Eric J Ma's Website

In this blog post, I explore the concept of "air gaps"—those manual steps in business or scientific processes where humans bridge the gap between digital systems. I share real-world examples from labs and software workflows, discuss why these gaps matter, and offer practical advice on identifying and closing them with automation and coding agents. Curious how closing even small air gaps can transform your team's efficiency and free up mental bandwidth?

In this blog post, I reflect on the difference between tool-specific and workflow-specific agent skills, sharing how my own daily sign-off skill encodes not just automation, but my personal way of working. I argue that workflow skills come with implicit assumptions—about tools, file structures, and mental models—that need to be documented for others to benefit. Without this procedural context, skills are only half as useful. Curious how agent skills can become more human and helpful for everyone?

I spent a weekend trying to make PyMC installable in WebAssembly environments via Pyodide. The journey involved making Numba optional, setting up Pixi development environments, and documenting the WASM build process. While PyMC can now technically install in WASM, the lack of WASM support in MCMC sampling backends (JAX, nutpie) means NUTS sampling remains out of reach. This represents a fundamental infrastructure gap, not just a missing dependency. Note: the Pixi-based approach described here was my weekend exploration; the actual PR to PyTensor respected their existing mamba-based toolchain.

I share how I use Obsidian and AI coding agents to manage personal knowledge at work. By choosing plain text, building structured note types, and encoding workflows into agent skills, I reduced knowledge management overhead from 30-40% of my time to less than 10%. The system helps me manage twelve people across two teams without losing context. This is an invitation to experiment with your own PKM system.

In this blog post, I share how coding agents can supercharge data analysis, but only if we stay in control. By slowing down, asking the right questions, and structuring sessions with journals and artifact gating, we avoid chaos and keep our scientific thinking sharp. I explain the skills and patterns that help teams focus on insights rather than just generating code. Curious how you can harness agent speed without losing your scientific edge?

In this blog post, I share ten lessons I've learned from experimenting with agentic coding in data science, from setting clear goals and structuring projects to leveraging coding agents for faster iterations and better insights. I discuss practical tips like maintaining logs, generating diagnostic plots, and treating the agent as a partner in exploration. Curious how you can make AI your jazz partner in data science and boost your productivity?

In this blog post, I share my hands-on experience using AI coding models, focusing less on benchmarks and more on the day-to-day feel—how model style, personality, and the right testing harness impact productivity and flow. I discuss the trade-offs between long-horizon autonomy and short-horizon iteration, and why a constructive, enthusiastic AI assistant matters as much as raw performance. Curious how the right mix of model and harness can transform your coding workflow?

In this blog post, I share how to combine repo memory and reusable skills to create self-improving coding agents. I walk through a maturity model, explain when to update AGENTS.md versus creating a skill, and highlight the importance of metacognition in systematizing your workflows. I also discuss how agents are evolving beyond coding tools into general-purpose teammates. Curious how you can make your coding agents smarter and more helpful over time?

In this blog post, I dive into the concept of 'skills' for coding agents—reusable playbooks that streamline repetitive tasks and make workflows explicit. I share real examples, from debugging to release announcements, and discuss how skills evolve through iteration and feedback. I also touch on the challenges of distributing and updating skills compared to MCP servers. Curious about how these skills can make your coding agents smarter and more efficient?

In this blog post, I share my approach to making coding agents truly self-improving by focusing on operational feedback, not just model updates. I explain how using an AGENTS.md file as repository memory and developing reusable skills can help agents learn from mistakes and reduce repetitive guidance. My goal is to create an environment where agents get better each week without constant babysitting. Curious how these strategies can make your coding agents more effective?