Research & Knowledge Hub
5,000+ research articles, technical guides, and in-depth analyses authored by council members and industry experts.
Latest Articles
5,000 articles
NVIDIA’s New AI Models: Nemotron 3.5 Lightning, Cosmos 3 and the Future of Agentic AI
Explore NVIDIA Nemotron 3.5 Lightning and Cosmos 3, how they support agentic and Physical AI, and what these models reveal about NVIDIA’s broader vision for autonomous intelligent systems.
How NVIDIA Cosmos 3 Enables Physical AI and Multi-Agent Workflows
Explore how NVIDIA Cosmos 3 enables Physical AI and agent-based workflows through multimodal reasoning, world simulation, action prediction, synthetic data, and autonomous system development.
NVIDIA Nemotron 3.5 Lightning vs NVIDIA Cosmos 3: Key Differences and Use Cases
Compare NVIDIA Nemotron 3.5 Lightning and NVIDIA Cosmos 3, including their architectures, reasoning capabilities, modalities, performance goals, and use cases across agentic and physical AI.
NVIDIA Cosmos 3 Explained: The Next Generation of Physical AI
Explore NVIDIA Cosmos 3, an open foundation model for Physical AI that combines vision reasoning, world simulation, multimodal generation, and action prediction for robotics and autonomous systems.
NVIDIA Nemotron 3.5 Lightning: What It Is and How It Advances AI Reasoning
Explore NVIDIA Nemotron 3.5 Lightning, a reasoning-capable 30B MoE model designed for faster agentic AI, efficient inference, coding, tool use, and specialized decision-making.
Who Created Jev AI?
Jev AI was created by TypeSafe AI and introduced by founder Diogo Almeida as the company’s first System One Model for fast, structured software decisions.
Jev AI: the complete guide to TypeSafe's System One Model
Explore Jev AI in this complete guide, including TypeSafe’s System One architecture, RLCD training, parallel sampling, typed decisions, calibrated confidence, performance, and automation use cases.
Jev for Software Automation
Explore how Jev supports software automation with fast, typed, probabilistic decisions for routing, scoring, classification, verification, branching, and workflow control.
Jev as an Intelligence Primitive
Explore how Jev functions as an intelligence primitive for software, providing fast, typed, probabilistic decisions that developers can combine into larger automated workflows.
Jev as a Decision Layer
Explore how Jev can function as a decision layer inside software, turning structured state and questions into typed, probabilistic outputs for automation and workflow control.
How Jev Fits Into an AI Stack
Learn how Jev fits into an AI stack as a fast decision layer that connects application state, structured questions, probabilistic outputs, and software automation workflows.
Jev Decision-Making Pipeline
Learn how Jev’s decision-making pipeline transforms structured program state and predefined questions into typed, probabilistic decisions for automated software workflows.