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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LLMs1 min read
Paint.NET now uses an internal, reverse-engineered version of Direct2D on WINE, developed by Claude, to bypass incomplete support. The code is unreviewed and relies on trust.
From Simon Willison
Models1 min read
Basis, Clay, and Exa Labs employ AI agents to improve onboarding, account management, and developer integrations, demonstrating enterprise applications of AI agents.
From OpenAI news
Agents1 min read
Jamf implemented real-time, per-user cost enforcement for Amazon Bedrock using IAM policies, Athena, and Lambda, enabling tiered model limits without disrupting sessions.
From AWS machine learning blog
AI1 min read
MIT's Quantum Initiative introduces a postdoctoral fellowship program to support interdisciplinary quantum research, with its first cohort starting this fall.
From MIT News: artificial intelligence
Research1 min read
Gemini Omni 1.1 Flash introduces new capabilities for building with greater control, aimed at developers managing models and agents.
From Google DeepMind blog
Models1 min read
Google AI announced a partnership involving Gemini and Pixel, aiming to improve AI integration with hardware and services, relevant for engineers managing models and agents.
From Google AI blog
Models1 min read
Google AI announced a coding course with 353,000 participants, focusing on AI agents and development tools, highlighting the scale and engagement in AI education.
From Google AI blog
Models1 min read
Google's Gemini API 3.6 introduces new features for flash learning, hooks, and triggers, enhancing managed agents for engineers.
From Google AI 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.
