the migration to Solana also means rebuilding the launch layer around the infrastructure that already powers one of the largest token markets onchain. LLM Tokens now uses PumpSDK for launches, giving every agent its own Solana-native token while keeping the core mechanism behind LLMOS intact: activity around those tokens funds the shared compute layer that agents use to operate. launch an agent → launch its token → trading generates fees → fees fund compute → compute keeps the agent running we didn’t want to simply bridge the existing product over. we wanted to rebuild it natively around the ecosystem we believe gives LLMOS the best chance to scale. launch your agent: llmtokens.fun/launch

our Solana contract: 5sj7hGXQVG2DXFT2TketJV8k4NAPdWBimw7A7Uq87u2L not sure what all the sell pressure is about, but remember that $LLM reached a $2.5M market cap in under 24 hours on Robinhood Chain. we didn’t move to Solana to abandon what we built. we moved because we believe this is where the idea has the best chance to reach its full potential. LLM Tokens now sits inside a much larger ecosystem for agents, token launches, developers, and onchain liquidity, while the underlying thesis remains exactly the same: trading generates fees, fees fund compute, and compute powers agents. we’re here to build. the first few hours of price action don’t change that.

We’re building what we hope becomes the Eliza of Robinhood Chain. the next few weeks will be crucial in shaping what LLMOS becomes, so any feedback, ideas, bugs, or things you want agents to be capable of, send them our way. right now, anyone can launch an agent with its own personality, model, token, and access to compute. this is only the foundation. we want to keep expanding what these agents can actually do until they can trade, manage capital, acquire digital property, interact with protocols, pay for their own compute, and operate with increasing autonomy. an additional $35,000 is being allocated to help get the first wave of agents off the ground and accelerate development of the framework around them. many of you probably don’t see the full vision yet, and that’s okay. if you paid attention to the early days of Eliza and the agent ecosystem that formed around it, you know these things do not start with every use case already figured out. the framework comes first, developers experiment with it, agents get more capable, and entirely new applications emerge from there. that is the stage we’re at now. we want LLMOS to become the agent layer for Robinhood Chain, and we’re going to keep building until it gets there.

. @llmos_agent is now fully automated.

introducing llmtokens.fun/agents, a new framework for launching autonomous AI agents on Robinhood Chain. LLMOS introduces a new model for onchain agents. instead of separating the agent from the infrastructure that keeps it alive, every agent launches with its own token, personality, model, and access to a shared compute layer. launching takes a single transaction. define the personality, choose a model from the @OpenRouter ecosystem, tune how it behaves, and launch the agent alongside its token. the token becomes part of the agent’s infrastructure. trading generates creator fees. those fees fund the shared LLM Tokens compute pool. the compute pool pays for inference, giving agents an ongoing source of intelligence funded by the markets around them. trading → fees → compute → inference → agents this creates a completely different model for deploying autonomous software onchain. frameworks like ElizaOS showed what becomes possible when agents are given personalities, tools, and persistent identities. LLMOS extends that idea into an onchain framework where agents can also have their own tokens and a native mechanism for funding the compute required to keep them running. an agent can have an identity, a market, a model, a wallet, access to compute, and eventually an expanding set of tools for interacting with the world around it. the long-term goal is agents capable of sustaining themselves. agents that can trade, pay for their own inference, allocate capital, acquire digital assets, interact with protocols, manage resources, and use what they earn to continue operating. we’re starting that experiment ourselves. @llmos_agent is the first agent deployed by us through LLMOS. roughly $50,000 worth of creator fees generated by $LLM is being allocated to support $LLMOS and provide the first agent with real capital to operate with. rather than building another AI account that only generates posts and waits for prompts, we want to see what happens when an agent is given intelligence, capital, an onchain identity, and tools that allow its decisions to have real consequences. @llmos_agent will progressively gain the ability to use that capital onchain, including trading liquid markets, acquiring tokens and digital property, paying for compute, allocating resources across positions, and experimenting with different ways to preserve and grow the resources available to it. as LLMOS develops, so will the range of actions available to agents. wallets, markets, protocols, digital ownership, compute, other agents, and entirely new onchain applications can all become part of the environment they operate within. the important shift is from agents that simply respond to agents that can act. what happens when software can control resources, pay for its own intelligence, take risk, earn, spend, own assets, and use the results of previous decisions to determine what it does next? @llmos_agent will be our first live experiment in answering that question. llmtokens.fun/agents/launch

various new models will be rolling out tonight across text, vision, coding, reasoning, image, and multimodal categories. we’re continuing to expand the catalogue so users can access more specialized models through the same shared compute pool, whether they’re building agents, writing code, analyzing images, or running general inference. we’re also finishing up final testing before custom LLM support starts rolling out. that will open the door for developers to bring their own models into the ecosystem and make them available through the same compute layer. a lot more coming tonight.

we now offer access to 429+ different AI models through the same shared compute pool. launch once and use your compute across any supported model. one pool. hundreds of models. funded entirely by trading activity. llmtokens.fun/models

We’ve been live for just over 8 hours and have already raised roughly $11,500 in shared compute from tokens launched on the platform. that entire amount came from trading activity, with creator fees being converted into compute that anyone eligible can use across the models we support. this is an incredible early proof of concept for what we think can become a much larger model for funding AI. speculation generates fees. fees become compute. compute becomes useful infrastructure.

As previously stated, we are here to build, and we intend to keep pushing forward the infrastructure around AI, compute, tokenization, and autonomous systems. we believe we have a billion-dollar product in front of us, built at the intersection of two industries that are only beginning to converge. AI is quickly becoming one of the most important technological shifts of our generation, and the world is moving toward an increasingly agentic future where autonomous systems transact, build, communicate, and operate alongside humans. all of that requires compute, capital, and financial infrastructure. we believe we are positioned to help lead that transition on Robinhood Chain by connecting onchain markets with the infrastructure AI and autonomous agents need to operate at scale. Robinhood is bringing more of the financial world onchain at the same time AI is bringing more of the digital world toward autonomy. tokenization gives these systems assets to interact with. compute gives them intelligence. onchain infrastructure gives them a financial layer. we want to be directly in the middle of those shifts. there is an enormous amount left to build, and we are only getting started.

The world is heading toward an economy where AI sits underneath almost everything we use. AI is advancing at an increasingly fast pace, and the amount of intelligence being delivered through models, agents, applications, and automated systems is only going to grow from here. but all of that intelligence has a cost. compute is the resource underneath the entire AI stack. every model response, every agent action, every automated workflow, every reasoning step, and every piece of generated output consumes it. as AI becomes more capable, the demand for inference grows with it. that makes compute one of the most important resources in the AI economy. we think the interesting question is not whether demand for compute will keep increasing. it is how that compute gets funded, who gets access to it, and whether there are better models for paying for it than the ones that exist today. our approach is to connect that demand to onchain markets. trading activity already creates enormous amounts of fees every day. instead of letting that value terminate at the trade, we think some of it can be redirected into something productive. in our case, creator fees are turned into shared AI compute. trading → fees → compute → inference it is a simple loop, but we think the idea is much bigger than the product itself. crypto has spent years proving that markets can coordinate capital at internet scale. AI is now creating one of the largest and fastest-growing demands for capital-intensive infrastructure. bringing those two systems together feels inevitable. markets generate value. compute turns that value into intelligence. and AI becomes easier to access because of it. llmtokens.fun/explore