Writing a Work Log (fredrikmeyer.net)

A software developer recently shared insights from their year-long practice of maintaining a "work diary," which serves as a daily log of tasks and challenges faced in their programming role. This practice significantly enhances productivity and self-reflection by facilitating task prioritization, providing clarity on issues through self-dialogue, and allowing interaction with a large language model (LLM) for skills assessment and seniority estimation. The developer leverages LLM-powered queries to evaluate their work, generate relevant skills for their CV, and identify time-consuming activities like bug fixing and meetings, ultimately recommending more time for focused coding efforts. By utilizing Emacs with Org Mode for efficient logging, the developer has established a structured approach to documenting their workday. This method not only tracks progress but also offers traceability, enabling them to verify completed tasks. The practical application of LLMs in analyzing work logs presents a significant implication for the AI/ML community, illustrating how these tools can assist in personal productivity and skill development, making the case for wider adoption among software professionals looking to enhance their workflow with AI integration.