World Wire

In this tutorial, we implement a practical use case with Loguru , a powerful, flexible, and production-ready logging library for Python. We start by building a clean, idempotent logging setup that can be safely rerun without duplicating handlers or producing messy output. From there, we move step by step through structured logging, contextual logging, custom log levels, global patching, callable formatters, and in-memory sinks. We also handle real-world logging needs such as rich exception traces, JSON log files, custom rotation, compression, retention, async logging, threaded execution, multiprocessing-safe logging, and standard logging module interception. By keeping everything in a Colab-ready workflow, we make it easy to test, inspect, and understand how Loguru can support debugging, monitoring, and observability in serious Python applications. Copy Code Copied Use a different Browser !pip install -q loguru nest_asyncio import os, sys, time, json, glob, gzip, shutil, asyncio,...

In this tutorial, we implement a SkillNet use case as a practical framework for discovering, installing, inspecting, evaluating, and organizing reusable AI skills. We start by setting up a robust SkillNet client with SDK and REST fallback support, then compare keyword search with semantic search to understand how skills can be found for different task requirements. From there, we install curated skills from GitHub, inspect their metadata, apply a quality gate across key evaluation dimensions, and visualize relationships between skills as a graph. Finally, we build a skill-augmented agent planner that breaks a complex goal into subtasks, discovers relevant skills, filters them, and assembles an execution pipeline. Copy Code Copied Use a different Browser import sys, subprocess def _pip(*pkgs): subprocess.run([sys.executable, "-m", "pip", "install", "-q", *pkgs], check=False) print("Installing dependencies (skillnet-ai, networkx, mat...

Genesis AI released Genesis World 1.0 . The platform consists of four components: the Genesis World physics engine, Nyx (a real-time path-traced renderer), Quadrants (a Python-to-GPU compiler), and a simulation interface. It is designed to accelerate robotics foundation model development through simulation-based evaluation. Robotics model development has two bottlenecks: data and iteration speed. The field has focused heavily on data. Genesis AI argues the slower, less-discussed bottleneck is the model development cycle itself — specifically, how fast teams can evaluate candidate policies and compare model checkpoints. What Problem Does This Solve? A typical policy evaluation at Genesis spans hundreds of tasks with hundreds of episodes each. Running that in the real world requires more than 200 hours of continuous robot operation with one operator and one robot station — for a single evaluation pass. Statistically meaningful comparisons across checkpoints require many such...

Nous Research’s open-source Hermes Agent now ships a Tool Search feature. It directly addresses a growing bottleneck in AI agent systems: too many MCP tools filling up the context window. In this explainer article, we will breaks down what Tool Search does, how it works, and when to use it. The Problem: MCP Tools Are Eating Your Context Window When you connect multiple MCP (Model Context Protocol) servers to an AI agent, every tool’s JSON schema gets sent to the model on every turn. This happens even if the model only needs one or two tools for a given task. Real-world deployments feel this immediately. A Hermes deployment with five MCP servers and 34 tools shows average prompt sizes of 45,000 tokens per turn. Roughly 22,000 of those tokens — around 50% — are tool schema overhead alone. Anthropic’s own engineering data shows tool definitions can consume 134,000 tokens before optimization. Tool Attention measures the “MCP Tools Tax” at 15...

In this tutorial, we explore AgentTrove , one of the largest open-source collections of agentic interaction traces, and learn how to work with it efficiently. Instead of downloading the full dataset, we use streaming to inspect rows, detect the conversation schema, normalize agent turns, and understand how user, assistant, system, and tool messages are structured. We also build utilities to parse command-style assistant outputs, render complete trajectories in a readable format, and study how agents interact with tools across different tasks. Also, we create a lightweight analytical workflow that samples thousands of traces, converts them into a DataFrame, summarizes turn-level statistics, visualizes important dataset patterns, and exports successful traces into a clean ShareGPT-style JSONL format for supervised fine-tuning. Copy Code Copied Use a different Browser !pip -q install "datasets>=2.19" pandas matplotlib pyarrow huggingface_hub import itertools, json, coll...