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How big are our embeddings now and why?

Sep 1 2025 #embeddings #openai #anthropic #huggingface #dimensionality A few years ago, I wrote a paper on embeddings. At the time, I wrote that 200-300 dimension embeddings were fairly common in industry, and that adding more dimensions during training would create diminishing returns for the effectiveness of your downstream tasks (classification, recommendation, semantic search, topic modeling, etc.) I wrote the paper to be resilient to changes in the industry since it focuses on fundamenta

LGND wants to make ChatGPT for the Earth

The Earth is awash in data about itself. Every day, satellites capture around 100 terabytes of imagery. But making sense of it isn’t always easy. Seemingly simple questions can be fiendishly complex to answer. Take this question that is of vital economic importance to California: How many fire breaks does the state have that might stop a wildfire in its tracks, and how have they changed since the last fire season? “Originally, you’d have a person look at pictures. And that only scales so far,”