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Building the Same App Using Various Web Frameworks [ learning engineering python 🛠 ] · 8 min read Recently, I’ve been wondering if I should migrate from my current web app stack (FastAPI, HTML, CSS, and a sprinkle of JavaScript) to a modern web framework. I was particularly interested in FastHTML, Next.js, and Svelte. FastHTML: Many folks have started building with it since Jeremy Howard launched

Evaluating the Effectiveness of LLM-Evaluators (aka LLM-as-Judge) [ llm eval production survey 🔥 ] · 49 min read LLM-evaluators, also known as “LLM-as-a-Judge”, are large language models (LLMs) that evaluate the quality of another LLM’s response to an instruction or query. Their growing adoption is partly driven by necessity. LLMs can now solve increasingly complex and open-ended tasks such as lo

Patterns for Building LLM-based Systems & Products [ llm engineering production 🔥 ] · 66 min read Discussions on HackerNews, Twitter, and LinkedIn “There is a large class of problems that are easy to imagine and build demos for, but extremely hard to make products out of. For example, self-driving: It’s easy to demo a car self-driving around a block, but making it into a product takes a decade.”

Uncommon Uses of Python in Commonly Used Libraries [ python engineering 🔥 ] · 10 min read To learn how to build more maintainable and usable Python libraries, I’ve been reading some of the most widely used Python packages. Along the way, I learned some things about Python that are off the beaten path. Here are a few things I didn’t know before. Using super() in base classes Python’s super() lets

How to Write Better with The Why, What, How Framework [ writing engineering productivity 🩷 🔥 ] · 10 min read Here’s a story from the early days of Amazon Web Services: Before writing any code, engineers spent 18 months contemplating and writing documents on how best to serve the customer. Amazon believes this is the fastest way to work—thinking deeply about what the customer needs before executi

The First Rule of Machine Learning: Start without Machine Learning [ machinelearning career 🔥 ] · 8 min read Update: This made the top of Hacker News (~600 points). Comments here. Applying machine learning effectively is tricky. You need data. You need a robust pipeline to support your data flows. And most of all, you need high-quality labels. As a result, most of the time, my first iteration doe

How to Set Up a Python Project For Automation and Collaboration [ engineering production python productivity 🔥 ] · 20 min read As your Python project gets larger in scope, it can become difficult to manage. How can we automate checks (e.g., unit testing, type-checking, linting)? How can we minimise collaboration overhead (e.g., code reviews, consistency)? How can we maximise developer experience

A couple of years ago, I started (read: was made) to adopt scrum in my work. I didn’t like it. The concept of estimation was vague to me: How do we estimate effort for data exploration or research? And after we move something from In Progress to Done, can we move it back? This happens often (in data science) where we need to revisit an upstream step, such as data preparation or feature engineering