AI research, ideas and product updates.

Coming soon: Humanloop for LLMs

Jordan Burgess

Today, we are announcing the preview of Humanloop for Large Language Models (LLMs). Sign up to be part of the closed beta.

Why you need to calculate error bounds on your test metrics

Raza Habib

Machine learning test metrics should always be calculated with credible intervals. Credible intervals give you upper and lower bounds on test performance so you know how big your test needs to be and when to trust your models. Humanloop Active Testing can give you uncertainty bounds on your test metrics and makes this easy.

Announcing Programmatic 4.0

Raza Habib

We're really excited to announce Programmatic 4.0 with support for No-Code Templates — simple UI-based labeling functions that anyone can understand even if they don't know how to program.

Why you should be annotating in-house

Raza Habib

There are huge advantages to labeling in-house such as quality control, faster iteration and privacy. New technologies like transfer learning, programmatic labeling and active learning are now making it practical for the best teams.

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