World Wire

In this tutorial, we explore the Patter SDK by building a voice-agent workflow that simulates how an AI phone assistant behaves during real conversations. We work with a restaurant booking use case in which we define dynamic caller variables, register callable tools, apply output guardrails, simulate speech-to-text and text-to-speech behavior, and run a complete scripted call flow without requiring live telephony credentials. We also inspect the installed Patter API when available, create a deterministic agent brain, track modeled latency and cost metrics, and validate the system through regression-style evaluations. Finally, we understand how the Patter SDK integrates agent logic, tool use, safety checks, call simulation, and real-world deployment patterns into a single structured voice-agent pipeline. Setting Up the Patter SDK, Tools, and Restaurant Backend Copy Code Copied Use a different Browser from __future__ import annotations import sys, subprocess, importlib, inspec...

SpaceXAI has open-sourced Grok Build , the terminal-based AI coding agent behind its grok CLI. The source landed on GitHub today. The release covers the agent harness, TUI, CLI shell, and developer tooling under the Apache 2.0 license What is Grok Build? A harness is the scaffolding around a model. It assembles context, calls the model, parses the reply, and dispatches tool calls. Grok Build was launched as an early beta on May 25, 2026. It is an agent that understands your codebase, edits files, executes shell commands, and searches the web. It also manages long-running tasks. It runs as a full-screen, mouse-interactive TUI. Three surfaces exist. There is the interactive TUI, headless mode for scripting and CI. Editors embed it through the Agent Client Protocol (ACP) . What does the published area contain? Building on that framing, SpaceXAI lists four published areas. The agent loop covers context assembly, response parsing, and tool-call dispatch...

Thinking Machines Lab just released Inkling , their first model trained from scratch, weights are open, fine-tunable on Tinker. The lab pitches it as a base for customization. What is Inkling? Inkling is a Mixture-of-Experts transformer with 975B total parameters and 41B active. It supports a context window of up to 1M tokens. Pretraining covered 45 trillion tokens of text, images, audio, and video. Inputs accept text, images, and audio; output is UTF-8 text only. The research team also previewed Inkling-Small, a 276B-parameter MoE with 12B active parameters. It matches or exceeds its larger sibling on many benchmarks, and its weights arrive once testing finishes. Because customization/finetuning is the key differentiator, the architecture matters here very much. Inside The Architecture The model architecture includes a 66-layer decoder-only transformer with a sparse MoE feed-forward backbone. Each MoE layer holds 256 routed experts plus 2 shared experts. Six route...