AI

A common challenge in AI-powered research workflows is depth versus context. If your agent reads ten web pages, its context window (the amount of text a large language model (LLM) can process at once) gets filled with raw content. If it also runs data analysis code, chart-generation logic competes with strategic reasoning for limited space. […]

Rank One, whose board includes a former CIA deputy director and a former FBI science chief, supplied face recognition to Meta for internal development of its smart glasses app.

The ruling holds that a company that designs, trains, operates, and manages an AI system must assume legal liability for any damages caused by the responses it generates.

“The government believes it has become aware of a method of bypassing, or ‘jailbreaking’ Fable 5,” the company said in a blog post.

“I’m not sure that this company supports a hackathon culture anymore,” one employee posted in a forum open to the entire staff.

Executives and employees alike are struggling with Meta’s chaotic AI strategy, according to sources and internal discussions reviewed by WIRED.

Rocket Close is a Detroit-based title agency and appraisal management company within Rocket Companies that provides title insurance, property valuation, and settlement services. As demand for mortgages and loans grew, title operations became a bottleneck in the homebuying process. Time-intensive, state-specific title examinations, combined with manual research and fragmented systems, slowed throughput and made it

GOP lawmakers, tech investors, and even OpenAI have tied the anti-data center movement in the US to Chinese interference. Experts say it’s much more complicated than that.

Amazon Quick and Cisco Webex MCP servers can turn meeting prep and follow-up into a single conversational workflow. Instead of switching between Webex meetings, Vidcast videos, transcripts, recordings, and message spaces, users ask one assistant to gather the context they need. This post shows how to build a custom meeting prep and follow-up assistant using

Organizations process millions of documents daily, from insurance claims and invoices to legal contracts and medical records. While traditional optical character recognition (OCR) solutions extract text, they can’t understand context, relationships, or meaning embedded within complex documents. This limitation creates bottlenecks that require manual intervention, increasing processing time and costs while introducing potential errors. Amazon