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Wednesday

Tuesday

  4. AWS Machine Learning

    Building Real-Time Speech Applications on SageMaker AI with vLLM-Omni (Part 1)

    AWS has published a tutorial demonstrating how to deploy Qwen3-TTS on Amazon SageMaker AI using the vLLM-Omni Deep Learning Container, enabling text streaming in and 24 kHz PCM audio chunks streaming out through SageMaker bidirectional streaming connections, along with a companion Gradio application. This DLC is based on vLLM-Omni v1.5 and adds the bidirectional streaming capability labels and routing middleware required by SageMaker AI; the example uses the native WebSocket route v1/audio/speech/stream.

    AWS has published a tutorial demonstrating how to deploy Qwen3-TTS on Amazon SageMaker AI using the vLLM-Omni Deep Learning Container, enabling text streaming in and 24 kHz PCM audio chunks streaming out through SageMaker bidirectional streaming connections, along with a companion Gradio application. This DLC is based on vLLM-Omni v1.5 and adds the bidirectional streaming capability labels and routing middleware required by SageMaker AI; the example uses the native WebSocket route v1/audio/speec

    Matched fieldsSummary、Body

    #lab#Tutorials / practice#Speech#English text is being prepared.
  5. AWS Machine Learning

    Generating Images and Videos with vLLM-Omni on SageMaker AI (Part 2)

    On Amazon SageMaker AI, deploy two endpoints using the same AWS vLLM-Omni DLC image: a real-time endpoint running FLUX.2-klein-4B to generate images, and an asynchronous endpoint running Wan2.1-VACE-1.3B for image-conditioned video generation. A text prompt first generates a PNG, which is then sent along with a motion prompt to the video endpoint; the generated MP4 is retrieved from Amazon S3, and the example also provides a command-line workflow and an optional Streamlit interface.

    Matched fieldsSummary、Body

    #lab#Tutorials / practice#English text is being prepared.#Multimodal

Monday

Saturday

Friday

  2. AWS Machine Learning

    Building Multi-Account AI Agents with AgentCore Gateway and MCP

    AWS explains how to build multi-account AI agents using Amazon Bedrock AgentCore Gateway and MCP: the platform account runs agents through AgentCore Runtime and performs LLM inference via Amazon Bedrock, while each line-of-business account packages its data and tools as MCP servers, with the Gateway serving as a single MCP endpoint for unified tool discovery and invocation. The solution also combines AgentCore Identity with Okta for OAuth 2.0 M2M authentication, and uses Policy in AgentCore (Cedar) to achieve fine-grained authorization.

    AWS explains how to build multi-account AI agents using Amazon Bedrock AgentCore Gateway and MCP: the platform account runs agents through AgentCore Runtime and performs LLM inference via Amazon Bedrock, while each line-of-business account packages its data and tools as MCP servers, with the Gateway serving as a single MCP endpoint for unified tool discovery and invocation. The solution also combines AgentCore Identity with Okta for OAuth 2.0 M2M authentication, and uses Policy in AgentCore (Ced

    Matched fieldsSummary、Body

    #lab#Tutorials / practice#Agents#English text is being prepared.

Thursday

  2. Cato AI Labs

    How to secure the actual behavior of Bedrock AgentCore agents

    Cato AI Labs points out that Amazon Bedrock AgentCore's native access controls cannot prevent prompt injection; content exchanged between agents and models must be inspected at four stages: prompts, tool calls, tool responses, and outputs. AgentCore Policy uses Cedar rules to intercept tool calls that go through the Gateway, but tools that developers write directly into code do not pass through the Gateway and are therefore not inspected. Cato's Amazon Bedrock integration discovers all agents on AgentCore in a read-only manner and covers agents on AWS, Microsoft Foundry, Amazon Quick, and developer-built endpoints.

    changed between agents and models must be inspected at four stages: prompts, tool calls, tool responses, and outputs. AgentCore Policy uses Cedar rules to intercept tool calls that go through the Gateway, but tools that developers write directly into code do not pass through the Gateway and are therefore not inspected. Cato's Amazon Bedrock integration discovers all agents on AgentCore in a read-only manner and covers agents on AWS, Microsoft Foundry, Amazon Quick, and developer-built endpoints.

    Matched fieldsSummary、Body

    #research#Safety / alignment#Agents#English text is being prepared.

Wednesday