World Wire

On September 12, 2026, Anthropic CEO Dario Amodei published a writeup ‘We Must Pace the Frontier’ . Its core message is blunt: ‘We must slow the pace at which we improve the capabilities of AI models.’ Within hours, OpenAI’s Sam Altman and xAI’s Elon Musk endorsed it. The next day, Microsoft CEO Satya Nadella welcomed ‘deliberate pacing’ and ’embedded evaluators.’ Amodei’s announcement post had passed 67 million views on X by September 13, 2026. We Must Pace the Frontier: I’ve written a new essay on why the AI industry should slow down, with a three-part plan for doing so. Anthropic is unilaterally committing to the first of these steps. We’ll provide third-party evaluators with permanent, employee-level access to our… — Dario Amodei (@DarioAmodei) September 12, 2026 This is the first time the heads of 3 competing frontier labs have converged on slowing down. The obvious question for practitioners is ...

AWS introduced Pizza Bot , as a self-hosted application for AI tasks that continue while users work elsewhere. It organizes completed results and pending decisions into an email-style inbox. Earlier versions served more than 2,000 people inside Amazon, supporting meeting preparation, email drafting, Slack summaries, CRM logging, and research. The public application was rebuilt as an open source project. Deployable: Yes. Pizza Bot offers macOS, Windows, and Linux desktop builds, and browser and terminal clients connected to a local or standalone backend. Its code is licensed under Apache 2.0. An Inbox for Asynchronous Work Pizza Bot separates tasks into All , the thread history; Unread , completed work awaiting review; and Action , work paused for approval or an answer. Users can organize threads into folders and inspect delegated workers in the Activity panel. Tasks can start manually, through cron schedules, or through webhooks. The server owns scheduling. After down...

An agent, in its simplest form, is an LLM calling tools in a loop. That loop works for short jobs. Give it a task that runs for an hour and 200 tool calls, and it breaks in 2 predictable ways. The AWS Samples design guide for autonomous cloud coding agents names them directly: shallow agents suffer from context overflow, get distracted (goal loss), and do not maintain state over long periods. The layer that fixes this is not the model. It is the harness, which AWS describes as managing everything but the model. This article opens up that layer. Compaction, memory strategy, context budgeting, and todo-state are the machinery that turns a shallow loop into a deep agent. We look at how LangChain Deep Agents , Claude Code , Manus , OpenAI Codex , and Amazon Bedrock AgentCore implement each one, with the actual thresholds they ship. Why a bigger window does not fix it The obvious fix is a larger context window. The evidence says it helps less than expected. Chroma’s Cont...