Useful Agentic Workflows (davidgasquez.com)

A recent exploration of "Useful Agentic Workflows" showcases innovative applications of large language models (LLMs) in enhancing productivity and research through automation. The author details a range of practical use cases, such as summarizing YouTube videos, extracting structured data, and generating dataset columns. A standout approach named "magic brushes" allows for keyboard shortcuts to automate repetitive LLM tasks, leading to significant time savings and reducing the burden of data cleanup. By employing a systematic method that combines project iteration with contextual prompts and tasks, the author transforms the interaction with AI agents into a creative, almost game-like experience. This shift toward agentic engineering is significant for the AI/ML community as it exemplifies the movement towards more user-friendly, context-aware interfaces that empower users to leverage AI without requiring deep technical knowledge. Moreover, by using LLMs to generate, test, and iterate on project ideas, practitioners can effectively explore and adapt various frameworks to suit their needs, making the research process more dynamic and less tedious. The introduction of a personal knowledge base and structured prompts further exemplifies a new model of collaboration between humans and AI, transforming traditional workflows into more fluid, engaging experiences.