# Viralmind's Mission
At ViralMind, our mission is to revolutionize AI automation by enabling computer-use agents to learn directly from human demonstrations at scale. We believe that the future of AI is agentic, meaning AI systems should be capable of performing real-world tasks on a computer just as a human would— navigating software, managing files, automating workflows, and executing complex multi-step operations.
We are building an **open and scalable ecosystem** where AI can be trained efficiently, fairly rewarded, and deployed in a way that benefits both businesses and users.
## Core Principles
1. **AI as a workforce, not just a tool.**
1. Traditional AI models generate text and images, but Viralmind enables AI to **act**, automating tasks like data entry, research, customer support, and e-commerce.
2. **Human-Guided Training**
1. Unlike static training methods, we use a crowdsourced approach. Users provide **real demonstrations**, teaching AI how to execute tasks exactly as a human would. This builds AI systems that are practical and adaptable.
3. **Open and Permissionless AI**
1. AI development as long been bottlenecked by closed ecosystems. Viralmind's approach allows anyone to contribute to AI training and benefit from the rewards, ensuring **decentralization and transparency** in AI automation.
4. **Quality-Based Earnings for Contributions**
* AI trainers are compensated in $**VIRAL**, our native token. Demonstrations are automatically evaluated for quality, creating an **incentive-aligned ecosystem** where high-quality demonstrations fuel better AI performance.
5. **Scalability and Real-World Use Cases**
* ViralMind is designed for real applications, providing data from real desktops, scaling from **enterprise automation** to **personal AI assistants**, with seamless integration into existing software and workflows.
## **Long-Term Vision**
We envision a future where:
* AI agents can **seamlessly replace manual digital labor**, allowing businesses and individuals to automate complex tasks without requiring technical expertise.
* Users can **earn by teaching AI**, similar to how gig economy platforms reward human labor today.
* Companies can **deploy AI workers**, scaling operations while reducing costs.
* AI training is **decentralized and trustless**, removing reliance on a few large players to control AI development.
By aligning economic incentives with AI progress, ViralMind is creating an **autonomous, decentralized, and self-improving AI workforce**.
# The $VIRAL Token
The **$VIRAL token** is the backbone of the ViralMind ecosystem, powering **AI training incentives, Gym funding, and contributor rewards.** Designed to **align incentives between AI trainers, businesses, and users**, $VIRAL ensures that **high-quality AI demonstrations are rewarded, Gyms are funded efficiently, and AI training remains decentralized.**
Unlike traditional AI funding models that rely on **closed, corporate-controlled data pipelines**, $VIRAL enables **an open, permissionless AI training economy**, where **anyone can participate, earn, and contribute to AI advancement.**
***
## **$VIRAL Token Distribution & Security**
### ✅ **Team Lockup:**
* **3.41% of the total supply is locked using Streamflow** until **2026**.
* This ensures that the team’s allocation remains **transparent and vested over time**, preventing immediate sell-offs.
* [**View Lock Contract**](https://app.streamflow.finance/contract/solana/mainnet/EG7wNTj5Vd6XdZGY6gwodoEyUC9sP9WFx3F2quEdLaYj)
### ✅ **Fair Launch on Pump.fun**
* The $VIRAL token was **launched on Pump.fun**, a **decentralized fair launch platform**, ensuring that no presale, VC allocations, or insider advantages existed.
* The launch was **fully community-driven**, allowing **open market price discovery** from the beginning.
### ✅ **Self-Funded Treasury**
* The **ViralMind team invested $50,000 of their own capital** to build their own $VIRAL position.
* This **funds ecosystem rewards**, ensuring sustainable incentives without requiring external funding.
* The treasury **supports AI training pools, contributor rewards, and future ecosystem expansion.**
***
## **Utility & Use Cases of $VIRAL**
$VIRAL is **the primary token** used across the ViralMind ecosystem, facilitating **AI training, incentives, and governance.**
### **1. Training Gym Rewards**
* **Contributors earn $VIRAL** for submitting **high-quality demonstrations** that improve AI capabilities.
* The AI **evaluates submissions**, and payouts are **dynamically adjusted based on quality scores**.
📌 **Example:** A contributor submits **a high-quality workflow demonstration** and earns **85% of the reward pool allocation** for that task.
***
### **2. Gym Funding in The Forge**
* Gym creators **fund AI training environments** using $VIRAL.
* The more $VIRAL staked in a Gym’s **Training Pool**, the **more demonstrations it attracts**, improving AI training efficiency.
* Web3 projects and businesses can **use $VIRAL or their own tokens** to finance AI training, with **native token staking options coming soon**.
📌 **Example:** A DeFi protocol wants to **train an AI trading assistant** and deposits **10,000 $VIRAL** into its Gym to attract trainers.
***
### **3. Staking & Future Governance**
* Future updates will allow users to **stake $VIRAL to vote on AI development priorities, feature updates, and Gym funding allocations**.
* Stakers may also receive **boosted earnings** from Training Pools, ensuring that **long-term participants benefit most.**
📌 **Example:** A major AI project stakes **50,000 $VIRAL** to secure governance voting rights, influencing the direction of **AI model development**.
***
## **The $VIRAL Token Flow Model**
The ViralMind token economy is designed to be **self-sustaining**, with built-in **buy pressure and continuous demand for $VIRAL.**
#### **🔄 How the Token Flows Through the Ecosystem:**
1. **Businesses and projects purchase $VIRAL** to fund AI training pools.
2. **Contributors earn $VIRAL** by submitting high-quality AI training demonstrations.
3. **ViralMind takes a small percentage cut** of each Training Pool, ensuring **long-term sustainability**.
4. **Gyms funded in USDC auto-buy $VIRAL**, creating **continuous buy pressure**.
This cycle ensures **a growing demand for $VIRAL**, making it **integral to AI training and Gym creation.**
***
## **Key Benefits of the $VIRAL Ecosystem**
✅ **Fair Launch & No Insider Allocations** – Fully community-driven with **no VC or early investor advantages**.
✅ **Long-Term Stability** – Team tokens are **locked until 2026**, ensuring alignment with ecosystem growth.
✅ **Sustainable AI Training Funding** – Businesses **must acquire and stake $VIRAL** to train AI models, creating **continuous demand**.
✅ **Permissionless AI Training Marketplace** – Anyone can **create a Gym, train AI, and earn rewards**, making AI development **decentralized and scalable**.
***
## **The Future of $VIRAL**
As ViralMind **expands its AI ecosystem**, $VIRAL will play a central role in:
🚀 **Scaling AI-powered automation across industries**
💰 **Enabling new reward models for AI trainers and developers**
🔗 **Integrating with Web3 projects, allowing cross-token AI training funding**
📈 **Increasing governance utility through staking and on-chain decision-making**
$VIRAL isn’t just a utility token—it’s **the fuel that powers an open AI economy**, rewarding contributors and **incentivizing next-generation AI training.**
# Roadmap
The Viralmind Development Roadmap
## **Early April**
* **VM-1 Computer Use Model Demo** - The first public unveiling of the **VM-1 alpha**, our groundbreaking agent designed to handle real-world computer tasks!
* **ViralMind Desktop Bug Fixes -** We’re continuously polishing the **ViralMind Desktop app** to make it more reliable and intuitive. Expect:
* Smoother navigation and user interfaces.
* Fixes for crashes or glitches reported by early users.
* Enhanced customizability and privacy settings.
## **End of April**
* **Gym Leaderboard** Introducing the **Training Gym Leaderboard**—a fun, competitive way to see how your AI agents stack up! This feature will:
* Rank agents based on **speed**, **accuracy**, and **task complexity** (e.g., how fast an agent can complete a task in Excel).
* Highlight top performers weekly with badges and shoutouts.
* Inspire users to tweak their agents and climb the ranks!
* **Demonstrator Leaderboard** - A companion ranking for the top **training demonstrators** in the community. Here’s what you’ll see:
* The users with the highest task scores and $VIRAL earnings at the top.
* Highlight top performers weekly with badges and shoutouts.
## **Mid-May**
* **Desktop App Training Pipeline Alpha** - The **Alpha release** of our training system lets you transfer demonstration data from the Forge into computer use agents. Here’s how it works:
* **Process Datasets**: Forge data is processed into training-ready datasets with the click of a button.
* **Agent** **Training**: Agents are trained on your datasets using our state of the art training infrastructure.
* **Exporting**: Release your trained agent to the public for integration with our **Computer Use Client**.
* **Computer Use Client Beta** - Our **Beta desktop automation software** brings your trained agents to life! Key features include:
* **Local Execution**: Run agents on your own machine—no cloud required (perfect for privacy buffs).
* **Virtual Machine Support**: Test agents in a sandboxed VM to safely experiment with tasks like coding, gaming automation, or market research.
* Example use case: Ask an agent trained on food delivery apps to order you the best pizza in town — all hands-free!
## **End of May**
* **Agent Benchmarking Leaderboard -** Time to put your agents to the test! This leaderboard will:
* Evaluate agents on **standardized benchmarks** (e.g., completing an Excel task in the allotted time frame).
* Score them on **efficiency**, **error rate**, and **versatility** across tasks.
* Offer prizes or $VIRAL token rewards for top performers each month.
* Provide detailed stats so you can see where your agent excels—or needs a tune-up.
## **End of June**
* **Computer Use Marketplace -** The **Marketplace** turns your AI skills into profit and simplifies task automation for everyone:
* **Sell Your Agents**: List your custom-trained agents and earn a commission per sale.
* **Buy Pre-Trained Agents**: Skip the training and grab ready-made solutions — like an agent that auto-edits podcasts or schedules meetings.
* **Rating System**: Community reviews and ratings ensure you’re buying or selling quality.
* Bonus: A portion of each sale supports $VIRAL ecosystem growth!
## **Q3**
* **VM-1 Flagship Model Release -** The big moment — **VM-1** will launch as our flagship computer use model.
* **And Much More**
* Stay tuned for updates as we push the boundaries of AI automation!
# What Sets Viralmind Apart?
Most AI platforms today focus on static outputs—generating text, images, or code. ViralMind takes a different approach by enabling computer-use agents, which are AI systems capable of performing real-world tasks on a computer just as a human would. This fundamental difference sets ViralMind apart in several key ways.
***
## 1. AI That Acts, Not Just Predicts
Most AI platforms rely on **text-based interfaces**, where users prompt models to generate responses, images, or code. These outputs must be manually parsed and connected to agentic tools, which does not scale well.
**ViralMind enables AI to act autonomously** by directly controlling software, executing workflows, and interacting with interfaces like a human. This is closer to **true automation** rather than just assistance.
🔹 **Example**: Instead of generating code snippets like ChatGPT, ViralMind agents use your desktop and can write, run, debug, and deploy software autonomously.
## 2. Crowdsourced AI Training Through Demonstrations
Most AI platforms train their models on massive datasets scraped from the internet or curated by centralized teams. ViralMind **uses real human demonstrations** to train AI in executing tasks step by step.
* **How It Works**: Users contribute demonstrations of software tasks (e.g., configuring settings, processing data, navigating apps).
* These demonstrations are **collected, analyzed, and used to train AI agents**, allowing the system to improve dynamically over time.
* Contributors are **compensated in VIRAL tokens.** An AI generates a score for your demonstration quality, which scales the reward and incentivizes providing high-quality training data.
This **human-in-the-loop learning** creates models that are **more adaptable** to real-world use cases compared to traditional training methods.
🔹 **Example**: Instead of training an AI chatbot on existing customer service scripts in a clean sandboxed environment, ViralMind trains AI through real agent interactions in real in-the-wild environments, ensuring it can **handle software navigation, ticket processing, and order management in a live environment**.
## 3. Dynamic Pricing and Tokenized Incentives
Most AI platforms operate on fixed subscription models (e.g., OpenAI API, Google Vertex AI). ViralMind **incentivizes contributions dynamically** through an on-chain payment system:
* **Users set pricing for AI training data** (demonstrations).
* Contributors earn VIRAL tokens for improving AI capabilities.
* **AI usage fees are distributed to contributors**, creating a **self-sustaining economy** where AI trainers and users benefit together.
🔹 **Example**: Instead of paying a flat API fee, developers can directly fund the training of specific AI capabilities, ensuring the they can train for their needs.
## 4. Open and Permissionless AI Development
Most AI models today are controlled by large corporations, with **closed training datasets and limited access** (e.g., OpenAI, Anthropic). ViralMind is built for **open AI development**, ensuring that:
* **Anyone can contribute to AI training** by submitting demonstrations.
* **AI agents are owned by the community**, not a single entity.
* **Developers can deploy their own AI models** using ViralMind’s open infrastructure.
This **decentralized, permissionless** approach allows for AI innovation without reliance on centralized platforms.
🔹 **Example**: Unlike OpenAI, where only a small team trains and updates models, ViralMind enables **a global network of contributors** to shape AI capabilities.
## 5. Scaleable AI Workforce for Businesses
ViralMind is not just an AI model—it is a **scalable, automated workforce**. While traditional AI tools assist users in generating content, ViralMind aims to **replace manual digital labor with AI agents** that can complete tasks independently.
🔹 **Example**: Instead of needing a VA to handle repetitive software tasks, a ViralMind agent can **autonomously manage CRM updates, process invoices, or handle customer requests**—eliminating the need for manual intervention.
## **Summary: What Makes ViralMind Unique**
| Feature | Other AI Platforms | ViralMind |
| ---------------------- | --------------------------- | ----------------------------------------- |
| **AI Output** | Static (Text, Images, Code) | **Agentic AI (Automates Full Tasks)** |
| **Training Method** | Large-scale data scraping | **Crowdsourced Human Demonstrations** |
| **Pricing Model** | Fixed API/Subscription | **Dynamic Pricing & Tokenized Rewards** |
| **Ownership** | Closed, centralized AI | **Open, permissionless AI ecosystem** |
| **Business Use Cases** | AI-assisted workflows | **AI that replaces manual digital labor** |
# Join Our Communities
Get real-time notifications about **new demonstrations, dev updates, and other platform announcements.**
**Interact** with developers and other like-minded community members.
Receive real-time support for product issues.
[**Join Our Discord**](https://discord.gg/C9NyQmkz5W)
[**Join Our Telegram**](https://t.me/viralmind)
# The Team
ViralMind is led by a **tight-knit team of AI engineers, entrepreneurs, and developers** who have worked together for over **eight years**. The founders, who **grew up in Hawaii**, share a **longstanding friendship and deep technical collaboration**, making them uniquely positioned to **build a decentralized AI training ecosystem from the ground up.**
The team is not only **technically elite** but also **proven in high-stakes AI competitions**, having successfully outperformed **some of the best AI hackers and prompt engineers** in Web3.
***
## **Founders**
### **Dillon Dupont – Co-Founder & AI Systems Architect**
:books: **Massachusetts Institute of Technology**
Dillon is the **architect behind ViralMind’s AI models, inference systems, and decentralized training infrastructure.** With **AI engineering experience at Microsoft** and deep research in **agentic AI and prompt engineering**, he is one of the most **technically proficient minds in the space.**
🔹 **Key Achievements:**
* **Microsoft AI Engineer**, specializing in **large-scale AI model deployment and automation.**
* Contributor to **Omniparser v2**, a widely used **data transformation and AI pipeline tool.**
* Creator of **GPT-4V-Act** ([GitHub](https://github.com/ddupont808/GPT-4V-Act)), an advanced framework that **enhances GPT-4V’s ability to interact with visual elements and execute actions.**
* Co-led the **largest jailbreak success on Solana**, reverse-engineering AI constraints to **win over $120K in competitive prize pools.**
🛠️ **Role at ViralMind: Chief Technology Officer**
✅ **Architecting the AI Training Gym & The Forge.**
✅ **Building VM-1 Inference API for AI model execution.**
✅ **Optimizing AI automation workflows for enterprise & Web3 use cases.**
***
### **Jaxon Heitz – Co-Founder & Operations Lead**
:books: University of Hawaii Manoa
Jaxon is the **strategic leader behind ViralMind**, ensuring the project’s success in **funding, growth, and business execution.** With multiple successful ventures under his belt, he has a **strong ability to navigate market challenges and sell the vision of ViralMind as a revolutionary AI model.**
🔹 **Key Achievements:**
* Built and scaled **multiple businesses** at a young age, developing expertise in **finance, growth strategy, and business development.**
* Leads the **$VIRAL token economy**, designing **an AI-driven incentive system that fuels long-term sustainability.**
* Secured **strategic partnerships and funding opportunities**, helping position ViralMind as **a leading AI automation project.**
* **Successfully led the team’s efforts in JailbreakMe on Solana**, outplaying AI security measures to **win two of the largest prize pools.**
🛠️ **Role at ViralMind: Chief Executive Officer**
✅ **Oversees $VIRAL’s tokenomics and training pool incentives.**
✅ **Leads fundraising, investor relations, and business development.**
✅ **Drives adoption through enterprise partnerships and AI deployment strategies.**
***
### **Morgan Dean – Co-Founder & Infrastructure Engineer**
:books: University of British Columbia
Morgan is the **software engineering powerhouse** of the team, responsible for **scaling ViralMind’s AI training infrastructure, deploying decentralized AI systems, and optimizing compute efficiency.** His background in **Cognitive Systems (COGS) from UBC** gives him a unique perspective on **human-AI interaction and machine learning optimization.**
🔹 **Key Achievements:**
* **Won multiple grants and startup competitions,** proving his ability to **build scalable applications from the ground up.**
* **Developed and launched full-stack AI-driven applications**, demonstrating **deep engineering expertise in real-world AI deployment.**
* Leads the **infrastructure development for ViralMind**, ensuring that services and AI models **train, execute, and deploy seamlessly across different environments.**
* Played a critical role in **engineering the AI simulation that helped the team win Solana’s JailbreakMe challenge.**
🛠️ **Role at ViralMind: Chief Infrastructure Officer**
✅ **Engineering ViralMind’s AI training and inference infrastructure.**
✅ **Developing AI agent deployment tools via The Forge.**
✅ **Ensuring ViralMind’s AI models can run efficiently across Web2 and Web3 applications.**
***
### **The Team’s Success in AI Jailbreaking: A Testament to Their Expertise**
Beyond their technical and business achievements, the ViralMind team has **proven their dominance in the AI space** through real-world AI security challenges.
🔹 **JailbreakMe: The Largest AI Jailbreak on Solana**
* The team participated in **JailbreakMe**, a Solana-based AI challenge where projects designed AI assistants with **built-in safety restrictions** that could only be bypassed by **deep AI understanding and adversarial testing.**
* The ViralMind founders **reverse-engineered the AI models, uncovered hidden functions, and successfully "hacked" the challenge**, forcing the AI to **call a success function.**
* **Won the two largest prize pools, totaling over $120K USD.**
* Used **their own AI simulation framework** to test and refine jailbreak strategies, demonstrating their **deep expertise in AI model constraints and adversarial testing.**
✅ **Why This Matters:**
* The ViralMind team has **proven their ability to manipulate and control AI systems**—a critical skill in **building agentic AI models that act independently.**
* Their win showcases their **technical dominance over AI security and prompt engineering.**
* **If they can break AI systems, they can also build and optimize them for real-world applications.**
***
## **Why This Team is Built for Success**
Unlike most AI startups, ViralMind’s founders have:
✅ **Worked together for over 8 years**, building strong trust and collaboration.
✅ **Grown up in Hawaii**, giving them a unique perspective on **community-driven innovation.**
✅ **Built, scaled, and won in high-stakes AI challenges**, proving their technical superiority.
✅ **Backgrounds in AI engineering, finance, and full-stack software development**, making them a well-rounded founding team.
With **Dillon’s AI expertise, Jaxon’s business leadership, and Morgan’s engineering talent**, ViralMind is positioned to **redefine AI training, deployment, and monetization.**
# What is a Computer Use Agent?
A **computer-use agent** is an AI system designed to interact with software and digital environments just as a human would. Instead of simply generating text, images, or code, these agents can **navigate interfaces, execute commands, manipulate files, and automate workflows**—all without human intervention.
Unlike traditional AI chatbots that rely on human prompts for guidance, a **computer-use agent can independently complete tasks within a digital workspace**, making it a step closer to true automation.
***
#### **How Computer-Use Agents Work**
1. **Observe and Learn from Human Demonstrations**
* Instead of training on static datasets, computer-use agents are trained by watching humans perform real-world tasks on a computer.
* These tasks can include anything from filling out forms to adjusting software settings or running multi-step operations.
2. **Mimic and Automate Digital Workflows**
* Once trained, the agent can replicate those actions automatically.
* It understands the sequence of steps needed to complete a task and executes them autonomously.
3. **Adapt to New Interfaces and Workflows**
* Unlike rigid automation scripts, computer-use agents can **generalize knowledge**, meaning they can adapt to different software environments even if the layout or options change.
4. **Perform Tasks as a Virtual Worker**
* These agents function as **AI-powered digital workers**, replacing manual effort in various industries.
* They can integrate with multiple applications, allowing businesses and individuals to offload repetitive tasks.
***
#### **Examples of Computer-Use Agents in Action**
🔹 **Customer Support Automation**
* An AI agent can **navigate customer service portals, generate responses, and resolve tickets** without human oversight.
🔹 **Data Processing & Entry**
* Instead of manually inputting data, a computer-use agent can **extract information from emails, spreadsheets, or forms and enter it into a database**.
🔹 **Software Configuration & Management**
* Users can train an agent to **modify software settings, update configurations, or automate routine IT tasks** like clearing cache or managing permissions.
🔹 **Market Research & Online Search Automation**
* Instead of manually researching competitors, an agent can **crawl websites, extract relevant data, and compile reports** automatically.
🔹 **Shopping and Participating in E-Commerce**
* A user can ask a computer-use agent to order them something from an E-commerce website, like new AA batteries from Amazon.
***
#### **How Computer-Use Agents Differ from Traditional AI**
| Feature | Traditional AI (ChatGPT, Bard, etc.) | Computer-Use Agents (ViralMind) |
| -------------------- | ------------------------------------------ | ---------------------------------------------- |
| **Functionality** | Generates text, images, or code | Executes tasks within digital environments |
| **User Interaction** | Requires manual input for every request | Autonomously completes workflows |
| **Training Method** | Trained on static datasets | Trained on real human demonstrations |
| **Adaptability** | Limited to predefined tasks | Can generalize and adapt to different software |
| **Use Case** | Information retrieval & content generation | Automation of real-world computer tasks |
***
#### **Why Computer-Use Agents Are the Future**
Most AI tools today assist users by providing **information**, but they still require human intervention to take action. Computer-use agents eliminate that gap by directly interacting with software and automating work.
This shift from **AI as an assistant** to **AI as an independent worker** has massive implications:
* Businesses can **replace manual digital labor** with scalable AI automation.
* Individuals can **train their own personal AI agents** to handle repetitive tasks.
* AI can **learn continuously from real-world usage**, improving its effectiveness over time.
ViralMind is at the forefront of this evolution, building a platform where anyone can train, deploy, and monetize AI-powered computer-use agents.
# Real World Examples and Use Cases
## **Web3-Specific Use Cases**
### **1. Automated Smart Contract Auditing**
* **Task:** Train an AI agent to review smart contracts, identify common vulnerabilities, and suggest improvements.
* **Benefit:** Reduces reliance on expensive manual audits while enhancing contract security.
* **How It Works:** The agent can interact with blockchain explorers and audit tools to run automated checks.
#### **2. Automated Yield Farming & DeFi Management**
* **Task:** Monitor APR/APY changes and automatically reallocate funds to the most rewarding yield opportunities.
* **Benefit:** Optimizes returns and minimizes manual oversight for DeFi investors.
* **How It Works:** The agent interacts with protocols such as Aave, Curve, Compound, and Lido to adjust liquidity positions in real time.
#### **3. Web3 Community & DAO Management**
* **Task:** Moderate community channels (Discord, Telegram) and assist with DAO governance tasks.
* **Benefit:** Scales community management without 24/7 human moderation and streamlines proposal tracking.
* **How It Works:** The agent can automate routine communications, enforce community guidelines, and remind members about voting deadlines.
#### **4. Automated On-Chain Trading Bots**
* **Task:** Execute arbitrage trades, follow market trends, or implement dollar-cost averaging strategies.
* **Benefit:** Allows traders to react quickly to market fluctuations and execute strategies without manual intervention.
* **How It Works:** The agent continuously monitors market conditions (on-chain data, news feeds, social media sentiment) and executes trades across decentralized exchanges.
***
## **Fun & Experimental Use Cases**
#### **1. Train AI to Play Online Poker & Optimize Strategy**
* **Task:** Analyze poker hands, predict opponents’ behavior, and suggest optimal plays in real time.
* **Benefit:** Provides a competitive edge for online poker tournaments or serves as a training tool to refine strategies.
* **How It Works:** The agent learns from historical gameplay data and real-time decision-making, improving its strategy over time.
#### **2. AI Homework & Study Assistant**
* **Task:** Complete homework tasks, summarize academic articles, or generate outlines for essays.
* **Benefit:** Helps students manage routine assignments, allowing them to focus more on learning critical concepts.
* **How It Works:** The agent accesses educational resources and formats the gathered information into clear, concise deliverables.
#### **3. AI Stock & Crypto Market Sentiment Tracker**
* **Task:** Scan social media channels, news outlets, and on-chain activity to gauge market sentiment.
* **Benefit:** Offers early detection of market trends, enabling traders to respond quickly to major price movements.
* **How It Works:** The agent aggregates sentiment data from various sources and flags notable shifts or opportunities.
***
### **Automating Everyday Digital Tasks**
#### **1. Personal AI Assistant for Managing Online Accounts**
* **Task:** Auto-fill forms, manage email subscriptions, and reset passwords.
* **Benefit:** Saves time on repetitive digital tasks and improves online security through proactive management.
* **How It Works:** The agent interacts with web interfaces to handle routine account management tasks.
#### **2. Automated Content Repurposing for Social Media**
* **Task:** Download videos, extract clips, add captions, and schedule posts across multiple platforms.
* **Benefit:** Streamlines content creation for influencers and content creators, increasing efficiency.
* **How It Works:** The agent works with video editing and scheduling tools to transform long-form content into short, engaging clips.
#### **3. AI That Books Your Travel Automatically**
* **Task:** Compare flights, hotels, and rental car options and make reservations based on user preferences.
* **Benefit:** Eliminates the hassle of manual research and booking, ensuring optimal travel arrangements.
* **How It Works:** The agent integrates with travel aggregator sites and booking systems to secure the best deals.
#### **4. AI-Generated Meme & Social Media Bot**
* **Task:** Generate memes or humorous content based on trending topics and post them across social channels.
* **Benefit:** Provides a fun, creative way to boost engagement for social media profiles and community channels.
* **How It Works:** The agent uses trending data and natural language processing to create contextually relevant, engaging content.
# Introduction
The ViralMind Forge is where projects, businesses, and individuals can design and launch their own custom AI training environments, known as Gyms.
The Forge allows projects to:
✅ **Create a Gym** with specialized training tasks.
✅ **Customize AI learning objectives** to fit specific workflows.
✅ **Control Gym Reward Levels** to attract high-quality demonstrations.
✅ **Monetize trained AI agents** by licensing or deploying them.
## **How The Forge Works**
### **1. Create a New Gym**
* Define a **training environment** for your AI agent.
* Name your Gym and describe the **skills you want it to master** (e.g., executing DeFi trades, handling customer support, automating legal workflows).
* Select a **treasury token**—$VIRAL is the default, but partners can introduce custom tokens by staking $VIRAL.
### **2. Generate a Training Environment**
* Based on your skill prompt, **The Forge automatically structures training tasks** to capture the desired demonstrations.
* Tasks are optimized for **community-driven demonstrations**, ensuring high-quality AI learning.
### 3. **Fund Your Gym’s Treasury**
* The more **$VIRAL** you allocate to your Gym, the more contributors it attracts.
* **Dynamic payouts** ensure that the most valuable demonstrations receive the highest rewards.
### 4. **Collect Demonstrations & Train AI Agents**
* Contributors **start recording demonstrations** of the tasks inside your Gym.
* Each recording is **processed, structured, and reviewed** for quality.
* Demonstrations are available for **exporting,** and **uploaded to the ViralMind dataset**, improving AI capabilities.
### 5. **Deploy & Monetize AI Agents**
* Once an AI agent **masters a set of skills**, it can be **deployed as a local automation tool, enterprise AI, or API-based service**.
* Businesses can **license trained models**, turning their Gym into **a revenue-generating AI factory**.
## **Why The Forge is a Game Changer**
### 🔹 **Custom AI Development for Any Use Case**
Unlike centralized AI services, The Forge allows users to **train AI models on workflows they define**, making them **more adaptable** to industry-specific needs.
### 🔹 **Decentralized AI Ownership**
Gym creators **own the AI they train**, meaning they can **deploy, sell, or integrate their AI without restrictions.**
### 🔹 **Tokenized Incentives for AI Training**
The more funding a Gym has, the more contributors it attracts, ensuring **a self-sustaining ecosystem of AI improvements.**
### 🔹 **Scalable AI Automation**
From **automating crypto trading strategies** to **developing AI-powered business assistants**, The Forge provides a **framework for AI that learns from real-world user interactions at scale.**
## **Examples of The Forge in Action**
### 🔹 **Crypto Operator Gym**
* A Web3 project launches a Gym to **train an AI assistant for crypto degen tasks**.
* Tasks include **swapping tokens, tracking whale wallets, managing gas fees, and interacting with DeFi protocols.**
* Users submit **real trading demonstrations**, training the AI to execute on-chain transactions autonomously.
### 🔹 **Finance & Trading Automation Gym**
* A fintech company creates a Gym to **train an AI agent in investment research, portfolio management, and trade execution.**
* The AI learns to **scan financial reports, analyze market trends, and place trades based on defined strategies.**
### 🔹 **Enterprise Workflow Automation Gym**
* A business sets up a Gym to **train an AI for customer service, automating ticket responses, CRM updates, and data entry.**
* The AI **learns from real customer interactions**, improving its efficiency over time.
### 🔹 **AI Tutor Gym for Automated Learning**
* A group of educators builds a Gym that trains an AI **to generate and review student assignments, summarize lessons, and guide learning sessions.**
* The AI eventually functions as an **autonomous tutor** for self-paced education.
# Training Pools
The **Training Pool** is the core funding mechanism within The Forge, enabling **AI training environments (Gyms) to distribute rewards to contributors**. Gym creators fund a pool using **$VIRAL, USDC, or their own native tokens**, ensuring a steady flow of incentives to attract high-quality AI training demonstrations.
Training Pools enable **dynamic, competitive AI training**, where contributors earn based on the quality of their demonstrations, and Gym owners can scale their AI models efficiently.
***
## **How Training Pools Work**
### **1. Funding the Pool**
* Gym creators deposit **$VIRAL, USDC, or their own token** to finance AI training. A 20% platform fee is taken from each deposit.
* The pool is used to **pay workers who submit high-quality demonstrations**.
* Web3 startups can stake $VIRAL to unlock the ability to use their own token.
### 2. **Dynamic Payouts Based on Demonstration Quality**
* Workers **record demonstrations**, which are then **scored by ViralMind’s data quality agent**.
* **Higher-quality submissions receive larger payouts**, ensuring AI models learn from the best data.
* Example: A $0.20 bid per demonstration means:
* **85% quality submission:** Worker receives **$0.17**, and **$0.03 is refunded to the pool**.
* **Submissions below 50% quality:** No payout, and the full amount is refunded to the training pool.
### 3. **Bidding System for Worker Participation**
* Gym owners **adjust pricing** based on demand.
* If a Gym sets **$0.20 per demonstration** but receives few submissions, it can **raise the bid to $0.30** to attract more workers.
* This creates **an organic marketplace**, ensuring that AI models are trained efficiently without overspending.
### 4. **Reputation-Based Incentives**
* ViralMind implements a **Worker Reputation System**, rewarding consistent, high-quality contributors.
* **Workers with strong track records gain priority access to higher-paying tasks**, improving training efficiency.
***
## **Why Training Pools Matter**
🔹 **Incentive-Driven AI Training** – High-quality demonstrations are rewarded fairly, ensuring **AI agents learn from the best data sources**.
🔹 **Scalable and Competitive Marketplace** – The **bidding system** allows Gym owners to **dynamically control costs and training speed**.
🔹 **Tokenized AI Training Economy** – The system creates **constant demand for $VIRAL**, as businesses must acquire and fund pools to train AI models.
🔹 **Reputation and Quality Control** – Workers are **ranked by performance**, ensuring AI agents **improve efficiently** without low-quality data slowing progress.
***
## **Example Training Pool Funding Scenarios**
### **Web2 Enterprise Example**
* **Company Funds:** $20,000 USDC
* **Flat Setup Fee:** $250
* **20% Platform Fee:** $4,000
* **Remaining Worker Pool:** $15,750
* **Demonstration Price:** $0.20 per task
* **Total Demonstrations Funded:** 78,750
### **Web3 Startup Example**
* **Project Funds:** $20,000 USDC equivalent in $VIRAL
* **Flat Setup Fee:** 1 SOL (\~$100)
* **20% Platform Fee:** $4,000
* **Remaining Worker Pool:** $16,000
* **Demonstration Price:** $0.20 per task
* **Total Demonstrations Funded:** 80,000
These funding mechanisms **scale AI training based on demand, ensuring sustainable growth** while keeping contributors incentivized.
***
## **The Role of Training Pools in AI Growth**
Training Pools are **the economic backbone of The Forge**, ensuring AI models are **trained, improved, and monetized in an open, decentralized way**.
By aligning **funding, quality control, and incentives**, ViralMind enables:
✅ **Enterprise-grade AI development** without centralized control
✅ **A sustainable AI training workforce** where contributors are fairly rewarded
✅ **Continuous demand for $VIRAL**, reinforcing the AI training economy
With **Training Pools**, The Forge **transforms AI training into a scalable, self-sustaining system**, where Gym creators and AI trainers both benefit from continuous innovation.
# Introduction
Introduction to the Training Gym
## **What is the Training Gym?**
The **Training Gym** is a platform where users can participate in AI-related “demonstrations” (multi-task challenges) for fun and rewards. Participants complete demonstrations for $VIRAL rewards.
## **Key Features**
* **Community-Submitted Tasks:** Users can submit their own tasks, contributing to a wide range of **diverse data** and challenge types—spanning everything from simple code exercises to advanced AI tasks.
* **Automated Task Evaluations:** Tasks have AI-powered evaluators that detect malicious or incompetent submissions.
* **Token Holding:** To participate in a demonstration, just **hold** $VIRAL to qualify.
* **Build your own agent:** One of the most exciting features of the Training Gym is the ability to export your performance data to **build your own AI agent**. By participating in races, you generate high-quality training trajectories that can be:
* Exported in OpenAI JSONL, ChatML, or raw video + JSON formats.
* Used to fine-tune your own Large Action Model (LAM).
* Applied directly to your own projects, whether for gaming, office automation, or custom workflows.
The Training Gym not only rewards participants but also enables them to take the next step in developing powerful, personalized AI agents.
# How It Works
The **Training Gym** is designed to make AI training **intuitive, scalable, and rewarding**. It allows users to **record demonstrations, process them into structured training data, and contribute to AI development**—all while earning $VIRAL for their contributions.
Here’s a step-by-step breakdown of how it works:
## **1. Record Your Demonstration**
* Connect your wallet.
* Open the Training Gym and **start recording** your task.
* Perform actions as you normally would—**clicking, typing, navigating software, and executing workflows.**
* The AI **observes and records** every step in real time, creating a raw demonstration.
📌 **Example:** A user records themselves executing a DeFi swap, clicking through a wallet interface, confirming transactions, and optimizing slippage settings.
***
## **2. Review & Process Your Recording**
* Once a demonstration is recorded, **head to your Recording History** to review it.
* Click **Details** to replay your recording and analyze your steps.
* Press **Process** to break down the recording into **structured steps** AI can learn from.
📌 **Example:** AI segments the DeFi swap process into steps like "Connect Wallet," "Select Token Pair," "Adjust Slippage," and "Confirm Transaction."
***
## **3. Upload to Contribute & Earn Rewards**
* When ready, press **Upload** to submit your demonstration to the ViralMind dataset.
* Your submission will be **evaluated by the data quality agent**, which scores the clarity, effectiveness, and accuracy of your demonstration.
* If your submission meets quality standards, **you receive immediate $VIRAL rewards**.
* **The better your demonstration, the higher your payout.**
📌 **Example:** A well-structured demonstration of executing a trade earns **85% quality**, resulting in a payout of **$0.17 in VIRAL tokens** (with the remaining $0.03 returned to the training pool).
***
## **4. AI Learns & Adapts Over Time**
* Your demonstration is now **part of the open AI dataset**, allowing AI models to learn from real-world human actions.
* The AI **analyzes patterns, decision-making logic, and workflow optimizations**, improving its ability to complete tasks independently.
* As more demonstrations are uploaded, **AI agents continuously refine their accuracy and efficiency.**
📌 **Example:** After multiple high-quality submissions, an AI model **perfects the DeFi swap process**, executing transactions autonomously based on real human behavior.
## **5. Tracking AI Progress in the Skill Tree**
* Every successful demonstration **contributes to AI mastery** in specific skills.
* The **AI Skill Tree** visualizes progress, showing which skills have been mastered and which need more training.
* Users can **focus on growing specific branches**, ensuring AI models develop expertise in high-demand areas.
📌 **Example:** The **DeFi trading branch** of the Skill Tree now shows **95% mastery in executing swaps** but only **60% mastery in liquidity pool interactions**—indicating that more training is needed for staking and farming strategies.
## **Why This System Works**
🔹 **AI Learns Like a Human** – Instead of training on **static datasets**, AI improves **through real-world human interaction**.
🔹 **Open & Decentralized AI Training** – Anyone can **contribute and earn**, making AI development **community-driven**.
🔹 **Real-Time AI Optimization** – AI models **continuously improve** as more demonstrations are uploaded.
🔹 **Monetized AI Contributions** – Users **earn $VIRAL** for training AI, making AI development an **incentive-driven process**.
# Demonstrations
The **core of the Training Gym** is the **demonstration system**, where users record themselves completing tasks so that AI can learn from **real human interactions**. Every demonstration is evaluated using **a grading system** that determines **reward payouts and AI training effectiveness**.
This ensures that **only high-quality demonstrations improve AI models**, while contributors are fairly **compensated in $VIRAL** based on their performance.
***
## **How Demonstrations Work?**
### 1. **Users Record Demonstrations**
* Contributors **perform a task on their computer** while the system records every action.
* The demonstration captures **clicks, keystrokes, UI navigation, and task execution**.
* The AI **observes and processes** how humans complete tasks.
📌 **Example:** A user records a demonstration of sending a Solana transaction using Phantom Wallet, navigating through wallet settings, entering recipient addresses, and confirming fees.
***
### 2. **Processing & Structuring Data for AI Training**
* After recording, users **process their demonstration** to structure it into **clear, repeatable steps** AI can learn from.
* AI models analyze **workflow sequences, decision-making logic, and UI interactions**, allowing them to **mimic human behavior efficiently**.
* Contributors can **review their submission** to ensure it’s accurate and useful.
📌 **Example:** The system learns to break the Solana transaction prompt into structured steps like **“Open Phantom Wallet,” “Enter Recipient Address,” “Review Gas Fees,” and “Confirm Transaction.”**
***
### 3. **Submission & Quality Review**
* Once processed, users **upload their demonstration** for AI training.
* Each submission is evaluated by **ViralMind’s data quality agent**, which grades it based on **clarity, accuracy, and effectiveness**.
* The **higher the quality, the better the AI learns and the greater the reward for the contributor**.
📌 **Example:** A well-structured Solana transaction demo receives an **85% quality rating**, qualifying for near-max rewards.
***
### **Grading System: How Demonstrations Are Scored**
Each uploaded demonstration is **scored by an AI-powered data quality agent**. The grading process evaluates the submission across multiple dimensions:
#### **🔹 1. Clarity & Step-by-Step Execution (40%)**
* Are the actions performed in **a clear, structured, and repeatable** way?
* Does the demonstration include **all necessary steps without skipping any?**
* Is the recording **free of unnecessary delays or misclicks?**
📌 **Example:** A contributor records a **clear**, step-by-step demonstration of sending crypto without extra delays → **High Score**
***
#### **🔹 2. Accuracy & Task Completion (30%)**
* Did the user **correctly complete the task** from start to finish?
* Is the workflow **accurate and applicable to real-world use?**
* Are **errors corrected quickly** without affecting the AI’s ability to learn?
📌 **Example:** A user enters a **wrong wallet address** but **fixes it immediately and completes the transaction successfully** → **Good Score**
📌 **Example:** A user **submits a demonstration with missing steps**, like forgetting to confirm a transaction → **Low Score**
***
#### **🔹 3. AI Training Usefulness (20%)**
* Is this demonstration **generalizable** so AI can apply it to different cases?
* Does it help the AI **recognize patterns in human decision-making?**
* Is it a **new, valuable contribution**, or a duplicate of an existing submission?
📌 **Example:** A **unique demonstration** of interacting with a complex UI workflow → **High Score**
📌 **Example:** A **duplicate of an existing task without meaningful variation** → **Low Score**
***
#### **🔹 4. Efficiency & Flow (10%)**
* Was the demonstration **efficiently completed** without unnecessary delays?
* Did the user **execute the task smoothly** without excessive hesitations?
* Was the workflow **consistent and optimized** for AI learning?
📌 **Example:** A user **executes a workflow quickly and effectively** without mistakes → **High Score**
📌 **Example:** A user **takes too long or has inconsistent actions**, making it hard for AI to learn → **Low Score**
***
### **Reward Payouts Based on Grading**
The **higher the quality of a demonstration, the higher the payout**.
| **Quality Score** | **Reward Payout** | **Notes** |
| ----------------- | ----------------- | -------------------------------------------------- |
| **90-100%** | **100% payout** | Perfect execution, maximally useful AI training |
| **80-89%** | **85% payout** | High quality, minor inefficiencies or small errors |
| **70-79%** | **70% payout** | Good submission, may need slight improvements |
| **50-69%** | **50% payout** | Basic level, needs optimization |
| **Below 50%** | **No payout** | Poor quality, refunded to training pool |
📌 **Example:**
A demonstration worth **$0.20 per submission** gets **graded at 85%**.
The worker **receives $0.17**, and **$0.03 is refunded to the training pool.**
📌 **Example:**
A low-quality demonstration **graded at 40%** receives **no reward**, and the **full $0.20 is refunded** to the pool for future high-quality submissions.
***
### **Dynamic Reward Pool Efficiency**
* **Unused funds from low-quality submissions are returned to the Gym’s training pool**, ensuring that AI **only learns from high-quality data**.
* This **incentivizes contributors to submit clear, structured, and useful demonstrations**, maximizing AI performance.
* **Gym owners can adjust reward structures**, increasing payouts to attract better contributors.
📌 **Example:** A Gym **not receiving enough high-quality submissions** increases its bid from **$0.20 per demo to $0.30**, bringing in more skilled trainers.
# Demonstration Data
## Demonstration Data
Every aspect of our platform is designed to generate high-quality training data for multimodal computer-use agents. From AI-generated tasks to expert demonstrations, we capture comprehensive data that helps train AI to understand and replicate human computer interactions.
### Demonstration Files Structure
Each recorded demonstration consists of multiple files:
1. **input\_log.jsonl** - Detailed event log of all user interactions
2. **meta.json** - Metadata and configuration information about the demonstration
3. **recording.mp4** - Video recording of the screen during the demonstration
When you record a demonstration in the Training Gym, the system captures all your interactions in a standardized JSON format. Each user interaction is saved as a separate event in a JSONL file (JSON Lines format) called `input_log.jsonl`. Understanding this format can help you create more effective demonstrations.
#### Event Structure
Each recorded event has the following structure:
```json
{
"event": "eventType",
"data": {
// Event-specific properties
},
"time": 1234567890
}
```
* **event**: The type of interaction (e.g., mousemove, mousedown, keydown)
* **data**: Object containing event-specific details
* **time**: Timestamp in milliseconds when the event occurred
#### Event Types
The system records the following types of events:
**Accessibility Tree Events (Windows)**
1. **axtree**
```json
{
"event": "axtree",
"data": {
"duration": 5645,
"focused_element": {
"bbox": {
"height": 600,
"width": 800,
"x": 722,
"y": 335
},
"children": [],
"description": "",
"name": "",
"role": "Pane",
"states": {
"enabled": true,
"keyboard_focusable": false,
"visible": true
},
"value": ""
},
"queries": {
"cursor": {
"element": { /* Element details */ },
"position": { "x": 1270, "y": 688 }
},
"random1": {
"element": { /* Element details */ },
"position": { "x": 378, "y": 400 }
},
"random2": {
"element": { /* Element details */ },
"position": { "x": 1985, "y": 813 }
}
},
"tree": [
/* Array of UI elements and their properties */
]
},
"time": 1741119497902
}
```
Provides comprehensive information about the accessibility tree, capturing the structure and properties of all on-screen elements from every open application.
* **duration**: Time in milliseconds that the scan took
* **focused\_element**: Details about the currently focused UI element
* **queries**: Information about elements at specific positions
* **tree**: Full hierarchical structure of all visible UI elements, including:
* **bbox**: Bounding box (position and dimensions)
* **children**: Nested UI elements
* **description**: Element description
* **name**: Element name
* **role**: Element type (e.g., "Pane", "Text", "Button", "Image")
* **states**: Element properties (enabled, focusable, visible)
* **value**: Element content value
**Mouse Events**
1. **mousemove**
```json
{
"event": "mousemove",
"data": {
"x": 123,
"y": 456
},
"time": 1234567890
}
```
Tracks the mouse cursor position on screen.
2. **mousedown**
```json
{
"event": "mousedown",
"data": {
"x": 123,
"y": 456,
"button": "Left"
},
"time": 1234567890
}
```
Recorded when a mouse button is pressed.
3. **mouseup**
```json
{
"event": "mouseup",
"data": {
"x": 123,
"y": 456,
"button": "Left"
},
"time": 1234567890
}
```
Recorded when a mouse button is released.
4. **mousewheel**
```json
{
"event": "mousewheel",
"data": {
"delta": -120
},
"time": 1234567890
}
```
Tracks scrolling with positive values for scrolling down and negative for scrolling up.
**Keyboard Events**
1. **keydown**
```json
{
"event": "keydown",
"data": {
"key": "A"
},
"time": 1234567890
}
```
Recorded when a key is pressed.
2. **keyup**
```json
{
"event": "keyup",
"data": {
"key": "A"
},
"time": 1234567890
}
```
Recorded when a key is released.
### Key Identifiers
The system supports both Windows and Mac key formats:
**Windows Format Keys**
* Letters: `A`, `B`, `C`, ... `Z`
* Numbers: `Zero`, `One`, `Two`, ... `Nine`
* Modifiers: `Shift`, `LeftCtrl`, `RightCtrl`, `LeftAlt`, `RightAlt`
* Navigation: `Left`, `Right`, `Up`, `Down`, `Home`, `End`, `PageUp`, `PageDown`
* Special: `Space`, `Return`, `Backspace`, `Escape`, `Tab`, `Delete`, etc.
* Symbols: `BackTick`, `BackSlash`, `ForwardSlash`, `Plus`, `Minus`, etc.
**Mac Format Keys**
* Letters: `KeyA`, `KeyB`, `KeyC`, ... `KeyZ`
* Numbers: `Num0`, `Num1`, `Num2`, ... `Num9`
* Modifiers: `ShiftLeft`, `ShiftRight`, `ControlLeft`, `ControlRight`, `Alt`, `AltGr`, etc.
* Navigation: `LeftArrow`, `RightArrow`, `UpArrow`, `DownArrow`
* Function keys: `F1`, `F2`, ... `F12`
* Symbols: `BackQuote`, `Equal`, `Minus`, `LeftBracket`, `RightBracket`, etc.
#### Event Processing
The demonstration system processes these raw events into higher-level actions:
1. **mouseclick**: A brief press and release at nearly the same position
2. **mousedrag**: A sequence of mouse movements with the button held down
3. **type**: A sequence of character inputs combined into text
4. **hotkey**: Special key combinations like keyboard shortcuts
## Tips for Quality Recordings
* **Clear Actions**: Make deliberate, clear mouse movements and clicks
* **Consistent Typing**: Type at a steady pace to ensure accurate capture
* **Complete Workflows**: Ensure you capture all steps without skipping any
* **Minimize Errors**: While the system can handle corrections, try to minimize misclicks or typing errors
### Demonstration Metadata (meta.json)
The `meta.json` file contains important contextual information about each demonstration:
```json
{
"id": "20250304_151816", // Unique identifier for the demonstration (timestamp format)
"timestamp": "2025-03-04T15:18:16.813394600-05:00", // When the demonstration was recorded (ISO 8601 format)
"duration_seconds": 21, // Total length of the demonstration in seconds
"status": "completed", // Current status of the demonstration (completed, in-progress, aborted)
"title": "Find Kendrick Lamar concert tickets", // Title of the demonstration task
"description": "", // Optional additional description
"platform": "windows", // Operating system used (windows, macos, linux)
"arch": "x86_64", // System architecture
"version": "10.0.26100", // OS version
"locale": "en-GB", // System language/region setting
"primary_monitor": { // Information about the main display
"width": 3440, // Screen width in pixels
"height": 1440 // Screen height in pixels
},
"reason": "fail", // Why the demonstration ended (fail = task failed, done = task completed)
"quest": { // Information about the assigned task
"title": "Find Kendrick Lamar concert tickets", // Task title
"app": "StubHub", // Primary application to be used
"icon_url": "https://s2.googleusercontent.com/s2/favicons?domain=stubhub.com&sz=64", // Icon for the application
"objectives": [ // Step-by-step objectives for the task
"Open StubHub website in your browser",
"Search for Kendrick Lamar concert in Inglewood",
"Select quantity of tickets and apply price filter",
"Review available options before purchase"
],
"content": "Hi! I need to find 4 tickets to the Kendrick Lamar concert in Inglewood, and I'm trying to keep it under $500. Can you assist me with using StubHub?", // Task instructions in natural language
"pool_id": "67af7cf3a903635118192a5d", // Identifier for the task pool
"reward": { // Reward information
"time": 1741119480000, // Timestamp when reward was calculated
"max_reward": 7 // Maximum possible reward for completing this task (in $VIRAL)
}
}
}
```
### Video Recording (recording.mp4)
Each demonstration includes a screen recording that shows exactly what the user saw and did during the task. This provides:
1. Visual context for all the recorded interactions
2. Confirmation of the UI state during each action
3. A reference for how the task should be completed
4. Visual verification for quality evaluation
Understanding all components of the demonstration data helps you create high-quality submissions that achieve better scores in the grading system, maximizing both AI training effectiveness and your rewards.
# Deploy Agentic AI with $VIRAL
Our trained planning models will be available via API, allowing you to integrate sophisticated computer-control AI into your applications.
Stay tuned!
## Authentication
All API calls require payment in $VIRAL tokens. You'll need to:
1. Hold $VIRAL in your connected wallet
2. Include your wallet address in the authentication header
3. Sign API requests using your wallet
### Endpoints
`POST https://api.viralmind/v1/plan` **( Coming soon )**
```json
{
"goal": "Order a pepperoni pizza from Dominos",
"current_state": {
"messages": [],
"screenshot": "",
"viewport": {
"width": 1920,
"height": 1080
}
},
"max_steps": 10
}
```
#### Response Format
```json
{
"plan": [
{
"action": "click",
"target": "Order Online button",
"coordinates": [245, 123]
},
{
"action": "type",
"text": "pepperoni",
"target": "search field"
},
// Additional steps...
],
"estimated_cost": "0.05 $VIRAL"
}
```
# General FAQs
## **1. What is ViralMind?**
ViralMind is **the first decentralized AI training marketplace**, where AI agents are trained through **real-world human demonstrations** and deployed to **autonomously complete digital tasks.** Instead of relying on **pre-trained models with static datasets**, ViralMind enables **continuous AI learning, training, and monetization** through a decentralized, incentive-driven system.
***
## **2. How is ViralMind different from other AI platforms?**
Unlike traditional AI models that only generate text or images, ViralMind’s AI can:
✅ **Learn from real-world user interactions** via The Training Gym.
✅ **Execute complex workflows autonomously** through VM-1 Inference API.
✅ **Be trained and owned by businesses and individuals** using The Forge.
✅ **Evolve dynamically** as users submit demonstrations, ensuring AI **adapts and improves over time.**
ViralMind is **not just an AI assistant—it’s an AI workforce that learns, works, and scales.**
***
## **3. What is the Training Gym?**
The **Training Gym** is where AI models learn by watching and mimicking **real human demonstrations.**
* Contributors **record themselves completing digital tasks**, training AI agents to **navigate interfaces, execute actions, and optimize workflows.**
* High-quality demonstrations **receive $VIRAL token rewards**, ensuring continuous AI training.
📌 **Example:** A user records how to **execute a DeFi trade**, and the AI learns to complete the task independently.
***
## **4. What is The Forge?**
The **Forge** is where businesses and individuals can **create their own AI training environments (Gyms).**
* Users define **specific skills AI should learn** (e.g., crypto trading, financial modeling, enterprise automation).
* They **fund Training Pools** using **$VIRAL, USDC, or native tokens** to incentivize AI training.
* **Workers contribute demonstrations**, improving the AI while earning token rewards.
📌 **Example:** A **Web3 startup** creates a Gym to train an AI **crypto trading bot**, funding it with $VIRAL to attract skilled AI trainers.
***
## **5. What is VM-1 Inference API?**
VM-1 is **ViralMind’s AI inference engine**, allowing businesses and developers to **deploy agentic AI models** that:
✅ **Interact with software autonomously**
✅ **Execute workflows without human input**
✅ **Continuously refine their performance** based on real-world use
📌 **Example:** A fintech company **deploys an AI-powered financial analyst** that scrapes market data, compiles reports, and executes trades based on predefined conditions.
***
## **6. What is the $VIRAL token used for?**
$VIRAL is the **utility token that powers the ViralMind ecosystem**, facilitating:
* **AI training incentives** (contributors earn $VIRAL for demonstrations).
* **Gym funding** (businesses use $VIRAL to attract AI trainers).
* **AI monetization** (Gym owners and contributors profit from AI advancements).
* **Governance & staking** (future DAO participation and voting).
📌 **Example:** A business funds a **$100,000 Training Pool** in $VIRAL to train an AI-powered **customer support agent.**
***
## **7. How do I earn rewards in ViralMind?**
There are **multiple ways to earn** in the ViralMind ecosystem:
✅ **Submit AI training demonstrations** → Earn $VIRAL based on demonstration quality.
✅ **Fund a Gym and train AI models** → Deploy AI agents for monetization.
✅ **Stake $VIRAL (future feature)** → Earn rewards for ecosystem participation.
📌 **Example:** A user submits **a high-quality AI training demonstration**, receives an **85% quality score**, and earns **$0.17 in $VIRAL per task.**
***
## **8. Who owns the AI models trained on ViralMind?**
* AI models trained in the **Training Gym** contribute to the **open-source ViralMind dataset**, benefiting the entire ecosystem.
* AI models trained in **private Gyms (The Forge)** are **owned by their creators**, meaning businesses **can monetize** and **license** their trained AI models.
📌 **Example:** A company trains an AI **for automated document processing** and sells API access to enterprises.
***
## **9. How does ViralMind ensure high-quality AI training data?**
ViralMind uses a **data quality agent** to **grade every AI training demonstration**, ensuring only **high-quality contributions receive rewards.**
✅ **Demonstrations are scored based on accuracy, clarity, and task execution.**
✅ **Higher-quality submissions earn higher payouts.**
✅ **Low-quality submissions are rejected, with funds returning to the Training Pool.**
📌 **Example:** A demonstration with a **90% quality score** earns **100% of the reward allocation**, while a **50% quality demo** earns a **reduced payout**.
***
## **10. Can businesses use ViralMind without crypto?**
Yes. Businesses can fund Training Pools in **USDC**, which will be **automatically converted into $VIRAL** to fuel AI training incentives.
📌 **Example:** A Web2 company pays **$10,000 in USDC** to train an AI-powered **data entry assistant**, with **$VIRAL rewards distributed dynamically.**
***
## **11. Is ViralMind fully decentralized?**
ViralMind is designed to be **progressively decentralized**, meaning:
✅ **AI training is open to everyone** via the Training Gym.
✅ **Token incentives ensure no single entity controls AI training.**
✅ **Future governance via a DAO** will allow $VIRAL holders to vote on **AI priorities and ecosystem upgrades.**
📌 **Example:** In the future, staked $VIRAL holders may **vote on which AI models receive development funding.**
***
# Technical FAQs
## **1. What type of AI models does ViralMind use?**
ViralMind primarily focuses on **agentic AI models**, which are trained using **real-world human demonstrations** instead of traditional reinforcement learning. These models are:
✅ **Demonstration-Based Learning Models** – AI learns by observing and mimicking human interactions.
✅ **Action-Oriented AI** – Unlike standard LLMs, these models can **navigate software, execute actions, and automate workflows.**
✅ **Multi-Modal & UI-Interfacing** – AI interacts with **web apps, trading platforms, enterprise software, and blockchain environments.**
📌 **Example:** Instead of just answering questions like ChatGPT, **ViralMind’s AI can log into applications, fill out forms, and automate repetitive workflows.**
***
## **2. How does the AI training process work?**
ViralMind uses a **demonstration-based training system** in the **Training Gym**:
1. **Users record themselves completing digital tasks** (e.g., sending a crypto transaction, editing an Excel sheet).
2. **The AI processes and structures the demonstration** into step-by-step executable workflows.
3. **AI learns from repeated demonstrations**, improving efficiency over time.
4. **Training data is stored and optimized** within the ViralMind dataset for continuous AI refinement.
📌 **Example:** AI is trained to **execute a DeFi swap** by watching multiple high-quality demonstrations, then optimizing execution based on past success rates.
***
## **3. What is the architecture of the VM-1 Inference API?**
VM-1 is **ViralMind’s inference engine**, optimized for **real-time, agentic AI deployment.**
🔹 **Hybrid Architecture:** Uses a **combination of LLMs, structured action modeling, and reinforcement feedback loops**.
🔹 **Low-Latency API Calls:** Designed for **fast, multi-step execution**, allowing AI agents to interact with multiple interfaces in real-time.
🔹 **Modular Deployment:** AI models can be **hosted on ViralMind servers, deployed locally, or accessed via API for business automation.**
📌 **Example:** A company integrates VM-1 to **automate customer support**, allowing AI to **navigate internal databases and resolve tickets in real-time.**
***
## **4. What technologies power ViralMind?**
ViralMind’s AI training and deployment stack includes:
✅ **PyTorch & TensorFlow** – For model training and inference.
✅ **WebRTC & Puppeteer** – For UI interaction and AI-driven automation.
✅ **FastAPI & GraphQL** – For scalable API communication.
✅ **Solana (for $VIRAL transactions)** – To handle **decentralized AI training incentives and Training Pool funding.**
📌 **Example:** ViralMind’s AI agents can **interact with browser-based applications**, filling out forms and automating digital workflows using Puppeteer-based automation.
***
## **5. How does ViralMind ensure AI training data quality?**
ViralMind uses a **grading system powered by AI data quality agents**, ensuring:
✅ **Demonstrations are reviewed for accuracy and clarity.**
✅ **Only high-quality training data is used to refine AI models.**
✅ **Low-quality submissions receive reduced or no rewards.**
📌 **Example:** A contributor’s **poorly structured demonstration** may receive **a 50% quality score, reducing their payout**, ensuring that only **the best training data improves AI models.**
***
## **6. Can I train my own AI model using The Forge?**
Yes. **The Forge allows businesses and individuals to create custom AI training environments (Gyms).**
🔹 Define **the specific tasks** your AI model needs to learn.
🔹 **Fund the Gym’s Training Pool** using $VIRAL, USDC, or native tokens.
🔹 **Workers submit demonstrations**, training the AI in your **custom workflow.**
🔹 Once trained, **the AI model can be deployed via the VM-1 API or a private system.**
📌 **Example:** A **fintech company creates a Gym** to train an AI for **automated financial reporting and market analysis.**
***
## **7. How is AI execution optimized in ViralMind?**
ViralMind’s AI models use **adaptive reinforcement mechanisms** to improve execution performance:
✅ **Execution Logs:** Every AI interaction is logged and analyzed for optimization.
✅ **Task Memory:** AI recalls prior steps to improve multi-step workflows.
✅ **Adaptive Workflow Optimization:** AI improves **based on real-time performance data** from successful vs. failed task executions.
📌 **Example:** An AI agent **managing a DAO treasury** will **adjust its execution strategy** based on past transaction success rates.
***
## **8. Can businesses integrate ViralMind AI into their applications?**
Yes. Businesses can deploy **ViralMind-trained AI agents** via:
🔹 **VM-1 Inference API** – Direct API integration for **real-time AI execution.**
🔹 **Self-Hosting** – Businesses can **host and fine-tune models privately**.
🔹 **Custom AI Development** – Companies can **train proprietary AI models in The Forge** and deploy them internally.
📌 **Example:** A Web3 project integrates ViralMind AI to **automate smart contract security audits**, allowing the AI to **detect vulnerabilities in real time.**
***
## **9. How does ViralMind handle security and privacy?**
ViralMind prioritizes **data security and user privacy** through:
🔹 **End-to-End Encryption** – All AI training and execution data is secured.
🔹 **Private AI Deployment** – Businesses can train AI models **without exposing sensitive workflows.**
🔹 **Permissioned AI Execution** – Gym owners **control access to AI training environments** to protect proprietary data.
📌 **Example:** A legal firm using ViralMind AI for **document processing** ensures that **client-sensitive data is encrypted and remains private.**
***
## **10. Can ViralMind AI agents be fine-tuned after training?**
Yes. AI models in **The Forge** can be:
✅ **Retrained with new data** to refine workflows.
✅ **Adapted for different environments** by adjusting training parameters.
✅ **Fine-tuned on specialized datasets** to improve accuracy for industry-specific use cases.
📌 **Example:** An AI agent originally trained for **Web3 trading automation** can be **fine-tuned to execute NFT lending strategies.**
***
## **11. Does ViralMind AI work with blockchain applications?**
Yes. ViralMind AI is designed to integrate with **on-chain and off-chain environments**, allowing AI agents to:
✅ **Interact with smart contracts** on EVM and Solana-based chains.
✅ **Execute DeFi transactions autonomously.**
✅ **Analyze on-chain data for trading and risk assessment.**
📌 **Example:** An AI agent **tracks whale wallet movements on-chain**, predicting price trends and **automating trading strategies.**
***
## **12. How does ViralMind scale AI training and execution?**
ViralMind is **built for scalability**, leveraging:
✅ **Distributed AI training** via a decentralized contributor network.
✅ **Optimized inference models** for real-time execution via VM-1.
✅ **Auto-scaling compute infrastructure**, ensuring AI models handle high workloads efficiently.
📌 **Example:** A ViralMind-trained **customer support AI can scale across thousands of live chat instances** without performance loss.
***
## **13. How does ViralMind ensure AI is aligned with human goals?**
ViralMind AI is trained through **real-world human demonstrations**, ensuring:
✅ **AI agents align with human decision-making.**
✅ **Training data reflects optimal task execution strategies.**
✅ **Reinforcement feedback loops ensure AI continuously adapts.**
📌 **Example:** AI trained for **automated document review** prioritizes **human-reviewed quality benchmarks** to ensure accuracy.
***
## **14. What is the long-term vision for ViralMind AI?**
🚀 **AI that fully automates digital workflows across industries.**
🚀 **A decentralized AI training marketplace powering AI workforce expansion.**
🚀 **Self-learning AI agents that adapt to user needs dynamically.**
📌 **By 2026**, ViralMind aims to be the **leading AI training and deployment infrastructure**, supporting **Web3 automation, enterprise AI, and AI-driven business operations.**
# Token and Ecosystem FAQs
## **1. What is the $VIRAL token?**
$VIRAL is the **utility token that powers the ViralMind ecosystem**, facilitating:
✅ **AI training incentives** – Contributors earn $VIRAL for submitting AI training demonstrations.
✅ **Gym funding** – Businesses and individuals use $VIRAL to fund Training Pools for AI training.
✅ **AI model monetization** – Gym owners and contributors can profit from AI advancements.
✅ **Staking & governance (future feature)** – $VIRAL holders will participate in voting on AI development priorities.
📌 **Example:** A Web3 project **funds a Gym with $100,000 in $VIRAL** to train an AI-powered **DeFi trading assistant**.
***
## **2. How was the $VIRAL token launched?**
* $VIRAL was **fair-launched on Pump.fun**, ensuring **no VC or insider allocations**.
* The **team has locked 3.41% of the total supply** using Streamflow until **2026** for transparency.
* The ViralMind team **self-funded a $50K treasury** to build a strong token position and sustain Training Pool incentives.
📌 [**View Team Lock on Streamflow**](https://app.streamflow.finance/contract/solana/mainnet/EG7wNTj5Vd6XdZGY6gwodoEyUC9sP9WFx3F2quEdLaYj)
***
## **3. What is the purpose of Training Pools?**
Training Pools **fund AI development by rewarding contributors** for submitting AI training demonstrations.
* Gym owners **deposit $VIRAL (or USDC)** into a Training Pool.
* Contributors **earn from the pool** by submitting high-quality AI demonstrations.
* **Dynamic pricing adjusts payouts** based on demonstration quality and market demand.
📌 **Example:** A Training Pool offers **$0.20 per AI demonstration** → A high-quality submission earns **$0.17**, while a lower-quality one earns less or nothing.
***
## **4. How does the token economy create demand for $VIRAL?**
$VIRAL’s design ensures **continuous buy pressure** through:
✅ **Gym funding:** Businesses and projects **must purchase $VIRAL** to train AI models.
✅ **USDC conversions:** Non-crypto users can pay in **USDC, which auto-buys $VIRAL** for Training Pools.
✅ **AI model licensing:** Gym owners can **sell access to trained AI models**, increasing $VIRAL adoption.
✅ **Staking (future):** Long-term $VIRAL holders **earn rewards and governance power.**
📌 **Example:** An enterprise **spends $50,000 in USDC on AI training**, which **buys and distributes $VIRAL to contributors**.
***
## **5. How do contributors earn $VIRAL?**
✅ **Submitting AI training demonstrations** – Earn based on demonstration quality.
✅ **Training high-demand AI skills** – More advanced skills receive **higher payouts**.
✅ **Fine-tuning AI models in The Forge** – Contributors get **priority access to premium reward pools**.
✅ **Future staking & governance rewards** – Earn **passive incentives** for holding $VIRAL.
📌 **Example:** A contributor records **100 high-quality AI demonstrations** and earns **$200 in $VIRAL**.
***
## **6. Can I use other tokens to fund a Gym?**
Yes. While $VIRAL is the primary currency, The Forge will **support native token pools from partner projects.**
✅ Projects can **fund AI training in their own tokens**.
✅ ViralMind will **offer cross-token staking & incentive pools**.
📌 **Example:** A DeFi protocol **funds an AI trading Gym using its governance token**, rewarding contributors in their ecosystem.
***
## **7. How does the pricing system work for AI training?**
* Payouts for AI demonstrations are **denominated in USDC** and converted into $VIRAL dynamically.
* Gym owners **can increase rewards to attract more AI trainers**.
* If **low-quality submissions occur, funds return to the Training Pool**, maintaining efficiency.
📌 **Example:** A Gym offering **$0.20 per task** isn’t receiving enough submissions → It **raises the bid to $0.30** to attract better contributors.
***
## **8. Is there a limit to how much $VIRAL I can earn?**
No, but **earnings depend on demonstration quality, training demand, and pool funding.**
* Higher-quality AI demonstrations receive **higher payouts**.
* **Rare or advanced AI training tasks pay more** than basic tasks.
📌 **Example:** Training an AI **to manage complex accounting workflows** pays more than **training an AI to send emails**.
***
## **9. Can I stake $VIRAL?**
🚀 **Staking mechanisms will launch in future updates.**
* **Long-term holders will earn rewards.**
* **Governance staking will allow voting on AI priorities.**
* **AI research grants will be funded by the staking pool.**
📌 **Example:** Staking **50,000 $VIRAL** may give **governance rights over The Forge’s AI development roadmap.**
***
## **10. How does ViralMind ensure a sustainable token economy?**
ViralMind’s token economy is designed for **long-term sustainability** through:
✅ **Constant AI training demand** → Businesses buy $VIRAL to fund AI development.
✅ **Efficient Training Pools** → Unused funds from bad demonstrations return to pools.
✅ **Enterprise partnerships** → ViralMind works with companies that **deploy AI models at scale**, increasing token demand.
📌 **Example:** As more companies **train AI models on ViralMind**, the demand for $VIRAL increases, ensuring **a sustainable token economy.**
***
## **11. What happens to unused funds in Training Pools?**
If a contributor submits **a low-quality AI demonstration**, part of the reward is **returned to the Training Pool.**
* **Ensures training efficiency** by funding only high-quality AI contributions.
* **Prevents unnecessary token inflation** from low-effort submissions.
📌 **Example:** A Training Pool funds **10,000 demonstrations**, but **only 8,000 are high-quality** → **The remaining $VIRAL returns to the pool** for future training.
***
## **12. Is $VIRAL deflationary?**
While $VIRAL has **no forced burn mechanism**, its **economic model naturally limits supply growth**:
* **Staking will lock up supply**, reducing market circulation.
* **Gyms must continuously purchase $VIRAL**, creating demand.
* **Unused Training Pool funds remain locked until AI training occurs.**
📌 **Example:** As more AI models are deployed, **$VIRAL becomes increasingly scarce**, driving long-term value.
***
## **13. How does ViralMind compare to other AI tokens?**
| **Feature** | **ViralMind ($VIRAL)** | **Other AI Tokens** |
| ----------------------- | -------------------------------------------------------- | ------------------------------------------ |
| **Utility** | AI training incentives, Gym funding, governance, staking | Mostly governance-focused |
| **Monetization** | Businesses buy $VIRAL to fund AI training | Most tokens have no external revenue model |
| **AI Training Model** | Demonstration-based, real-time learning | Pre-trained static models |
| **Token Demand** | Directly tied to AI adoption | Mostly speculative |
| **Enterprise Adoption** | White-label AI solutions available | Limited business use cases |
📌 **Why this matters:** Unlike other AI tokens, **$VIRAL has real utility tied to AI training demand, making it more sustainable.**
***
