Blog
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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Research1 min read
The $ au^ au$-bench evaluates agent building from real business data, requirements, and APIs, measuring performance across multiple tasks to reflect real client engagement conditions.
From arXiv cs.AI
Agents1 min read
University Startups and g/d/n/a scaled Trinity, a conversational AI for students with disabilities, into a serverless multi-agent architecture on Amazon Bedrock for IDEA-aligned plans.
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.
