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Benchmarking OpenAI models for automated error resolution

Large Language Models (LLMs) are increasingly shaping the future of software development, offering new possibilities in code generation, debugging, and error resolution. Recent advancements in these AI-driven tools have prompted a closer examination of their practical applications and potential impact on developer workflows.

LLMs for Customer Service and Support

Customer service has come a long way, from its roots in face-to-face interactions and phone support to today's sophisticated digital platforms. The latest phase in this evolution is about shifting from human-led processes to AI-driven solutions. According to Gartner, by 2025, 80% of customer service and support organizations will utilize generative AI technology to enhance agent productivity and elevate the overall customer experience (CX).

Lumigo Introduces AI to Simplify Observability Workflows

Lumigo is expanding its troubleshooting and observability platform with cutting-edge AI-powered tooling, now available in beta, which will provide developers and DevOps teams with the fastest and most cost-efficient way to debug and observe complex microservices. AI is quickly reshaping the technology landscape. However, observability tools have been slow to find ways to leverage AI in a fashion that provides tangible value.

Future Trends in Testing Technologies and Their Industry Implications

In today's fast-paced world, technological advancements are reshaping industries at an unprecedented rate. Testing technologies, in particular, have evolved rapidly, becoming more integral to ensuring the quality, safety, and reliability of products across various sectors. Recent studies have shown that the global market for testing technologies is expected to grow significantly in the next decade, driven by innovation and the increasing complexity of modern products.

What is Data Modeling? What is its Importance in the New Age of AI?

AI is all about the game of data. And the emergence of advanced AI models, including the newsmaker one - Generative AI, is compelling business leaders to revisit their data platforms. Stating the obvious, businesses now need customized AI models built for their unique business needs and perfected as per their data. So, your journey to a successful AI transformation or adoption begins with a robust data infrastructure as a stepping stone.

Debugging your Rancher Kubernetes Cluster the GenAI Way with k8sgpt, Ollama & Rancher Desktop

The advancements in GenAI technology are creating a significant impact across domains/sectors, and the Kubernetes ecosystem is no exception. Numerous interesting GenAI projects and products have emerged aimed at enhancing the efficiency of Kubernetes cluster creation and management. From simplifying application containerization for engineers to addressing complex Kubernetes-related queries or troubleshooting issues within a cluster, GenAI demonstrates immense potential.

AI at the Peak of Inflated Expectations? A Reality Check

The AI hype is undeniable. Buzzwords like ‘machine learning’, ‘deep learning’, and ‘artificial intelligence’ have permeated boardrooms, media, and tech conferences. However, recent market movements suggest that AI might be at the ‘peak of inflated expectations’. Nvidia, a leading player in AI hardware, has seen its stock plummet by about 20% over the last month (8th July to 8th August 2024).

New GenAI Search Revamps Customer Experience

Splunk has launched a GenAI summary feature in splunk.com and docs.splunk.com search platforms designed to give users a quick and accurate glance of the most pertinent information they are looking for. This GenAI feature serves up a contextual high-level summary pulled from various relevant search results on topics ranging from Splunk product and feature usage to general Splunk terminology.