AI

As large language models (LLMs) grow in size and complexity, maximizing inference throughput while minimizing latency remains a critical challenge for enterprise production deployments. Speculative decoding is one effective strategy to address this, utilizing a lightweight draft model to guess future tokens which are then verified by the target LLM in a single forward pass. While state-of-the-art frameworks like Extrapolation Algorithm for Greater […]

The global editorial directors of WIRED and Architectural Digest on teaming up to help you understand how we live today, and what comes next.

Devices that monitor seniors for safety are appealing to worried loved ones and underresourced home care agencies.

A Silicon Valley software maker and an ecommerce company reveal to WIRED how they are navigating the emerging challenge of “tokenomics.”

In a bid to dismiss a lawsuit over xAI’s polluting gas turbines, the Justice Department claimed the company is integral to military operations—including the Iran War.

Anthropic leaders flew to Washington, DC, to meet with White House officials on Monday. After high-level talks, they’re still split on the risk Claude Fable 5 presents.

In an internal memo seen by WIRED, Bosworth promised employees more stability, better communication, and the return of workplace perks as the company seeks to improve morale.

Today, we are announcing the availability of the Gemma 4 family on Amazon Bedrock. Built by Google DeepMind and released under the Apache 2.0 license, Gemma 4 is a family of open-weight models designed with a focus on intelligence-per-parameter across a broad range of deployment scenarios. The family includes three instruction-tuned variants: Gemma 4 31B,

When your AI agent fails in production, knowing that it failed is only the beginning. The harder question is why it failed and what to fix. Traditional evaluation tells you “this agent scored 60 percent on goal completion,” but leaves you manually reviewing execution traces to understand what went wrong. For teams operating agents at

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.