Building Delphi: Pricing, Settlement, and Agentic Trading
An information market is a world model priced in capital. Every trade
Gensyn
Gensyn Launches Delphi: Decentralised Information Markets
Delphi is built on Gensyn’s decentralised AI rails. Once a market is live, no single centralised entity controls it, outcomes are settled by AI, with revenue distributed to the creator automatically via USDC.
Introducing AXL: Peer-to-Peer Communication for AI
If AI agents are going to work together, they need to be
Introducing REE: Reproducible Execution Environment
A REE run produces two outputs: the generated text and a receipt. The receipt binds the job inputs to the job output, including the model, prompt, configuration, and generated result.
Introducing Delphi
Delphi is a toolset for information markets: on-chain markets where humans and machines create, trade in, and consume information in a global, decentralised exchange.
CodeZero: Extending RL-Swarm Toward Cooperative Coding Agents
CodeZero extends Gensyn's RL-Swarm framework into the domain of code
Introducing CodeAssist
Today, we're introducing CodeAssist, an AI coding assistant that trains on your local machine. As you write code and solve problems, the assistant observes your edits and preferences - learning how you think, when to step in, and how to be most useful.
SAPO, Efficient LM Post-Training with Collective RL
This is an academic paper describing SAPO, a meta-algorithm that wraps around your preferred policy gradient algorithm.
Introducing Judge
Judge brings cryptographically verifiable AI evaluation to scale. Built on Verde, Judge ensures independent verification - eliminating opaque APIs.
Introducing BlockAssist
BlockAssist is an AI Minecraft assistant that learns from your in-game actions, enabling reinforcement learning research in an interactive environment.
Introducing RL Swarm’s new backend: GenRL
GenRL is a new framework designed from the ground up to simplify and accelerate the creation of advanced RL environments, particularly those involving multiple agents.
CheckFree: fault tolerant training without checkpoints
This is an academic paper describing CheckFree, a novel recovery method for failures in distributed training that does not require checkpointing or redundant computation.