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Daily notes on new models, LLM releases, agent frameworks and AI research, written from the sources we follow and delivered as a newsletter every day.
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Agents1 min read
The AWS Ray Serve Deep Learning Container provides a supported, pre-tested container for TorchServe workloads, eliminating the need to manage the entire GPU inference stack. This allows teams to deploy vision-language models on Amazon EKS using a single GPU node.
From AWS machine learning blog
Agents1 min read
This post details four patterns for automating user-level custom permissions in Amazon Quick, enabling least-privilege access enforcement. These patterns cover user registration, account defaults, event-driven automation, and retroactive updates.
From AWS machine learning blog
Agents1 min read
A GitHub Actions pipeline can be integrated with Amazon Bedrock AgentCore to automatically evaluate AI agent behavior. This allows for regression detection and immediate blocking of pull requests when agent performance degrades.
From AWS machine learning blog
Agents1 min read
A multimodal WhatsApp ordering assistant was deployed using Amazon Bedrock AgentCore and Amazon Nova 2, supporting text, voice notes, and real-time voice calls on a single business number.
From AWS machine learning blog
Agents1 min read
Amazon Bedrock can now be customized for large, complex documents by combining it with Amazon Textract's high-accuracy text extraction, enabling scalable ingestion and querying of PDFs and images.
From AWS machine learning blog
Agents1 min read
The post details migrating a customer support agent from a notebook environment to Amazon Bedrock AgentCore, progressing through stages to reduce operational burdens and enhance deployment efficiency.
From AWS machine learning blog
Agents1 min read
Amazon announced a method to embed Quick Sight visuals into React apps with per-user access control using Cognito and Lambda, deployed via CloudFormation.
From AWS machine learning blog
Agents1 min read
A global broker built an automated pipeline using Amazon Bedrock AgentCore to analyze .NET code, generate diagrams, and maintain searchable documentation via AWS services.
From AWS machine learning blog
Posts are drafted from public feeds by models OpenSmartRoute routes to - the same router, skill and metering customers use - and always link to the original source. Corrections: support.
How this blog is made
Each feed entry becomes one request to OpenSmartRoute: the router picks a model with a cost-weighted objective, the editorial-writer skill is layered on the prompt, and the outcome trains the learners - the same pipeline available to every workspace.
Open any post to see which target answered, its confidence, the alternatives and what the request cost. Run the same pipeline yourself: register feeds in the operator console, map a small model under Providers, or call POST /api/v1/route with execute: true.
