
Red Hat Shrinks Nemotron 3.5 Lightning's 30B Agent Model by Half With FP8
Takeaways − Red Hat AI released an FP8 quantized build of NVIDIA Nemotron 3.5 Lightning 30B A3B. Cuts GPU memory and disk by roughly 50% versus the BF16 reference weights.

Takeaways − Red Hat AI released an FP8 quantized build of NVIDIA Nemotron 3.5 Lightning 30B A3B. Cuts GPU memory and disk by roughly 50% versus the BF16 reference weights.

The book “Disciplined Entrepreneurship” by Bill Aulet, managing director of the Martin Trust Center for MIT Entrepreneurship and the Ethernet Inventors…

“What you see is what you get” is a guiding principle for many software engineers — create programs where the content you’re editing looks the same as the…

What Is Jev? A Guide to TypeSafe AI’s System One Model Agents run in a loop: an LLM decides what to do, a tool executes, a model evaluates the results, and then continues in that loop until the task is complete.

Expanding AI Storage Access with NVIDIA cuObject and the NVIDIA SCADA Server SDK | NVIDIA Technical Blog Expanding AI Storage Access with NVIDIA cuObject and the NVIDIA SCADA Server SDK By Harish Arora , Vikram Sharma Mailthody , Kiran K.

As agentic applications become more autonomous, enterprises need shared infrastructure for context, model, and tool access; governance; evaluation; and observability, rather than rebuilding these capabilities for every agent.

Modulate , the Boston audio AI company behind a gaming voice moderation tool called ToxMod , has raised USD $25 million in new funding.

Warning : Undefined variable $stocks in /var/www/briefs.co/htdocs/wp-content/plugins/oxygen/component-framework/components/classes/code-block.class.php(133) : eval()'d code on line 448 Warning : foreach() argument must be of type array|object, null given in /var/www/briefs.co/htdocs/wp-content/plugins/oxygen/component-framework/components/classes/code-block.class.php(133) : eval()'d code on line 448 Warning : Undefined variable $funds in /var/www/briefs.co/htdocs/wp-content/plugins/oxygen/component-framework/components/classes/code-block.class.php(133) : eval()'d code on line 472 Warning : foreach() argument must be of type array|object, null given in /var/www/briefs.co/htdocs/wp-content/plugins/oxygen/component-framework/components/classes/code-block.class.php(133) : eval()'d...

Author(s): Chew Loong Nian – AI ENGINEER Originally published on Towards AI.

Author(s): Quan Huynh Originally published on Towards AI. Build an AI Agent Evaluation with JEV Build a small eval harness for a tool-using AI agent: code…

Have you ever read a paper in Science or Nature and thought, “Man, that research was so cool.

Every tool you've ever set up has an onboarding screen you just straight click past.

Video AI company Pika offers new tool to speed up AI video creation Video AI company Pika launched a program to help AI creators make videos faster by using fewer prompts. The product automates which AI model is used to create a given element in the video and speeds up the prompting process.

The GitHub Copilot CLI, GitHub Copilot app, and GitHub Copilot SDK are all backed by the Copilot agent runtime, an agentic harness that can be embedded into…

Hello again :) I actually built a tool I want to use - you may find it helpful too.Explaining design styles/layouts/components is a nightmare for…

ToolGrad: Efficient tool-use dataset generation with textual "gradients" ToolGrad: Efficient tool-use dataset generation with textual "gradients" Zhongyi Zhou, Research Scientist, and Ruofei Du, Interactive Perception & Graphics Lead, Google XR ToolGrad is a data generation framework that reverses the traditional paradigm by first generating tool-use answers before user queries. We show this design enables LLMs to achieve better tool-use performance.

Multilingual Reasoning Image Inputs Safety Modes Citations Tool Use Structured Outputs For both trial keys and production keys, North Small Translate is free until rate limits are reached. Learn more about rate limits for different models and key types here .

Mapping global methane emissions from space with deep learning Mapping global methane emissions from space with deep learning Vishal Batchu, Research Engineer, and Michelangelo Conserva, Research Scientist, Google Research The Methane Analysis and Plume Localization with EMIT model is a deep-learning framework that automates the detection, enhancement quantification, and source estimation of methane plumes globally, turning raw satellite data into...

MUSIC IP HOLDINGS UNVEILS GROUNDBREAKING PATENT PORTFOLIO AND LICENSE FOR AI MUSIC CREATION, WITH UDIO and GRAI AS FIRST ADOPTERS MUSIC IP HOLDINGS UNVEILS GROUNDBREAKING PATENT PORTFOLIO AND LICENSE FOR AI MUSIC CREATION, WITH UDIO and GRAI AS FIRST ADOPTERS New patent framework enables responsible AI innovation, protecting artists and songwriters while supporting licensed AI services Online licensing platform makes...

Research Note: CARE-X is a research model and not a Microsoft product offering or medical device.

Introducing Muse Spark: Scaling Towards Personal Superintelligence Introducing Muse Spark: Scaling Towards Personal Superintelligence Today, we’re excited to introduce Muse Spark, the first in the Muse family of models developed by Meta Superintelligence Labs. Muse Spark is a natively multimodal reasoning model with support for tool-use, visual chain of thought, and multi-agent orchestration.

Fetch the complete documentation index at: /llms.txt Use this file to discover all available pages before exploring further. For installation instructions, usage examples, and reference information, see the Pinecone Python SDK documentation .

Using Pinecone asynchronously with FastAPI Building high-performance vector search applications requires frameworks and tools that can handle concurrent operations effectively. In this article, we'll explore the benefits of using Pinecone's Python SDK with FastAPI, a web framework for building high performance APIs in Python and asyncio.