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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
Pathway’s Baby Dragon Hatchling (BDH) architecture is being developed and scaled on Amazon SageMaker HyperPod. BDH-CQ achieved a new cost-efficiency mark on the ARC-AGI-1 benchmark.
From AWS machine learning blog
Agents1 min read
Amazon SageMaker Feature Store now supports updating individual feature values directly. The new UpdateRecord API allows for efficient, single-call updates to both Standard and In-Memory online stores, optimizing feature management.
From AWS machine learning blog
Agents1 min read
This post details extending managed MLflow and Amazon SageMaker AI Model Registry sync to two cross-account governance topologies: a hub-and-spoke pattern and a hybrid pattern. These patterns centralize governance with AWS RAM or keep development accounts isolated.
From AWS machine learning blog
Agents1 min read
Managed MLflow on SageMaker now synchronizes richer model metadata, including training metrics and lineage, into the SageMaker AI Model Registry. This allows for centralized governance of candidate models within a single AWS account using IAM guardrails.
From AWS machine learning blog
Agents1 min read
Australian teams can now invoke OpenAI GPT-5.6 models on Amazon Bedrock with cross-region inference from Sydney and Melbourne, including setup and monitoring options.
From AWS machine learning blog
Agents1 min read
Claude Fable 5.1 is now accessible on Amazon Bedrock and Claude Platform, featuring improvements and Enterprise Safeguards for data control in cloud environments.
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.
