World Wire

In this tutorial, we build a production-ready agentic workflow that prioritizes reliability over best-effort generation by enforcing strict, typed outputs at every step. We use PydanticAI to define clear response schemas, wire in tools via dependency injection, and ensure the agent can safely interact with external systems, such as a database, without breaking execution. By running everything in a notebook-friendly, async-first setup, we demonstrate how to move beyond fragile chatbot patterns toward robust agentic systems suitable for real enterprise workflows. Copy Code Copied Use a different Browser !pip -q install "pydantic-ai-slim[openai]" pydantic import os, json, sqlite3 from dataclasses import dataclass from datetime import datetime, timezone from typing import Literal, Optional, List from pydantic import BaseModel, Field, field_validator from pydantic_ai import Agent, RunContext, ModelRetry if not os.environ.get("OPENAI_API_KEY"): try: ...

Brain-computer interfaces (BCIs) are finally having their ‘foundation model’ moment. Zyphra, a research lab focused on large-scale models, recently released ZUNA , a 380M-parameter foundation model specifically for EEG signals. ZUNA is a masked diffusion auto-encoder designed to perform channel infilling and super-resolution for any electrode layout. This release includes weights under an Apache-2.0 license and an MNE-compatible inference stack. The Problem with ‘Brittle’ EEG Models For decades, researchers have struggled with the ‘Wild West’ of EEG data. Different datasets use varying numbers of channels and inconsistent electrode positions. Most deep learning models are trained on fixed channel montages, making them fail when applied to new datasets or recording conditions. Additionally, EEG measurements are often plagued by noise from electrode shifts or subject movement. ZUNA’s 4D Architecture: Spatial Intelligence ZUNA solves the generalizability problem by treating brain sign...

In this tutorial, we build an end-to-end visual document retrieval pipeline using ColPali . We focus on making the setup robust by resolving common dependency conflicts and ensuring the environment stays stable. We render PDF pages as images, embed them using ColPali’s multi-vector representations, and rely on late-interaction scoring to retrieve the most relevant pages for a natural-language query. By treating each page visually rather than as plain text, we preserve layout, tables, and figures that are often lost in traditional text-only retrieval. Copy Code Copied Use a different Browser import subprocess, sys, os, json, hashlib def pip(cmd): subprocess.check_call([sys.executable, "-m", "pip"] + cmd) pip(["uninstall", "-y", "pillow", "PIL", "torchaudio", "colpali-engine"]) pip(["install", "-q", "--upgrade", "pip"]) pip(["install", "...

The ‘uncanny valley’ is the final frontier for generative video. We have seen AI avatars that can talk, but they often lack the soul of human interaction. They suffer from stiff movements and a lack of emotional context. Tavus aims to fix this with the launch of Phoenix-4 , a new generative AI model designed for the Conversational Video Interface (CVI) . Phoenix-4 represents a shift from static video generation to dynamic, real-time human rendering. It is not just about moving lips; it is about creating a digital human that perceives, times, and reacts with emotional intelligence. The Power of Three: Raven, Sparrow, and Phoenix To achieve true realism, Tavus utilizes a 3-part model architecture. Understanding how these models interact is key for developers looking to build interactive agents. Raven-1 (Perception): This model acts as the ‘eyes and ears.’ It analyzes the user’s facial expressions and tone of voice to understand the emotional context of the conversation. Sparrow-1 ...

Google DeepMind is pushing the boundaries of generative AI again. This time, the focus is not on text or images. It is on music. The Google team recently introduced Lyria 3 , their most advanced music generation model to date. Lyria 3 represents a significant shift in how machines handle complex audio waveforms and creative intent. With the release of Lyria 3 inside the Gemini app, Google is moving these tools from the research lab to the hands of everyday users. If you are a software engineer or a data scientist, here is what you need to know about the technical landscape of Lyria 3. The Challenge of AI Music Building a music model is much harder than building a text model. Text is discrete and linear. Music is continuous and multi-layered. A model must handle melody, harmony, rhythm, and timbre all at once. It must also maintain long-range coherence . This means a song must sound like the same song from the 1st second to the 30th second . Lyria 3 is designed to solve these probl...

Google is moving beyond the rectangular screen. For over 10 years, Google designers have explored how to build interfaces for transparent displays. The result is Jetpack Compose Glimmer , a design system built specifically for display AI glasses . For devs and data scientists, this is a shift from designing for pixels to designing with light. The Additive Display Constraint Most developers are used to LCD or OLED screens. However, AI glasses use additive displays . These displays only add light to the user’s field of vision. They cannot create opaque black or make the real world darker. On an additive display, black is 100% transparent . It is not a color; it is a void. If you use a standard Material Design card (light surface with dark text), it fails. The light surface becomes a bright block of light that drains the battery and creates halation . Halation is an effect where bright light bleeds into dark areas, making text unreadable. To solve this, devs must use dark surfaces and...