The current developments in the conversational AI and music technology fields are marked by significant advancements in intent discovery and classification, as well as innovative approaches to music creation and retrieval. In the realm of conversational AI, there is a notable shift towards leveraging large language models (LLMs) for intent discovery, with a focus on reducing dependency on extensive labeled data. This trend is exemplified by methods that utilize in-context learning and few-shot prompting to identify and classify user intents dynamically. Additionally, there is a growing emphasis on multi-turn dialogue systems, where models are being developed to better understand and predict user intents across multiple conversational turns, enhancing both accuracy and efficiency. In the music technology domain, there are pioneering efforts to integrate advanced machine learning techniques, such as contrastive language-audio pretraining, for tasks like DJ tool retrieval, showcasing the potential for zero-shot learning in creative applications. Furthermore, novel systems are being designed to make music creation more accessible, such as intuitive interfaces using Lego bricks and Raspberry Pi, which democratize the process of music making. These developments collectively push the boundaries of what is possible in both conversational AI and music technology, offering innovative solutions that are poised to impact various industries.