World Wire

NVIDIA has released Alpamayo 2 Super , a 34B-parameter vision-language-action (VLA) mode l for autonomous driving, under an open commercial license. The stated design target is the long-tail events: rare, multi-agent situations that conventional detection-and-prediction stacks handle poorly. The model pairs a 32B VLM backbone, built on NVIDIA Cosmos 3 Super Reasoner and post-trained with reinforcement learning, with a 2.3B diffusion-based action decoder. From one pass over full-surround camera video it emits a planned trajectory, a causal explanation of that trajectory, and a meta-action. Is it deployable Yes, and for commercial use from day one. The weights are released under OpenMDW-1.1, the Linux Foundation’s permissive license for open model distributions; source code is Apache 2.0. The license covers fine-tuning, derivative models and commercial redistribution. NVIDIA is applying OpenMDW across the entire Alpamayo family, so earlier releases introduced for R&D ar...

CopilotKit has just released the Channels SDK . It is an open source library that puts an existing agent inside a messaging platform. The core assumption is focused and verifiable. You already have an agent. It already has a model, tools and business logic. Channels gives it a place to work with people, without a rewrite per platform. It runs on Slack and Microsoft Teams as early access surfaces. Discord and Google Chat are named as planned. Installation is two packages: Copy Code Copied Use a different Browser npm install @copilotkit/channels @copilotkit/runtime The connection to your agent is AG-UI , the agent-user interaction protocol CopilotKit maintains. Anything that speaks AG-UI works. That includes LangGraph, CrewAI, Mastra, Pydantic AI, Google ADK, and a plain HTTP agent you wrote yourself. Your model and orchestration layer stay where they are. The distinction that matters is what a channel is not . It is not a second agent. It is a transport along...

In this tutorial, we build a complete pixel -native retrieval-augmented generation pipeline from scratch and examine how document retrieval works without relying on conventional HTML parsing, text extraction, or fixed chunking strategies. We render web pages and PDF documents as images, divide them into overlapping tiles, generate multimodal embeddings with SigLIP, CLIP, or an optional Qwen3-VL backend, and store the resulting vectors in a FAISS index for efficient similarity search. We also strengthen retrieval with OCR-based BM25 scoring and reciprocal rank fusion, aggregate tile-level evidence into document-level results, and expose the system through a FastAPI search service. Along the way, we evaluate retrieval quality using Recall@k and mean reciprocal rank, train a lightweight residual adapter with contrastive learning, visualize retrieved screenshots, and optionally pass the strongest evidence tiles to a vision-language model for grounded answer generation. Copy Code Copi...