Eric J Ma's Website

In this blog post, I share how months of hands-on struggle and learning paved the way for me to ship a complex graph-based memory feature for Llamabot in just two days—with AI as my design partner. I explain why you have to "earn your automation" and how AI can amplify, not replace, your critical thinking. Curious how pairing deep preparation with AI can supercharge your workflow and lead to breakthroughs?

In this blog post, I share how using xarray can transform laboratory and machine learning data management by unifying everything—measurements, features, model outputs, and splits—into a single, coordinate-aligned dataset. This approach eliminates the hassle of index-matching across multiple files, reduces errors, and makes your workflow more reproducible and cloud-ready. Curious how this unified structure can simplify your experimental data analysis and save you time? Read on to find out!

In this blog post, I reflect on my 10th year at the SciPy Conference, sharing highlights from teaching tutorials, attending inspiring talks, recording informal podcast conversations, and contributing to open source projects. I discuss the power of community, the evolution of scientific notebooks, and the importance of financial aid in making SciPy accessible. Curious about the behind-the-scenes moments and lessons learned from a decade at SciPy?

In this blog post, I reflect on the challenges of integrating AI into education and the workplace, sharing lessons from educators who found that unrestricted AI access can undermine true learning and assessment. I discuss why it's crucial to earn the privilege to use automation by first mastering foundational skills and demonstrating the ability to verify AI outputs. How can we ensure that AI enhances, rather than replaces, our critical thinking and problem-solving abilities?

In this blog post, I explore how combining the Diataxis documentation framework with Clayton Christensen's jobs-to-be-done theory can transform the way we write docs. By focusing on the specific outcomes readers want to achieve, we can make our documentation more useful and competitive—not just against other docs, but against all the ways people solve their problems. What happens when you treat your documentation as a product designed to help users get real jobs done?

In this blog post, I share how I built a fully functional receipt scanning and expense tracking app in just over an hour using an unconventional tech stack—FastAPI, HTMX, Notion, and my own LlamaBot AI. I describe how Claude Code enabled a focused, terminal-based workflow that kept me in the zone and made rapid prototyping possible. Curious how combining unusual tools can unlock new possibilities and boost your productivity? Read on to find out!

In this blog post, I share my journey from dreading slide creation to building DeckBot, a tool that automates the process, and reflect on the power of building your own tools as a data scientist. I discuss how creating custom solutions has empowered me and my teams, scaled our impact, and fostered a culture of innovation. I also highlight the importance of organizational support and the joy of learning through building. Curious how building your own tools can transform your work and mindset? Read on to find out!

In this blog post, I share why I believe the future of LLM applications lies beyond chat interfaces. Drawing on insights from colleagues, thought leaders, and my own experience building DeckBot, I argue that embedding AI into structured workflows—like TurboTax—creates more effective and delightful user experiences. Instead of relying on open-ended chat, we should inject LLMs at key moments within well-defined processes. Curious how this shift could transform the way we build and use AI-powered tools?

In this blog post, I share insights from my interviews with researchers and digital professionals on how to use AI, especially large language models, as a tool for active learning rather than passive consumption. I discuss strategies like creating personalized syllabi, applying critical thinking, and using AI for feedback, emphasizing that true learning requires effort and agency. Want to know the key trick to making AI your learning partner instead of your crutch?

In this blog post, I explore Cursor's latest update featuring automatic model routing, which eliminates the need for manual model selection. This change reduces the cognitive tax of micro-decisions, allowing me to focus more on coding. Drawing parallels to Apple's design philosophy, I discuss how removing unnecessary interfaces can enhance user experience. This shift in AI tool design suggests a future where systems handle complexity, simplifying user interactions. What other invisible frictions in AI tools could be automated away to improve our workflow?