OpenAI wants to retire the leading AI coding benchmark—and the reasons reveal a deeper problem with how the whole industry measures itself.

Enter large language model (LLM) evaluation. The purpose of LLM evaluation is to analyze and refine GenAI outputs to improve their accuracy and reliability while avoiding bias. The evaluation process ...

Large language models (LLMs), artificial intelligence (AI) systems that can process human language and generate texts in ...

If mHC scales the way early benchmarks suggest, it could reshape how we think about model capacity, compute budgets and the ...

A large language model delivered high sensitivity and specificity in analyzing electronic health records of patients for ...

With reported 3x speed gains and limited degradation in output quality, the method targets one of the biggest pain points in production AI systems: latency at scale.

Researchers from the University of Maryland, Lawrence Livermore, Columbia and TogetherAI have developed a training technique that triples LLM inference speed without auxiliary models or infrastructure ...

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The Arkanix infostealer combines LLM-assisted development with a malware-as-a-service model, using dual language implementations to maximize reach and establish persistence.

Vibe coding isn’t just prompting. Learn how to manage context windows, troubleshoot smarter, and build an AI Overview ...

A team of researchers has found a way to steer the output of large language models by manipulating specific concepts inside ...

Use the vitals package with ellmer to evaluate and compare the accuracy of LLMs, including writing evals to test local models ...