Today I'm excited to announce that we are launching @hyperparamapp, an AI-powered Swiss Army knife for massive LLM datasets. It lets you view, score, filter, label, and transform LLM data directly in the browser. I started Hyperparam one year ago because I knew that the world of data was changing, and existing tools like Python and Jupyter Notebooks were not built for the scale of LLM data. The weights of LLMs may be tensors, but the input and output of LLMs are massive piles of text. The training set of LLMs is a large corpus of text from various sources, meticulously cleaned and preprocessed. The output of LLMs is also text, and it’s being produced in even greater quantities than the training data. No human has the patience to sift through all that text, so we need better tools to help us understand and analyze it. That's why I built Hyperparam to be the first tool specifically designed for working with LLM data at scale. To accomplish this required rethinking how data analysis tools work. I started Hyperparam as a side project and wanted to see if I could build it entirely in the browser. No Python, no servers, just pure interactive web experience. I'm pretty excited about how it turned out. Hyperparam is fast, powerful, and easy to use. It can handle datasets with millions of rows of text and provides a rich set of tools for exploring and analyzing that data using LLMs agents for assistance. Every company is now producing volumes of LLM data. Chat logs, agent traces, coding agent logs, and more. Hyperparam is designed to help you make sense of all that data. If you're working with LLM data (and let's be honest... every company is producing LLM data now), I encourage you to give Hyperparam a try. It’s free while in beta. 🚀