This Teacher Has Doubts About A.I. But He Won a Prize Using It.

The Coast Guard’s digital transformation strategy hinges on automating repetitive tasks, harnessing data and “empowering” the workforce with new tools.

A 15-day AI agent simulation shows why short tests may miss long-term risks shaped by tools, rules and other agents.

Groups tied to OpenAI, Anthropic and other industry players have spent $16 million trying to pick the next member of Congress from Manhattan.

$61 an hour by 2034 is where union housekeeper pay is headed under a strike-averting deal that lifts wages 50% over eight years, putting full-time earnings at $100,000 to $110,000 by 2032. In a market where Manhattan rooms already average between $500 and $600 a night, rates are expected to climb another 50% to 60% […]

Outside groups have spent roughly $12 million to support or oppose Mr. Bores’s campaign for a House seat in Manhattan, elevating his name in a crowded race.

Agentic AI has moved from conference hype to a budget line item. This is where the conversation gets more interesting and more uncomfortable. Unlike traditional AI systems that respond to a single prompt, classify a document, recommend an action, or generate a summary, agentic AI systems are designed to pursue goals. They plan, call tools, inspect results, retry failed steps, consult memory, hand off tasks to other agents, and sometimes critique their own work before producing an answer or taking an action. That extra autonomy is the value proposition. It also introduces the cost problem. A single chatbot interaction may consume a few thousand tokens. A useful agentic workflow can consume hundreds of thousands or millions of tokens per day because it does more than answer a question. It decomposes the problem, retrieves context, reasons through options, invokes APIs, checks the output, and often runs multiple passes before reaching a result. Therefore, the economics need to be understo

A homeschooling center in Manhattan is part of the company’s nationwide expansion. Internal documents reveal its strategy: “Opening date > safety.”

Stanford researchers released OpenJarvis, an open-source framework that runs inference, agents, memory, and learning entirely on-device. It decomposes a personal AI system into five composable primitives — Intelligence, Engine, Agents, Tools & Memory, and Learning — and lands within 3.2 points of the best cloud model at roughly 800× lower marginal API cost. The post Meet OpenJarvis: A Local-First Framework for On-Device Personal AI Agents with Tools, Memory, and Learning appeared first on MarkTechPost.