Normal view

Received before yesterday

How to Train an AI Agent for Command-Line Tasks with Synthetic Data and Reinforcement Learning

15 January 2026 at 16:00
What if your computer-use agent could learn a new Command Line Interface (CLI)—and operate it safely without ever writing files or free-typing shell commands?...

What if your computer-use agent could learn a new Command Line Interface (CLI)—and operate it safely without ever writing files or free-typing shell commands? In Part 1 of our series on building a computer use agent, we built a custom Bash computer-use agent using NVIDIA Nemotron in just one hour. In this sequel, we’ll take it further by teaching the same reasoning model with no prior…

Source

Multi-Agent Warehouse AI Command Layer Enables Operational Excellence and Supply Chain Intelligence

9 January 2026 at 14:00
Warehouses have never been more automated, more data-rich, or more operationally demanding than they are now—yet they still rely on systems that can’t keep...

Warehouses have never been more automated, more data-rich, or more operationally demanding than they are now—yet they still rely on systems that can’t keep up. Throughput is rising, SLAs are shrinking, and fleets of AMRs, conveyors, and sensors expand every year. But beneath that technological surface, most sites still rely on a familiar trio: a Warehouse Management System (WMS)…

Source

Inside NVIDIA Nemotron 3: Techniques, Tools, and Data That Make It Efficient and Accurate

15 December 2025 at 14:00
Agentic AI systems increasingly rely on collections of cooperating agents—retrievers, planners, tool executors, verifiers—working together across large...

Agentic AI systems increasingly rely on collections of cooperating agents—retrievers, planners, tool executors, verifiers—working together across large contexts and long time spans. These systems demand models that deliver fast throughput, strong reasoning accuracy, and persistent coherence over large inputs. They also require a level of openness that allows developers to customize, extend…

Source

💾

How to Build Privacy-Preserving Evaluation Benchmarks with Synthetic Data

12 December 2025 at 16:33
Validating AI systems requires benchmarks—datasets and evaluation workflows that mimic real-world conditions—to measure accuracy, reliability, and safety...

Validating AI systems requires benchmarks—datasets and evaluation workflows that mimic real-world conditions—to measure accuracy, reliability, and safety before deployment. Without them, you’re guessing. But in regulated domains such as healthcare, finance, and government, data scarcity and privacy constraints make building benchmarks incredibly difficult. Real-world data is locked behind…

Source

NVIDIA Kaggle Grandmasters Win Artificial General Intelligence Competition

5 December 2025 at 18:00
NVIDIA researchers on Friday won a key Kaggle competition many in the field treat as a real-time pulse check on humanity’s progress toward artificial general...

NVIDIA researchers on Friday won a key Kaggle competition many in the field treat as a real-time pulse check on humanity’s progress toward artificial general intelligence (AGI). Ivan Sorokin and Jean-Francois Puget, two members of the Kaggle Grandmasters of NVIDIA (KGMoN), came in first on the Kaggle ARC Prize 2025 public leaderboard with a 27.64% score by building a solution evaluated on…

Source

Building Scalable AI on Enterprise Data with NVIDIA Nemotron RAG and Microsoft SQL Server 2025

18 November 2025 at 20:00
At Microsoft Ignite 2025, the vision for an AI-ready enterprise database becomes a reality with the announcement of Microsoft SQL Server 2025, giving developers...

At Microsoft Ignite 2025, the vision for an AI-ready enterprise database becomes a reality with the announcement of Microsoft SQL Server 2025, giving developers powerful new tools like built-in vector search and SQL native APIs to call external AI models. NVIDIA has partnered with Microsoft to seamlessly connect SQL Server 2025 with the NVIDIA Nemotron RAG collection of open models.

Source

❌