Osmosis is a forward-deployed reinforcement learning platform that helps companies train task-specific AI models that outperform foundation models at a fraction of the cost. Build, deploy, and continuously improve intelligent systems with hands-on integration and real-time feedback.
A Small Model Just for Structured Output
Osmosis-Structure-0.6B is a small model trained with reinforcement learning to do one thing well: extract structured data, typically JSON, from unformatted text. That’s it!
Liquid AI — Device-native foundation models.
Liquid AI is an efficiency-first foundation model company. We build highly capable, compute-optimized models that bring intelligence to any device and medium of choice.
Introducing Clef: our open-source decision models, and new RL fine-tuning platform
We are introducing Clef and Clef-flash, open-source decision models hosted on Workers AI for high-speed classification and agentic workflows. Also launching: a new reinforcement learning platform that allows developers to fine-tune decision models using their own data.
Environments Hub: A Community Hub To Scale RL To Open AGI
RL environments are the playgrounds where agents learn. Until now, they’ve been fragmented, closed, and hard to share. We are launching the Environments Hub to change that: an open, community-powered platform that gives environments a true home.Environments define the world, rules and feedback loop of state, action and reward. From games to coding tasks to dialogue, they’re the contexts where AI learns, without them, RL is just an algorithm with nothing to act on.
Osaurus — Own Your AI on Apple Silicon
Own your AI: local-first agents with memory, tools, and identity on Apple Silicon. Offline, open source, and API-compatible with OpenAI, Anthropic, and Ollama.
Osaurus — Own Your AI on Apple Silicon
Own your AI: local-first agents with memory, tools, and identity on Apple Silicon. Offline, open source, and API-compatible with OpenAI, Anthropic, and Ollama.
INTELLECT-2: The First Globally Distributed Reinforcement Learning Training of a 32B Parameter Model
Today we are launching INTELLECT-2: the first 32B parameter globally decentralized Reinforcement Learning training run where anyone can permissionlessly contribute their heterogeneous compute resources.
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OpenPipe/ART
Agent Reinforcement Trainer: train multi-step agents for real-world tasks using GRPO. Give your agents on-the-job training. Reinforcement learning for Qwen3.6, GPT-OSS, Llama, and more!
OpenPipe/ART
Agent Reinforcement Trainer: train multi-step agents for real-world tasks using GRPO. Give your agents on-the-job training. Reinforcement learning for Qwen3.6, GPT-OSS, Llama, and more!
OpenPipe/ART
Agent Reinforcement Trainer: train multi-step agents for real-world tasks using GRPO. Give your agents on-the-job training. Reinforcement learning for Qwen3.6, GPT-OSS, Llama, and more!
MacPaw Partners with Liquid AI to Bring On-Device AI to Millions of Mac Users — Blog
MacPaw partners with Liquid AI to bring private, fast, on-device AI to millions of Mac users, starting with the Eney assistant for macOS.
Training AI Agents with RL | Unsloth Documentation
Learn how to train AI agents for real-world tasks using Reinforcement Learning (RL).
tiles.run/tiles
Tiles is a local-first private AI assistant. Powered by on device models and ATproto.
tiles.run/tiles
Tiles is a local-first private AI assistant. Powered by on device models and ATproto.
All Infrastructure is AI Infrastructure: Keynote by Mirantis CTO
Organizations must embrace infrastructure built for AI: composable, observable, scalable, and ready to deliver performance across all environments.
