From Code Foundation Models to Agents and Applications: A Practical Guide to Code Intelligence

Anthropic has launched Claude Opus 4.5, an AI model that demonstrates improved visual and quantitative reasoning capabilities compared to its predecessors, marking a gradual but significant advancement in AI technology. This release is characterized by enhanced performance and efficiency across various tasks.

President Donald Trump announced the Genesis Mission on November 24, 2025, likening it to the Manhattan Project, aiming to revolutionize scientific research in the U.S. The initiative directs the Department of Energy to create a closed-loop AI experimentation platform that integrates national laboratories and government data for enhanced research capabilities.

Anthropic has entered the competitive landscape of artificial intelligence with the launch of its latest model, Claude Opus 4.5, which is touted as a significant advancement in AI capabilities, promising improved performance and efficiency across various tasks.

Anthropic has launched Claude Opus 4.5, an advanced AI model that emphasizes coding efficiency, cost-effectiveness, and user-controlled reasoning, marking a significant step in AI development. This model is positioned as a direct competitor to offerings from OpenAI and Google, showcasing enhanced capabilities in various tasks.

A recent study has revisited the effectiveness of large language models (LLMs) like GPT and Claude in analyzing media frames, particularly in the context of news coverage surrounding the US Mpox epidemic of 2022. The research systematically evaluated these generative models against traditional methods, revealing that manual coders consistently outperformed LLMs in frame analysis tasks.

The Empathetic Cascading Networks (ECN) framework has been introduced as a multi-stage prompting technique aimed at enhancing the empathetic and inclusive capabilities of large language models, particularly GPT-3.5-turbo and GPT-4. This method involves four stages: Perspective Adoption, Emotional Resonance, Reflective Understanding, and Integrative Synthesis, which collectively guide models to produce emotionally resonant responses. Experimental results indicate that ECN achieves the highest Empathy Quotient scores while maintaining competitive metrics.

GP-GPT has been introduced as the first specialized large language model designed for gene-phenotype mapping, addressing the complexities of multi-source genomic data. This model has been fine-tuned on a vast corpus of over 3 million terms from genomics, proteomics, and medical genetics, showcasing its ability to retrieve medical genetics information and perform genomic analysis tasks effectively.

Large language models (LLMs) like ChatGPT are increasingly used in healthcare information retrieval, but they are prone to generating hallucinations—plausible yet incorrect information. A recent study, MedHalu, investigates these hallucinations specifically in healthcare queries, highlighting the gap between LLM performance in standardized tests and real-world patient interactions.