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
Iris-mini and Iris-pro are search agents trained at 35B and 397B parameters, respectively, using a data pipeline and training recipe involving RL and supervised fine-tuning. Results are evaluated with and without inference-time context management.
From arXiv cs.AI
Research1 min read
MaxKernel introduces a multi-agent system for designing high-performance TPU kernels, leveraging LLMs and real-time compiler feedback. It includes human-in-the-loop, autonomous, and graph-based search paradigms.
From arXiv cs.AI
LLMs1 min read
The OpenAI Codex desktop app stores LibreOffice binaries in its cache, including full installations and usage skills, which may impact model deployment and integration strategies.
From Simon Willison
LLMs1 min read
Python 3.15.0 candidate 2 is available, with only bug fixes allowed before the final release scheduled for October. Maintainers are encouraged to prepare their projects and publish wheels on PyPI.
From Simon Willison
LLMs1 min read
This report details the evolving state of open models, focusing on size trends, licensing options, and key performance indicators for models deployed in production environments. It highlights shifts in model architecture and accessibility for engineers.
From Hugging Face blog
Research1 min read
Microsoft Research released Orchard, an open-source framework designed to simplify the training and evaluation of AI agents. This framework focuses on reducing complexity and enabling strong performance from smaller models, facilitating research in scalable agentic AI.
From Microsoft Research
Research1 min read
Flint is an open-source visualization language enabling AI agents to generate expressive charts from concise specifications. It provides a middle ground between simple chart specifications and complex manual chart creation.
From Microsoft Research
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.
