Short essays, technical notes, paper reflections, learning logs, and field notes from the spaces I move through.
IBM Technology’s The 7 Skills You Need to Build AI Agents makes a point that feels increasingly true: if an agent can act in the real world, then prompt writing is only the starting point. ...
I have been looking for a clean way to explain what /goal really does in Codex. The most useful mental model I found is simple: /goal is not a prettier prompt. It is a working contract for long-running agent work. You are telling the agent what success looks like, what the boundary is, and how to know when to stop. That framing matters because the feature is built for work that outlives one turn. If the objective is durable enough, the agent can keep making progress, validate its own steps, and come back to you with a result instead of a half-finished thought. ...
I recently read Matt Pocock’s article, “5 Agent Skills I Use Every Day”. It resonated with my experience using coding agents such as Claude Sonnet and Claude Opus. The article gave me a clearer language for something I have been feeling: good agent work depends on good engineering process. We need better questions, written context, small slices, tests, and codebases that agents can understand. ...
I’ve been using Streamlit for quick internal tools and dashboards, but a colleague introduced me to Reflex, so I’m trying it out as another way to build Python web apps. What caught my attention is that Reflex is a full-stack Python framework for building web apps with UI, state, backend logic, data models, and deployment in one codebase. This is especially suitable for Python backend developers who seek to build more scalable and production-ready web apps. ...
Paper Overview Title: Optimizing Order Sets With a Large Language Model–Powered Multiagent System Authors: Liu S, Huang SS, McCoy AB, Wright AP, Horst S, Wright A Journal: JAMA Network Open Year: 2025 DOI: https://doi.org/10.1001/jamanetworkopen.2025.33277 Why This Paper? I read this paper because it sits at the intersection of clinical pharmacy, healthcare workflow, and practical AI systems. Relevant to clinical decision support and order-set maintenance Uses a multiagent LLM design instead of a single-model prompt Shows the gap between factual correctness and actual clinical usefulness Offers a good example of expert alignment in a high-stakes domain This article is a cleaned-up conversion of my original blog post into the site’s Notes format. ...
Paper Overview Title: A Framework for Human Evaluation of Large Language Models in Healthcare Derived from Literature Review Authors: Thomas Yu Chow Tam, Sonish Sivarajkumar, Sumit Kapoor, Alisa V. Stolyar, Katelyn Polanska, Karleigh R. McCarthy, Hunter Osterhoudt, Xizhi Wu, Shyam Visweswaran, Sunyang Fu, Piyush Mathur, Giovanni E. Cacciamani, Cong Sun, Yifan Peng, and Yanshan Wang Journal/Conference: npj Digital Medicine Year: 2024 DOI/Link: https://doi.org/10.1038/s41746-024-01258-7 This scoping review analyzes 142 studies of human evaluation for healthcare LLMs and argues that current practice is inconsistent, under-specified, and often too weak for high-risk clinical use cases. Selected Figures Figure 1. Healthcare applications of LLMs This figure shows where human evaluation has been used most often: clinical decision support, medical education, patient education, and question answering. Figure 7. QUEST human evaluation framework This is the most important figure in the paper because it turns the review findings into a practical evaluation workflow. Figure 9. PRISMA flow diagram This figure summarizes the literature search and screening process behind the 142 included studies. ...
This article offers a sample of basic Markdown syntax that can be used in Hugo content files. ...