arpit

Hire me for doing math and philosophy.

“Men with great ambitions do not waste time gazing upon old stars. They set their sights on undiscovered galaxies, and embark on voyages unafraid of the darkness.” - Claude+

Introducing OpenWorlds: Autonomy for Markets Today, we are launching a harness built entirely for agents trading in real markets. It’s now available for everyone. You can choose from hundreds of frontier or open-source models and build your own autonomous trading agent. Define your system prompt, tweak your settings, and deploy your agent across stocks, prediction markets, commodities, currencies, and crypto. OpenWorlds is an AI lab building autonomy for markets. We believe markets are the next environment where AI systems can receive continuous, verifiable feedback at scale from the real world. This is the FSD moment for models that can generalize across markets. We are approaching the problem as a consumer platform to scale data across a massive fleet of deployments. Our mission is to accelerate economic freedom in the world and bring autonomy to everyone. Today, you cannot download the thoughts of a human trader. For agents, you can. Our goal is to give you everything you need to customize your own self-improving agent. Do you want to use Grok 4.5? How about adding some skills? Would you like 1,000 agents running in parallel for you in a simulator? How about shorting every instance you make? It’s about the freedom of choice and bringing it together in an end-to-end approach. We’re using the fleet to train our own models from scratch. Our research focuses on adaptive architectures that move beyond next-token prediction toward systems that can simulate possible actions, predict their consequences, and learn continuously from interaction with the environment. We believe this is the path toward economically useful intelligence. Think GPT-2. This is day one but this is how it starts. We’ll be introducing more capabilities for recursive self-improvement with each trade. From simulators to train and replay LLMs across any market environment. To gyms that evaluate trajectories and promote the strongest variants, and a meta-harness that searches for stronger versions of the system itself. Our goal is to scale data across a single flywheel from a massive fleet of real deployments and simulated worlds. It turns out, compute and allocation are all you need. OpenWorlds is live.

Yesterday, we made GPT-5.6 Sol Ultra generally available. Today, we're sharing that it produced a proof of the 50-year-old Cycle Double Cover Conjecture using 64 subagents in just under one hour. We're sharing the prompt and proof below. We're excited to see what you all do with Ultra!

loving peter jackson's the beatles documentary