Author: Hunter

Analysts predict that demand for engineers will skyrocket during the next decade, and that the supply will fall substantially short. A Comptia report about the tech workforce estimates that there will be an additional 7.1 million tech jobs in the United States by 2034. Yet nearly one in three engineering jobs will go unfilled each… Read more

Shure’s new USB gaming mic looks familiar, but it might be too streamlined. Read more

In early January, soil moisture in much of Southern California was in the bottom 2 percent of historical records. Read more

The arrival of La Niña is starving California of rain, and more high Santa Ana winds could be on the way. Read more

Agents are the hottest thing in tech right now. Top firms from Google DeepMind to OpenAI to Anthropic are racing to augment large language models with the ability to carry out tasks by themselves. Known as agentic AI in industry jargon, such systems have fast become the new target of Silicon Valley buzz. Everyone from… Read more

These WIRED-tested indoor air-quality monitors have been teaching us things about our air quality we can never unsee. Read more

On January 13, Atlas C/2024 G3 will reach its closest point to the sun. Read more

Time-series forecasting plays a crucial role in various domains, including finance, healthcare, and climate science. However, achieving accurate predictions remains a significant challenge. Traditional methods like ARIMA and exponential smoothing often struggle to generalize across domains or handle the complexities of high-dimensional data. Contemporary deep learning approaches, while promising, frequently require large labeled datasets and… Read more

Retrieval-Augmented Generation (RAG) is a machine learning framework that combines the advantages of both retrieval-based and generation-based models. The RAG framework is highly regarded for its ability to handle large amounts of information and produce coherent, contextually accurate responses. It leverages external data sources by retrieving relevant documents or facts and then generating an answer… Read more

Large language models (LLMs) like OpenAI’s GPT and Meta’s LLaMA have significantly advanced natural language understanding and text generation. However, these advancements come with substantial computational and storage requirements, making it challenging for organizations with limited resources to deploy and fine-tune such massive models. Issues like memory efficiency, inference speed, and accessibility remain significant hurdles.… Read more