automation a decade ago. They hoped AI could be used to instantly decode the business impact of incidents and automation to respond to incidents without human intervention.

Artificial Intelligence (AI) is revolutionizing the business landscape, offering immense potential to transform organizations into exponential enterprises. ServiceNow, recognized as the "engine of the enterprise," envisions a future of work where AI-powered solutions automate processes, enhance decision-making, and extract valuable insights from data.

In recent years, the field of Information Technology Operations (ITOPS) has witnessed a revolutionary transformation with the advent of Generative Artificial Intelligence (AI). This cutting-edge technology has brought about significant changes in how IT operations are managed and has proven to be a game-changer for businesses worldwide. In this blog post, we will explore the revolution of Generative AI in ITOPS, its impact, benefits, and the prospects it holds.

If anything’s going to decide what the future of IT automation holds, it’ll be artificial intelligence (AI) and generative AI. And if this past half-year hype has proven anything, it’s that we could see some serious, game-changing surprises. Did anyone see generative AI including tools like ChatGPT coming?

Maybe you came across the term “vector database” and are wondering whether it’s the new kid on the block of data retrieval systems. Maybe you are confused by conflicting claims about vector databases. The truth is, the approach used by vector databases has been around for a few years.