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Computer raises task autonomy, lowers cost, and widens the scope of work users take on.

Evolving search from monolithic services to programmable primitives for the era of agent harnesses.

We reimplemented our Unigram tokenizer from scratch as a focused performance project.

Improving the quality-efficiency frontier of model context through query-aware context compression models.

Today we are releasing pplx-embed-v1 and pplx-embed-context-v1, two state-of-the-art text embedding models built for real-world, web-scale retrieval.