Browsing: Agent

In this tutorial, we take a deep dive into nanobot, the ultra-lightweight personal AI agent framework from HKUDS that packs full agent capabilities into roughly 4,000…

NVIDIA researchers introduced ProRL AGENT, a scalable infrastructure designed for reinforcement learning (RL) training of multi-turn LLM agents. By adopting a ‘Rollout-as-a-Service’ philosophy, the system decouples…

Over the past year, AI agents have evolved from merely answering questions to attempting to get real tasks done. However, a significant bottleneck has emerged: while…

def parse_click_coords(action_str): “”” Extract normalised (x, y) coordinates from a click action string. e.g., ‘click(0.45, 0.32)’ -> (0.45, 0.32) Returns None if the action is not…

import asyncio import json import io import contextlib import re from dataclasses import dataclass from typing import Callable, Awaitable import nest_asyncio nest_asyncio.apply() TOOL_DEFINITIONS = [ {…

In this tutorial, we implement a reinforcement learning agent using RLax, a research-oriented library developed by Google DeepMind for building reinforcement learning algorithms with JAX. We…

had spent nine days building something with Replit’s Artificial Intelligence (AI) coding agent. Not experimenting — building. A business contact database: 1,206 executives, 1,196 companies, sourced…

In the current landscape of Retrieval-Augmented Generation (RAG), the primary bottleneck for developers is no longer the large language model (LLM) itself, but the data ingestion…

Google has officially released the Colab MCP Server, an implementation of the Model Context Protocol (MCP) that enables AI agents to interact directly with the Google…

The NVIDIA Agent Toolkit is Jensen Huang’s answer to the question enterprises keep asking: how do we put AI agents to work without losing control of…