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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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Models1 min read
Google Search now offers new ways to explore and upgrade home decor, including visual ideas and style suggestions, aiding users in planning interior design projects.
From Google AI blog
Research1 min read
Gemini 3.7 Flash is a new language model introduced by Google DeepMind, designed for improved performance and efficiency in AI applications.
From Google DeepMind blog
Research1 min read
DeepMind announced SL2T, a model designed to convert sign language into text, supporting features for Deaf and hard of hearing users.
From Google DeepMind blog
AI1 min read
A study shows non-experts tend to rely on LLM-based diagnostic tools even when incorrect, while clinicians identify AI errors. This highlights differences in trust and error detection based on user experience.
From MIT News: artificial intelligence
AI1 min read
Researchers developed an automated framework that helps AI models generate CAD programs more accurately and efficiently.
From MIT News: artificial intelligence
AI1 min read
Assistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI's neural network before their chatbot ever says a word.
From MIT News: artificial intelligence
AI1 min read
Through research and entrepreneurship, Professor Devavrat Shah is helping to design methods that can handle constant decision-making using limited computational resources.
From MIT News: artificial intelligence
Research1 min read
Google DeepMind has released Nano Banana 2 Lite, a smaller language model, and Gemini Omni Flash, a new flash storage solution optimized for AI workloads. These tools are designed to enable efficient and cost-effective AI development and deployment.
From Google DeepMind 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.
