The article discusses a noticeable linguistic pattern emerging in AI-generated text – the negation structure 'it's not X, it's Y' – and how it's even appearing in spoken language. It explores the implications of this pattern for identifying AI-generated content and the potential impact on human writing and communication.
This article details new prompting techniques for ChatGPT-4.1, emphasizing structured prompts, precise delimiting, agent creation, long context handling, and chain-of-thought prompting to achieve better results.
This blog post details an experiment testing the ability of LLMs (Gemini, ChatGPT, Perplexity) to accurately retrieve and summarize recent blog posts from a specific URL (searchresearch1.blogspot.com). The author found significant issues with hallucinations and inaccuracies, even in models claiming live web access, highlighting the unreliability of LLMs for even simple research tasks.
Researchers developed RoboPAIR, an algorithm designed to jailbreak robots controlled by large language models (LLMs), demonstrating a high success rate across multiple robotic systems.
A guide on how to create and use the bubble mode in Emacs for selecting and manipulating code regions, including expanding and shifting regions, and how to integrate it with LLM queries.
This Splunk Lantern blog post highlights new articles on instrumenting LLMs with Splunk, leveraging Kubernetes for Splunk, and using Splunk Asset and Risk Intelligence.
Exploring physical interface design for LLMs, with projects like AIncense and TinyChat Computer, empowering users through tangible experiences.
The article discusses the integration of Large Language Models (LLMs) and search engines, exploring two themes: Search4LLM, which focuses on enhancing LLMs using search engines, and LLM4Search, which looks at improving search engines with LLMs.
This article introduces a practical agent-engineering framework for the development of AI agents, focusing on the key ideas and precepts within the large language model (LLM) context.
The author tests the new GPT-4o AI from OpenAI on a standard set of coding tests and finds that it delivers good results, but with one surprising issue.
