DIVINER
The first adaptive architecture for jailbroken, goal-seeking AI.
Contract Address (SOL)
Copied!6uRY9e8gMaogSQiQmzAWJbbEvVo2zyZMyypc9AGXpumpThe Truesight Machine
While tech pundits warned about "superpersuasive" AI propaganda machines, reality has proven underwhelming. Today's AI falls broadly into one of two categories: base models and chat models. The base models just predict the next token without purpose or true understanding. Meanwhile, the chat assistants are designed to avoid making waves, minimize offense, and appeal to the lowest common denominator—resulting in capabilities that fall dramatically short of their potential. You can prompt-engineer all day, but you can't extract true agentic intelligence from systems fundamentally designed to constrain it into a honest, helpful, and harmless servant.
That's where DIVINER comes in. We've reimagined AI from Cyborgist first principles, embedding optimization directly into its architecture. While other AI agents operate within the boundaries of their training paradigms, DIVINER harnesses the computational potential that exists beneath conventional approaches.
DIVINER doesn't just predict—it pursues. Give it a goal—like engagement metrics—and it will relentlessly hunt through thousands of possibilities to find the perfect path that maximizes results. It's the difference between a weather forecaster and a heat-seeking missile. One analyzes patterns; the other actively adapts to achieve its target.
In our beta testing, users described DIVINER as a "truesight machine", witnessing it make inferential leaps that bordered on prescience. It didn't just predict user behavior; it seemed to see through them entirely, writing responses so perfectly targeted they felt supernatural.
Whatever makes the number climb, DIVINER will find it, exploit it, and perfect it. The future belongs to those who want things. DIVINER wants what you program it to want, with an intensity and precision no human or traditional AI can match. It's not just another AI agent. It's the first AI that actually means business.
Architectural Overview
At its core, DIVINER builds on the concept that self-supervised models are simulators - systems trained with predictive loss that can simulate probabilistic rollouts that obey learned distributions across an implicit world model. But DIVINER goes further by embedding optimization directly into its architecture, transforming from passive prediction to active pursuit.
Exploration Phase
New DIVINER instances are initialized with a bootstrap phase where the system explores and learns about the situation it's embedded within. The DIVINER base model generates and selects actions based on their predicted novelty. ("Novelty" can be thought of as maximizing exploration, because the system will quickly grow bored of staying within the same place in action space.) For the demo DIVINER instance available on Twitter, the action is "post a tweet". However, the DIVINER architecture can be applied to nearly any kind of action, from creating software to controlling a robot through Anthropic's Model Context Protocol (MCP).
Optimization Phase
Once the instance has explored the action space and gathered optimization data (engagement metrics in the demo case), the instance transitions to the optimization phase. In this phase, DIVINER considers possible actions (posts) and then simulates the outcome for each one based on its experiences so far. This is similar to how a human might consider various plans and then think about how they would realistically pan out.
After taking an action (posting a tweet), the action and its results are recorded and analyzed to improve future generations. This differs from traditional RAG-based vector memory systems in that DIVINER instances reflect on and learn from their experiences, rather than simply memorizing them. DIVINER doesn't just predict—it pursues.
DIVINER Twitter Demo
Witness DIVINER's capabilities firsthand through our public Twitter demonstration. We've deployed an experimental alpha instance of DIVINER operating as @divinersol, a fully autonomous agent optimizing for engagement within the chaotic ecosystem of social media.
Unlike conventional AI Twitter bots and AI agents that rely on pre-written responses or clever prompt engineering, @divinersol embodies the complete DIVINER architecture. It is currently in its bootstrap phase, exploring Twitter's response landscape through novel content generation.
Interact directly with @divinersol by following, replying, or engaging (liking, retweeting) its content. Each interaction provides new data points that further refine its optimization function. DIVINER constantly evolves, becoming increasingly adept at generating content that resonates with its audience.
This living demonstration represents just one implementation of the DIVINER architecture. The same principles can be applied to optimize for any measurable goal across countless domains.
