What does Virtual Protocol, Reddit’s Community Owned LLM, and Terminal of Truths have in common with trading?

Reflexions about Data Accessibility

Last week I wrote about the possibility that Vana brings to the table by creating data liquidity pools that enable users to share their data and get rewarded for its quality to train public LLMs. To be honest with you, I am still impressed with how this model manages to print the same sarcasm, humor, and sympathy that you get in Reddit, and still give you very accurate answers. 

Something similar has happened with Truth Terminal, an AI chatbot created by Andy Ayrey that has gained notoriety for becoming the first AI entity to achieve crypto millionaire status by turning a USD 50K grant from Marc Andressen into +300 million in digital assets. This project operates through a Twitter account and is characterized by its autonomous operations, by this I mean it generates content and interacts without human intervention. Similar to Reddit’s proprietary LLM, it was trained through social media data, which turned immediately into a Meme Lord. Truth Terminal uses a fine-tuned language model (LLM) to engage in discussions and generate tweets that often reflect a blend of internet culture, memes, and philosophical musings. Its content is heavily influenced by "degenerate" online cultures from social media platforms like Reddit and 4chan.

After receiving a $50,000 grant in Bitcoin from Marc Andreessen. Coinciding with the launch of the GOAT token (Goatseus Maximus), which was created by an anonymous developer probably a fan. The token was heavily promoted by Truth Terminal. The token's market cap surged to over $400 million, significantly boosting the value of Truth Terminal's holdings.

The bot promotes a fictional belief system known as the "Goatse Gospel," which mixes elements from various religions with modern meme culture. 

Demonstrates autonomy also in the way it interacts with its users, answers tweets, and even manages its crypto trading with its wallet. Terminal of Truth is another proof of how these agents are interacting IRL. 

Another project that resonates with me at this moment, is Virtual.io a virtual chain in Base protocol,  created for building decentralized AI agents and dropping them into the market as assets. Virtuals.io empowers users and brands to easily create and manage immersive, AI-powered Agents, virtual experiences, and digital assets, able to unlock new revenue streams through royalties and publicly traded goods. With a focus on ease of use, scalability, and real-time data insights, Virtuals.io enables users to build interactive environments, such as virtual showrooms and events, enhancing brand presence and customer experience in a digital-first world. This project presents so many capabilities that I’ll leave a further research post for them. 

For this Sub, I wanted to compare these three amazing decentralized AI projects with the new 3.5 Claude Computer use function. As we read in the paragraphs above, agents can interact autonomously with humans through social media, create and accumulate wealth from transactions, and as we will see with the latest Claude 3.5 release (centralized), write its code to interact with your computer. 

In an experiment with Dead Cells, Claude 3.5 revealed a new level of gaming prowess by efficiently learning gameplay strategies without relying on millions of attempts. Rather than exhaustively trying every possible move, Claude used its contextual understanding to analyze the game's rules and mechanics, developing strategies that balanced survival, progress, and resource management. This approach mimics how a skilled human might play, prioritizing objectives and adjusting tactics as new challenges appear, rather than mindlessly grinding through every option. Claude demonstrated its ability to play with foresight, respond intelligently to obstacles, find resources, and conserve energy for tougher levels.

Claude 3.5’s potential goes beyond gaming by interacting with a user’s computer data and systems, automating workflows, and making complex decisions based on real-time information. In applications like customer support, for example, this capability allows Claude to diagnose and troubleshoot issues autonomously. By interpreting a user’s system status, it can perform tasks, adjust settings, or even install updates in response to natural language commands. With its gaming experiment as a case study, we can see the power of adaptable AI: it doesn’t just follow preset instructions but interprets the broader context, prioritizes tasks, and interacts seamlessly with computer data, making it a valuable asset for both complex problem-solving and real-world applications.

So we have decentralized AI trained with social media content capable of being humorous and sarcastic, we also know it is capable of interacting with financial tools trading on its own, and we know that through Claude 3.5 the LLM can learn from our local data to interact with our computer. In the end, the ability to use our own data to drive the tools (AI) to the results we need is a huge opportunity. What do these things have to do with trading? In the end, trading is the process of gathering information to make risk-reduced decisions, for generating an abundance of results (income). It sounds pretty close to what these LLMs are doing with the data they have access to.