World Wire

In this tutorial, we build the entire Agentic UI stack from the ground up using plain Python, without relying on external frameworks to abstract away the core ideas. We implement the AG-UI event stream to make agent behavior observable in real time, and we bring in A2UI as a declarative layer that allows interfaces to be defined as structured JSON rather than executable code. As we progress, we enable an LLM to generate full user interfaces from natural language, synchronize agent and UI state through JSON Patch updates, and enforce human-in-the-loop safety for critical actions. Also, we gain a clear, end-to-end understanding of how agent reasoning transforms into interactive, protocol-compliant user interfaces. Copy Code Copied Use a different Browser import subprocess, sys for pkg in ["openai", "rich", "pydantic"]: subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", pkg]) import os, getpa...

In this tutorial, we explore Pyright , Microsoft’s high-performance static type checker for Python, and walk through its most powerful features in a hands-on, Colab-friendly format. We start from the ground up with basic annotations and type inference, then progressively advance through Union types, type narrowing, generics, Protocols, TypedDict, dataclasses, and modern typing constructs like Self, TypeAlias, and NewType. We also examine how strict mode raises the bar for type safety across an entire codebase and how pyrightconfig.json provides fine-grained control over diagnostic rules at the project level. Also, we deliberately introduce both correct and intentionally broken code to see exactly how Pyright catches real-world mistakes before they ever reach runtime. Copy Code Copied Use a different Browser import subprocess, sys, json, textwrap, os subprocess.check_call([sys.executable, "-m", "pip", "install", "pyright", "-q...

IBM released two new open speech recognition models— Granite Speech 4.1 2B and Granite Speech 4.1 2B-NAR — and they make a compelling case for what a ~2B-parameter speech model can do. Both are available on Hugging Face under the Apache 2.0 license. The pair targets a specific problem that enterprise AI teams know well: most production-grade automatic speech recognition (ASR) systems either demand massive compute or sacrifice accuracy to stay within budget. IBM’s bet is that careful architecture decisions can let you have it both ways. What These Models Actually Do Granite Speech 4.1 2B is a compact and efficient speech-language model designed for multilingual automatic speech recognition (ASR) and bidirectional automatic speech translation (AST) covering English, French, German, Spanish, Portuguese, and Japanese. Its non-autoregressive counterpart, Granite Speech 4.1 2B-NAR , focuses exclusively on ASR — specifically targeting latency-sensitive deployments — and supports English...

Cursor, the AI-powered code editor, is opening up the core technology behind its coding agents to developers everywhere. The Cursor team announced the public beta of the Cursor SDK — a TypeScript library that gives engineers programmatic access to the same runtime, harness, and models that power Cursor’s desktop app, CLI, and web interface. This signals a meaningful shift in how AI coding tools are being positioned: not just as interactive assistants sitting alongside a developer, but as deployable infrastructure that organizations can wire into their existing systems. From Interactive Tool to Programmable Infrastructure If you’ve used Cursor before, you know it as an IDE where you interact with an agent in real time — asking it to write functions, fix bugs, or explain code. The Cursor SDK changes the access model. Instead of a developer sitting at a keyboard, the agent can now be invoked programmatically: from a CI/CD pipeline trigger, a backend service, or embedded directly insid...