Tech

Amazon challenges competitors with on-premises Nvidia 'AI Factories'

If the product name sounds familiar, it should. It's a collab with Nvidia that combines AWS tech with the chip maker's tech.

What does it mean when Uncle Sam is one of your biggest shareholders? Chip startup xLight is about to find out

You can imagine how this is all going over in Silicon Valley, where the libertarian ethos runs deep.

Mistral closes in on Big AI rivals with new open-weight frontier and small models

Mistral unveils its Mistral 3 lineup, including a frontier model and efficient small models designed for offline, customizable enterprise use — aiming to prove small, fine-tuned AI can beat closed-source giants.

No, you can't get your AI to ‘admit’ to being sexist, but it probably is anyway

Though LLMs might not use explicitly biased language, they may infer your demographic data and display implicit biases, researchers say.

Supabase hit $5B by turning down million-dollar contracts. Here's why.

Watch as Supabase's CEO discusses the open-source database platform's rise and what it means for vibe coding, developers and the database giants who have historically controlled this market.

Optimizing GGUFs for Decoder-Only Embedding Models

Two weeks ago, we released GGUF formats of jina-embeddings-v4 - a universal embedding model for multimodal multilingual retrieval - with various quantized versions. Our motivation was simple: as a 3.75B parameter model, the vanilla transformer version of jina-embeddings-v4 doesn't scale well on our GCP G2 (L4 GPU) API instances, so we wanted to speed up inference using these smaller, faster GGUF versions. During our experiments, we discovered some interesting findings while converting and running GGUF embedding models. Since most of the llama.cpp community focuses on LLMs, we thought it'd be valuable to share this from an embedding provider's perspective.