https://aiunderstanding.org/news/goodfire-presents-silico-an-agent-for-interpreting-and-debugging-ai-modelshttps://aiunderstanding.org/api/news/image/goodfire-presents-silico-an-agent-for-interpreting-and-debugging-ai-modelsGoodfire presents Silico, an agent for interpreting and debugging AI modelsGoodfire’s new Silico platform is designed to inspect hidden model representations, diagnose behavior and guide targeted interventions across life-sciences, robotics, vision and language models.https://aiunderstanding.org/api/news/video/goodfire-presents-silico-an-agent-for-interpreting-and-debugging-ai-models2026-08-26T12:01:51.315Zyes https://aiunderstanding.org/news/ollama-adds-claude-desktop-support-as-a-third-party-gatewayhttps://aiunderstanding.org/api/news/image/ollama-adds-claude-desktop-support-as-a-third-party-gatewayOllama adds Claude Desktop support as a third-party gatewayOllama says developers can connect Claude Desktop to local Ollama models, Ollama-hosted cloud models, or Anthropic models through a toggle in Ollama.https://aiunderstanding.org/api/news/video/ollama-adds-claude-desktop-support-as-a-third-party-gateway2026-08-26T03:02:51.999Zyes https://aiunderstanding.org/news/hugging-face-introduces-gradio-tool-for-building-deployable-ai-workflowshttps://aiunderstanding.org/api/news/image/hugging-face-introduces-gradio-tool-for-building-deployable-ai-workflowsHugging Face introduces Gradio tool for building deployable AI workflowsHugging Face says its new gr.Workflow feature lets developers connect typed Python functions, models, Gradio Spaces and datasets into visual pipelines that can also be exposed as REST APIs and deployed to Spaces.https://aiunderstanding.org/api/news/video/hugging-face-introduces-gradio-tool-for-building-deployable-ai-workflows2026-08-25T03:16:31.227Zyes https://aiunderstanding.org/news/aws-describes-an-ai-workflow-for-correcting-and-harmonizing-biomedical-metadatahttps://aiunderstanding.org/api/news/image/aws-describes-an-ai-workflow-for-correcting-and-harmonizing-biomedical-metadataAWS describes an AI workflow for correcting and harmonizing biomedical metadataAWS has published a deployable workflow that uses language models, embeddings and rule-based validation to identify inconsistent biomedical metadata and recommend corrections, with either human approval or agent-driven automation.https://aiunderstanding.org/api/news/video/aws-describes-an-ai-workflow-for-correcting-and-harmonizing-biomedical-metadata2026-08-24T16:56:32.834Zyes