From the back row: - Well under one percent of the web crawl frontier models learn from is in an In...

AI Engineer @ Paris 🇫🇷 on X: "From the back row: - Well under one percent of the web crawl frontier models learn from is in an Indian language. Sarvam's document model read thirteen trillion tokens of text before it saw a pixel, and four months after launch it is digitizing 35 million pages. - F… / X

AI Engineer @ Paris 🇫🇷

@aiDotEngineer

From the back row: - Well under one percent of the web crawl frontier models learn from is in an Indian language. Sarvam's document model read thirteen trillion tokens of text before it saw a pixel, and four months after launch it is digitizing 35 million pages. - Feed a model an hour of video and roughly a million visual tokens go in. The only ground truth is a transcript or a few labeled frames, so it learns from about two percent of them. Perceptron AI's fix is a model that decides which tokens to read. - A frontier model scores about thirty percent on a data lab's benchmark of decisions made from PDFs. Reducto found that handing models a parsed version of the page lifted their scores and cut their reasoning tokens. - Merve Noyan's pipeline labels images with a big open model, has two smaller models judge the boxes, and trains a detector on the survivors, for three or four dollars. Her coding agent made extra training images by flipping traffic signs left to right, until told not to. - Meta keeps most videos out of its three agent review pipeline. It compresses similar frames, caches verdicts on viral videos, and lets creators with a strong record skip it on metadata alone. Vision & OCR playlist:

youtube.com/watch?v=RQi7x-…

Building the Document Context Layer for AI Agents — Jerry Liu, LlamaIndex

From youtube.com

5:47 PM · Sep 23, 2026

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