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10 articles
Learn how to set up a benchmarking framework to evaluate AI coding agents like Claude Code and GPT-5.5, similar to industry benchmarks used in 2026.
Meta AI has launched NeuralBench, a unified open-source framework for benchmarking NeuroAI models using the largest EEG benchmark to date, covering 36 tasks and 94 datasets.
Learn how to set up and run a basic benchmark test for agentic reasoning using Python and Hugging Face Transformers. This tutorial teaches you to evaluate AI agents' ability to handle real-world tasks.
Learn how to build AI agents with modular skills using LangChain and OpenAI, and understand why these agents often fail in realistic conditions despite strong benchmark performance.
This article explains how to implement NVIDIA's Transformer Engine with mixed-precision, FP8 support, benchmarking, and fallback execution for optimizing transformer model performance.
This article explains how human disagreement in AI benchmarking can lead to unreliable performance metrics and why current practices need to evolve to account for annotation variability.
Nvidia sets new MLPerf records with 288 GPUs while AMD and Intel pursue different strategic paths in AI hardware competition.
AI models like GPT-5 and Gemini 3 Pro can confidently describe images they've never seen, and current benchmarks fail to detect this issue. A Stanford study highlights the dangers of AI hallucinations and calls for new evaluation methods.
This article explains the trade-offs in AI language model performance, focusing on how models like Grok 4.20 reduce hallucinations but lag behind top-tier models in benchmarks.
This article explains how current AI agent benchmarks focus narrowly on coding tasks, ignoring 92% of the US labor market, and why this limits the real-world applicability of AI systems.
