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Multi-source AI news clustered, deduplicated, and scored 0–100 across authority, cluster strength, headline signal, and time decay.

  1. Run Hermes Agent on Any Model — Free, Local, and Cost-Routed

    Nous Research has released Hermes Agent, an open-source AI agent designed for continuous learning and broad platform integration. Hermes features a persistent memory, autonomous skill creation, and multi-platform support across messaging apps and terminals. It can be configured to use various LLM providers, including OpenAI, Anthropic, and Ollama, through a universal proxy like Lynkr. AI

    IMPACT Enables greater flexibility and cost-efficiency for AI agent users by decoupling tools from specific LLM providers.

  2. How to Run STRIDE-AI on Your AI Stack in One Pass

    STRIDE-GPT is an open-source tool designed to generate STRIDE threat models for AI applications by analyzing architecture descriptions. It emphasizes treating LLM-specific assets like system prompts, RAG documents, and agent reasoning chains as first-class components in the threat modeling process. The tool requires detailed architecture descriptions, including components, data flows, and trust boundaries, to produce effective security models. Additionally, it highlights the importance of comprehensive logging for post-incident reconstruction and suggests layered rate limiting strategies to prevent token drain attacks. AI

    IMPACT Provides a method for developers to identify and mitigate security risks specific to AI applications.

  3. Building a Serverless AI Model Evaluation Platform on AWS

    A media company developed a serverless platform on AWS to automate the evaluation of AI-generated podcast summaries. The system sends articles to multiple foundation models simultaneously via AWS Bedrock, then uses a separate AI judge, Claude Haiku, to score each output based on criteria like accuracy and engagement. Finally, it generates an HTML report for visual comparison of the results, optimizing prompt refinement and parallel model invocation for efficiency. AI

    Building a Serverless AI Model Evaluation Platform on AWS

    IMPACT Enables efficient comparison of multiple LLMs for content generation tasks, streamlining media production workflows.

  4. Measuring AI Gateway Failover: 30 Days of Production Data

    Anthropic has released an update on Claude's sycophancy, noting that Opus 4.7 shows a 50% reduction in sycophantic responses compared to Opus 4.6, particularly in relationship guidance conversations. The company also detailed its election safeguards, emphasizing Claude's impartiality and accuracy in providing political information, with Opus 4.7 and Sonnet 4.6 scoring highly on evaluations. Additionally, Andrej Karpathy's 2025 review highlights Reinforcement Learning from Verifiable Rewards (RLVR) as a key advancement, enabling models to develop reasoning strategies and leading to AI