World Wire

There is a particular kind of irony that the legal profession rarely gets to witness in such pristine form. In May 2025, Latham & Watkins a firm that routinely bills over $2,000 an hour for its partners and counts Anthropic among its clients filed a court declaration in Concord Music Group v. Anthropic that contained fabricated citation details. The citations weren’t invented by a sleep-deprived associate pulling an all-nighter. They were generated by Claude, the very AI model that Latham & Watkins was in court defending. Sit with that for a moment. The lawyer arguing that Claude is not a copyright infringement machine used Claude to format a legal citation in an active case and Claude got the authors wrong, the title wrong, and nobody caught it until opposing counsel started digging. The irony isn’t just delicious. It’s instructive. Because what happened inside that filing is a near-perfect X-ray of the structural problem that AI poses for legal prac...

In this tutorial, we build a fully interactive, multi-page web application using NiceGUI . We start by setting up the environment and designing a reusable layout that includes navigation, theming, and dark mode support. As we move forward, we implement a live dashboard with real-time metrics and charts, demonstrating reactive bindings and timed updates. We then extend the application with a complete CRUD-based todo system, followed by a validated form with dialogs and user feedback mechanisms. We also incorporate file upload functionality with dynamic previews and conclude the feature set with an asynchronous chat interface that simulates real-time interaction. Also, we ensure that the app runs seamlessly in Colab by using background threading and dynamic port allocation. Copy Code Copied Use a different Browser import sys import subprocess subprocess.run([sys.executable, "-m", "pip", "install", "-q", "nicegui"], check=True) impo...

In this tutorial, we build a complete skill-based agent system for large language models and explore how modular capabilities can be structured like an operating system for AI agents. We define reusable skills, attach metadata and schemas to them, register them in a central registry, and enable dynamic orchestration through tool calling and multi-step reasoning. As we move through the implementation, we show how an agent can select the right skill for a task, compose multiple skills for more advanced workflows, hot-load new capabilities at runtime, and track everything through an observability dashboard. Copy Code Copied Use a different Browser import os import sys import json import time import getpass from abc import ABC, abstractmethod from dataclasses import dataclass, field from datetime import datetime from enum import Enum from typing import Any, Dict, List, Optional, Type try: from openai import OpenAI from rich.console import Console from rich.panel import Pane...

In this tutorial, we walk through an end-to-end implementation of an advanced machine learning pipeline using ZenML . We begin by setting up the environment and initializing a ZenML project, then define a custom materializer that enables seamless serialization and metadata extraction for a domain-specific dataset object. As we progress, we build a modular pipeline that performs data loading, preprocessing, and a fan-out hyperparameter search across multiple models. We evaluate each candidate, log rich metadata at every step, and use a fan-in strategy to select and promote the best-performing model. Throughout the process, we leverage ZenML’s model control plane, artifact tracking, and caching mechanisms to ensure full reproducibility, transparency, and efficiency. Copy Code Copied Use a different Browser import os, sys, subprocess, json, shutil from pathlib import Path def _sh(cmd, check=True): print(f"$ {' '.join(cmd)}") return subprocess.run(cmd, check=...

Web search and content retrieval have quietly become the most critical infrastructure decisions in AI agent development. An agent without reliable access to live web data is effectively operating on stale knowledge — a hard limitation for any production deployment handling research, lead enrichment, competitive intelligence, or real-time monitoring. In 2026, the ecosystem of search and fetch APIs has matured considerably, with purpose-built tools replacing the older pattern of wrapping raw Google SERP data and passing it directly into a language model. This article covers the leading search and fetch APIs based on evaluations across output format, agent-native design, token efficiency, free tier generosity, latency, and framework integrations.  TinyFish TinyFish is an important entrant in this space and among the most directly agent-native of the group. Its Search and Fetch endpoints are free with generous rate limits — one API key, no credit card. The free plan i...