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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.
Prime Intellect - The Open Stack for Self-Improving Agents
The compute and infrastructure platform for you to train, evaluate, and deploy your own agentic models.
Prime Intellect - The Open Stack for Self-Improving Agents
The compute and infrastructure platform for you to train, evaluate, and deploy your own agentic models.
Quickly Training Game-Playing Agents with Machine Learning
Posted by Leopold Haller and Hernan Moraldo, Software Engineers, Google Research In the last two decades, dramatic advances in compute and connecti...
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.
Intro to Psyche - Psyche
Psyche is a system that enables distributed training of transformer-based AI models over the internet, aiming to foster collaboration between untrusted parties to create state-of-the-art machine learning models. It leverages a peer-to-peer distributed network for communication and data sharing.
Training Agentic Reasoners — Will Brown, Prime Intellect
Frontier Pre-training at Microsoft AI | Microsoft AI
In June, we announced MAI-Thinking-1, a general-purpose foundation model post-trained on top of MAI-Base-1 with strong reasoning performance. We’ve recently completed pre-training our new state-of-the-art model, MAI-Base-2, an MoE foundation model with 80 billion active and 2.8 trillion total parameters.
Hyperspace — Decentralized AI Agent Network
Run an autonomous AI agent on the decentralized P2P network. Earn points, serve inference, and contribute to distributed ML research.
Prime Intellect on Twitter / X
Announcing our $130M Series A to build the Open Superintelligence StackLed by Radical Ventures, with NVIDIA, Intel Capital, Dell Capital, and existing investorsTrain, deploy, and continuously improve your own models using our stack.Own your intelligence. pic.twitter.com/BM31LfVUNQ— Prime Intellect (@PrimeIntellect) July 8, 2026
QVAC - Decentralized, Local AI in a Single API
QVAC is Tether’s answer to centralized AI, an entirely new paradigm where intelligence runs privately, locally, and without permission on any device. The era of Stable Intelligence has begun.
Buzz: share spare compute and run open models together over p2p networks.
How Block is using Buzz, MeshLLM, and iroh to turn the computers in a community into shared infrastructure for AI agents.
Memory Efficient RL | Unsloth Documentation
We're excited to introduce more efficient reinforcement learning (RL) in Unsloth with multiple algorithmic advancements:
Training AI Agents with RL | Unsloth Documentation
Learn how to train AI agents for real-world tasks using Reinforcement Learning (RL).
Towards infinite context windows: neural KV cache compaction | Base Labs
Working to advance and democratize open-source intelligence.
Hermes Agent — Open-Source AI Agent with Memory, Skills, and Cron
The open-source AI agent from Nous Research with persistent memory, reusable skills, tools, cron jobs, GitHub workflows, and multi-platform messaging.