Eric J Ma's Website

In this blog post, I share how combining AI coding assistants with Marimo notebooks can supercharge your Python development and data science workflows. I walk through handy features like the --watch flag for live updates, the marimo check command for code quality, and even advanced options like MCP and built-in AI editing. Curious how you can automate and speed up your notebook workflow while keeping your code clean?

In this blog post, I share my first impressions of Anthropic's skills repository, comparing its token-efficient, customizable approach to the more standardized MCP server model. I break down the strengths and trade-offs of each, from creative workflows to technical utilities, and raise open questions about distribution and cross-vendor support. Curious about which approach might fit your workflow best?

In this blog post, I share what I learned building LlamaBot: the real challenge in AI-assisted development is keeping AI agents up-to-date with evolving documentation. I explain how the Model Context Protocol (MCP) lets LLMs access dynamic, queryable knowledge bases, solving the obsolescence problem and enabling smarter, context-aware AI assistants. Curious how you can make your documentation instantly accessible to any AI agent?

In this blog post, I share my hands-on comparison of DSPy and LlamaBot for building structured LLM applications, using a real-world expense extraction example. I explore how each framework handles schema design, type safety, and prompt optimization, highlighting their strengths and trade-offs. Curious which approach might best fit your next LLM project?

In this blog post, I share hard-earned lessons from using AI coding agents on real projects. I discuss why effective agent use goes beyond good prompts, highlighting the importance of systematic workflows, external memory, and fast iteration. I cover practical patterns for planning, testing, refactoring, and documentation, plus tips for integrating agents into your development process. Curious how these strategies can help you get the most out of coding agents?

In this blog post, I share how I solved the challenge of using multiple GitHub accounts on the same computer by configuring separate SSH keys and updating SSH and Git settings. I walk through step-by-step instructions, troubleshooting tips, and ways to automate account switching for different repositories. If you've ever struggled with Git pushing to the wrong account or want a smoother workflow for personal and volunteer projects, this guide is for you. Curious how to make Git always use the right account without hassle?

In this blog post, I share how using AGENTS.md—a new open standard for AI coding agents—lets you teach your LLM assistant project-specific preferences that persist across sessions. I cover practical tips like enforcing markdown standards, specifying test styles, and introducing new tools, all by updating AGENTS.md. This approach turns your agent into a trainable teammate, not just a forgetful bot. Want to know how to make your coding agent smarter and more aligned with your workflow?

In this blog post, I share how effective biotech data scientists master both life sciences and software skills by applying Scott Young's ultralearning principles. Drawing from my own experience, I explain how to strategically bridge knowledge gaps, focus on real-world projects, and alternate deep dives between domains for continuous growth. Want to know which ultralearning strategies can help you level up your biotech data science career?

In this blog post, I share how I've completely revamped The Data Science Bootstrap Notes for 2025, reflecting major changes in Python tooling and best practices. I discuss moving from conda to pixi and uv, automating project setup with pyds-cli, integrating AI thoughtfully, and embracing CI/CD for reproducible workflows. I also highlight the core philosophies that guide my approach and explain what outdated advice I've removed. Curious how these changes can help you build scalable, modern data science projects?

In this blog post, I share 10 practical ways I've used AI and large language models to save time and boost my effectiveness at work—beyond just coding and emails. From crafting tailored presentations and prepping for negotiations to automating tedious forms and practicing tough conversations, these strategies help you focus on what really matters. Want to know how AI can help you work smarter, not harder, beyond 2025?