OpenAI released Privacy Filter on the Hub this week: an open-source personally-identifiable information (PII) detector that labels text across eight categories in a single forward pass over a 128k context. Model card. We spent a few hours building with it and landed on three apps that each reveals a different slice of what it can do. Document Privacy Explorer: drop in a PDF or DOCX, read the document back with every PII span highlighted in place. Image Anonymizer: upload an […]

NVIDIA Nemotron 3 Nano Omni is a new omni-modal understanding model built for real-world document analysis, multiple image reasoning, automatic speech recognition, long audio-video understanding, agentic computer use, and general reasoning. It extends the Nemotron multimodal line from a strong vision-language system to a broader text + image + video + audio model. Nemotron 3 Nano Omni delivers best-in-class accuracy on complex document intelligence leaderboards such as MMlongbench-Doc, OCRBenchV2, while also leading in video and audio leaderboards like WorldSense and […]

We’re thrilled to share that DeepInfra is now a supported Inference Provider on the Hugging Face Hub! DeepInfra joins our growing ecosystem, enhancing the breadth and capabilities of serverless inference directly on the Hub’s model pages. Inference Providers are also seamlessly integrated into our client SDKs (for both JS and Python), making it super easy to use a wide variety of models with your preferred providers. DeepInfra is a serverless AI inference platform offering one of the most cost-effective pricing […]

An in-depth technical walkthrough of data engineering, pre-training, supervised fine-tuning, and reinforcement learning behind the Granite 4.1 LLMs. Authors: Granite Team, IBM TL;DR — Granite 4.1 is a family of dense, decoder‑only LLMs (3B, 8B, and 30B) trained on ~15T tokens using a multi‑stage pre‑training pipeline, including long‑context extension of up to 512K tokens. The models are further refined with supervised fine‑tuning on ~4.1M high‑quality curated samples and reinforcement learning via on‑policy GRPO with DAPO loss (Yu et al., 2025). […]

Summary. AI evaluation has crossed a cost threshold that changes who can do it. The Holistic Agent Leaderboard (HAL) recently spent about $40,000 to run 21,730 agent rollouts across 9 models and 9 benchmarks. A single GAIA run on a frontier model can cost $2,829 before caching. Exgentic‘s $22,000 sweep across agent configurations found a 33× cost spread on identical tasks, isolating scaffold choice as a first-order cost driver, and UK-AISI recently scaled agentic steps into the millions to study […]