Today, preparing a new benchmark to be indexed for our new RAG evaluation task. I asked the agent to write the corpus transformer and it came back clean: parsed the XML, mapped every field, ran green. On a small dataset I'd have shipped it.
I built Trivi because I kept skipping words.
Just unfamiliar ones. Terms I half-knew, expressions I'd seen but never pinned down, bash commands I couldn't quite remember. Every time I hit one, the same calculation ran in my head: Is it worth breaking my flow to look this up? The answer was usually no.
I keep noticing this new pattern in how I work, think, and make decisions. Recently, it showed up so clearly that I felt it was worth writing down.
I was trained to think economically about software work. Count your operations, mind your memory, justify your complexity. Big-O thinking does not stay in the algorithms course, it shapes how you approach everything: automate and optimize. Now I keep automation and I learn to drop optimizing. I have a vision for how I want to build and evaluate, and for the first time the friction is low enough to actually follow it.
Most of the worry around large language models lands on hallucinations, meaning incorrect information. Achieving 100% precision is a prerequisite for any financial data system. According to my evaluations for professional-grade extraction with LLMs, the harder challenge is recall. If an LLM encounters a complex website structure or a 50-page legal document, it misses details buried deep in the text.
High-quality data is often a point of pride for any company collecting them. In a recent engagement, the client had built a reliable database through a rigorous, labor-intensive process where mid-level staff reviewed every entry made by junior analysts. Even the most careful manual process eventually hits a wall: the velocity of information. In investments, data ages rapidly, and a manual team cannot scale their output to keep pace with the market without a linear increase in headcount.
So, Jupyter Notebooks. We had some good times, didn't we?
Writing code and seeing the results instantly felt great. But over time, the quirks I once found charming became... less so. Navigating between cells to find code? Frustrating. Forget to run a cell, and dependencies laugh in your face. And the workflow... Each function, each step in an experiment, means executing cells manually, at a time. It felt like trying to bake a cake but having to preheat the oven separately for every ingredient.