Tom Fenton moves from local AI concepts to hands-on tools for matching LLMs to hardware, running local chatbots with Ollama and benchmarking AI performance.
The offices of Google are pictured in London on February 28, 2026. JUSTIN TALLIS/AFP via Getty Images Google released agents-cli on April 21, 2026, and it has shipped 13 updates in the 71 days since — ...
Sixtyfour CEO Saarth Shah explains how his company built AI research agents around rigorous evaluation systems rather than language model fluency, creating verification infrastructure that proves ...
Lowering computational costs in decentralized finance systems using AI-assisted contract development
Researchers have developed a benchmarking framework to assess whether artificial intelligence (AI) can generate decentralized ...
DSpark can make decoding faster, but acceptance quality still determines how much speed the system actually realizes.
A new technical paper, Agentic Hardware Design as Repository-Level Code Evolution, was published by researchers at Nvidia ...
AIPOCH, in collaboration with the Department of Pathology at Zhongshan Hospital, Fudan University, today unveiled ...
Most widely cited AI coding benchmarks, including the original SWE-bench, were built primarily around Python repositories, meaning headline performance results may not accurately predict how coding ag ...
Existing Reinforcement Learning (RL) approaches for deep search agents primarily rely on binary outcome rewards (i.e., whether the final answer is correct). However, pure outcome rewards fail to ...
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