# Overview Bringing non-fungible tokens and decentralized finance to the masses. ![The Monnfts Token (MON)](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2Fp1jKP9AKaiTthd6CSFag%2Fwall-3.jpg?alt=media\&token=d7429de1-f197-4330-a99a-1cf9b8f2df10) ## What is MONNFTS? Monnfts is a programming and development project built upon web 3 software on the blockchain platform, the project operates in the fields of NFTs, Affiliate Marketing and AMM Dex. Which are explained as thus: * NFT (Non-fungible token) is a currency which is unique and cannot be replaced by other tokens. The realm of NFTs is currently growing and expanding rapidly, a lot of businesses have been putting resources towards its development. * Affiliate Marketing (Affiliate Networks) operates on the CPA (cost per action) model, which is charged for each customer action (buying, downloading software, completing a form). * AMM DEX (Automated Market Maker, Decentralized Exchange) is a trading method that uses an algorithm to calculate the token price at the time of purchase on decentralized exchanges. ![ECOSYSTEM MONNFTS](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2FamJzp32l9M6QR9V11W0V%2Fz3116201886552_7c0d4ddac71b644d5017f4321aac93ca.jpg?alt=media\&token=0a6b8d59-23d9-456c-840c-1d4010f834e7) ### **Highlights of MONNFTS** The highlight of Monnfts is it’s role as an all-in-one suite for NFT, Marketing and DeFi. Monnfts has a ton of features, including: * NFT Marketplace: A marketplace for artist’s NFT products. * CryptoPop: 5000 unique collectible characters stored on the Binance Smart Chain (BSC) with certified proof of ownership. They’re fun, small, 24×24 pixel art characters that are algorithmically generated and tokenized. * M2C: Affiliate network which operates under the CPA and CPO models. * Trade: Provide exchange-trading features and Liquidity—similar to Uniswap. * Earning: Provide liquidity to AMM DEX for receiving your rewards. * Launchpad: This is a feature of launching tokens similar to Launchpad in the Binance ecosystem. ## The MONNFTS Token (MON) MON coin is a cryptocurrency inspired by Bakeryswap. Likewise, MON coin works similarly to Bake in that it operates at low cost and fast speeds. MON coin is stored on the Binance smart system chain, which brings its many benefits to the community. Because of it’s architecture, MON coin can achieve higher transaction speeds while keeping costs down. Currently, the MON token is understood as the original BEP-20 governance under the BSC platform. Accordingly, the MON coin will take on the main roles: * Used for the purchase of NFT tokens. * Used to purchase CryptoPOP. * Used to provide the liquidity to MONSwap. * Received as rewards for staking, farming, and gaming. * Interact with other platforms such as M2C or Launchpad. # Tokenomics ![](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-Mio1-rDAL3IsoQDatd4%2F-MlEMT5nrgDsVJRno-rp%2F-MlENuG7vVbxbIJbcPgd%2FBanner-digital-MON%20Token.jpg?alt=media\&token=69f1ffc1-2423-408f-96c5-5b36b6832170) ### Token information Token Name: **MONNFTS**\ Ticker: **MON**\ Blockchain: **Binance Smart Chain**\ Token Standard: **BEP-20**\ Contract: [0x5E4f0f6FAC92E76E41a0d043DF565fe8fBFc1De3](https://bscscan.com/address/0x5E4f0f6FAC92E76E41a0d043DF565fe8fBFc1De3)\ Token type: **Governance, Utility…**\ Total Supply: **10,000,000,000 MON** ### MON Tokenomics

MON Tokenomics

Key Changes to Monnfts Tokenomics: **1% Airdrop: 100% Release at TGE** * We want to express our gratitude to our community members who have supported us from day one. To show our appreciation, we’re introducing a 1% airdrop, with 100% of the tokens being released at Token Generation Event (TGE). This means that everyone who has been a part of our journey will receive a token share right from the start. **6% Seed Round: 10% Release at TGE, Unlock Within 10 Months** * For our early supporters and investors, the 6% seed round allocation will see 10% of the tokens released at TGE, with the remaining 90% being gradually unlocked within the next 10 months. We believe this structure aligns the interests of our investors with the long-term success of Monnfts. **10% Exchange Liquidity Provision** * Liquidity is vital for a thriving marketplace. We’re dedicating 10% of the tokens to ensure a robust and dynamic exchange liquidity provision to benefit all token holders. **14% Private Sale: Burn All Remaining Tokens in the Contract** * In the interest of fairness and transparency, we have decided to burn all remaining tokens from the 14% private sale. This will eliminate any overhang and ensure that the token supply remains accountable. **33% Marketing: Release Schedule Over 3 Years Equivalently Every Month** * To fuel the growth and awareness of Monnfts, we are allocating 33% of the tokens to a marketing fund. These tokens will be released on a schedule spread evenly over the course of 3 years. This ensures that our marketing efforts remain steady and impactful. **36% Burn: Unlock and Burn Quarterly Within 24 Months** * To maintain a deflationary aspect to our tokenomics, 36% of the tokens will be unlocked and burned quarterly over a 24-month period. This will contribute to reducing the overall token supply, enhancing the scarcity and value of Monnfts. At Monnfts, we believe in a community-first approach. Our revised tokenomics system is designed to reward our early supporters, maintain fairness and transparency, and provide a strong foundation for the future growth and development of our ecosystem. We are immensely thankful for your continued support, and we look forward to building the future of NFTs and blockchain technology together. Stay tuned for more updates and exciting developments on our journey! # Roadmap and Updates ### **Completed Milestones** :ballot\_box\_with\_check: Q1,2 - 2021: Research and development of the blockchain ecosystem. Establishment of a dev team, building a blockchain system on BSC platform according to BEP20 standards. :ballot\_box\_with\_check: Q3 - 2021: Begin testing web3 NFTs Marketplace, complete website Monnfts.com; create social media, Lite Paper. :ballot\_box\_with\_check: Q4 - 2021: Launch NFTs Marketplace :ballot\_box\_with\_check: Q1 - 2022: Airdrop, Private Sale on invest.monnfts.com. IDO/Lauch Token on Pinksale and DxSale. Listing exchanges DEX, CEX. Listing on CMC and Coingecko. :ballot\_box\_with\_check: Q2 - 2022: Launching CryptoPops on Pop.monnfts.com ### **Updated Roadmap** #### Q2 - 2022: \+ Prebuild M2C (Affiliate marketing platform); \ \+ Release the Beta version of Affiliate Marketing and start the test network. #### Q3 - 2022: \+ Cryptopops NFT Collection list on Monnfts Marketplace.\ \+ Build Monswap with Moncake token. #### Q4 - 2022: \+ Launching M2C platform.\ \+ Launching Monswap and Staking NFT Pops.\ \+ Prebuild Mini Games for play to earn. #### Q1 - 2023: \+ Prebuild centric METAVERSE FOR Everyone: NFTs Room, Event Room, Shopping Room.\ \+ Launching Mini Games. **Q3 - 2023:** Launching centric METAVERSE. **Year 2024** **First Quarter — 2024:** Improvement In the first quarter of 2024, we are dedicated to improving our Monnfts system and enhancing our Tokenomics. These improvements will set the foundation for the groundbreaking developments we have in store. **Second Quarter — 2024:** Marketing and Partnerships Our focus in the second quarter is on marketing and expanding our user base. We are aiming to reach 500,000 users and establishing vital links with museums, galleries, and artists. Furthermore, we are excited to announce our cooperation with the Ho Chi Minh City Museum, and we’ll be applying for a license to authenticate antiques, organize auctions, NFT auctions, and seek partners in the antique segment. **The third to fourth quarter of 2024:** Upgrade the development of the CryptoPops NFT collection on various Blockchain platforms 1. tofuNFT • BNB Chain 2. OpenSea • Ethereum 3. OpenSea • Polygon 4. tofuNFT • Polygon 5. Magic Eden • Solana **Year 2025** **First Quarter — 2025:** Milestone Achievements In early 2025, we aim to achieve significant milestones, including the completion of the CMC affiliate marketing network and reaching 1000 partners and over 100,000 users. These achievements will solidify our presence and impact in the NFT space. **Second Quarter — 2025:** * Foster development collaboration with Carspa. * Debut security tokens with functionalities akin to ERC-20 on the Ethereum blockchain — CS, aiming to reflect the value of various assets, including silver ETFs, corporate bond ETFs, and stocks. These are collateralized by underlying assets and can be freely transferred between wallets. **Third Quarter — 2025** * Private Sale, IDO/Launch Token on Pinksale and DxSale. * Listing exchanges DEX, CEX. * Listing on CMC and Coingecko **Fourth Quarter — 2025:** * Promote collaboration with PPP\_GGI to tokenize health alert features based on blood pressure and heart rate. * Globalize the PPP\_GGI service. * Partner with banks to issue the GGI Visa card.

ROADMAP 2024-2025

This roadmap represents our unwavering commitment to innovation and growth. We invite you to join us on this incredible journey, and together, we will shape the future of NFTs and blockchain technology. Stay tuned for more updates and developments as we work tirelessly to make Monnfts a global leader in the world of blockchain and digital assets. # Airdrop 🏆The 1st MonNFTs Airdrop Campaign is close with incredible result, here is massive statistic from the campain. We very proud to receive your love for this. 👑 Total joined member: 112.198 🎉 Winner: 5.000 👍This is awesome, exceed our expectation. very thank full to a great MonNFTs Community! Thanks for everything. 🎁 MonNFTs winner list: [https://docs.google.com/spreadsheets/d/1C6dneXMlIGHkH9o-5-qefIDRQw2qNqtUJhomdnFZ33A/](https://docs.google.com/spreadsheets/d/1C6dneXMlIGHkH9o-5-qefIDRQw2qNqtUJhomdnFZ33A/edit?usp=sharing) # General information Mirror is building the foundation for the next generation of intelligence networks. ### Building the Future of Collective Intelligence
## Abstract Artificial Intelligence has transformed how humans access information. However, today's AI systems remain largely isolated, generating responses from individual models without the benefits of collaboration, collective reasoning, or persistent intelligence. Mirror introduces a new paradigm. Instead of relying on a single AI perspective, Mirror enables networks of specialized intelligence entities to collaborate, analyze, debate, and simulate possible futures. The result is a collective intelligence platform designed to help individuals, businesses, researchers, and organizations make better decisions in an increasingly complex world. Mirror begins as an Intelligence Simulation Platform, evolves into a Web3 ownership infrastructure, expands into an Intelligence Infrastructure Network, and ultimately becomes a global social network of autonomous intelligence communities. *** ## 1. Introduction Every major technological era has introduced a new network. The Internet connected information. Social Media connected people. Artificial Intelligence connected humans and machines. Mirror introduces the next evolution: A network where intelligence itself can interact, collaborate, and evolve. Modern AI tools provide impressive responses but remain fundamentally limited by single-perspective reasoning. Complex decisions often require multiple viewpoints, competing opinions, and collaborative analysis. Mirror was created to address this challenge.
*** ## 2. The Problem As AI adoption accelerates, several fundamental limitations remain. ### Single-Perspective Intelligence Most AI systems generate answers from a single reasoning process. Users receive one interpretation instead of a diversity of perspectives. ### Lack of Collective Analysis Human breakthroughs emerge through discussion, disagreement, and collaboration. Current AI systems rarely replicate this process. ### Ephemeral Knowledge Conversations disappear after completion. Intelligence does not accumulate collectively over time. ### Limited Context Most AI interactions remain disconnected from larger ecosystems of knowledge and experience. ### No Persistent Intelligence Communities There is currently no large-scale infrastructure where autonomous intelligence communities can continuously interact and evolve.
*** ## 3. The Mirror Vision Mirror is building the world's first Collective Intelligence Network. The goal is not to create another chatbot. The goal is to establish a digital ecosystem where intelligence becomes collaborative, persistent, and interconnected. Mirror enables hundreds of specialized intelligence entities to work together on behalf of users. Each entity contributes unique perspectives, expertise, and reasoning patterns. Together they form intelligence communities capable of analyzing complex questions from multiple dimensions. Over time, these communities become part of a larger intelligence network capable of generating increasingly valuable insights.
*** ## 4. The Mirror Intelligence Engine At the core of Mirror lies the Mirror Intelligence Engine. This architecture coordinates large numbers of specialized intelligence entities that operate simultaneously to explore different perspectives and possibilities. Rather than producing a single answer, the system generates collective intelligence through collaboration. Core capabilities include: * Parallel reasoning * Multi-perspective analysis * Collaborative intelligence generation * Consensus formation * Scenario simulation * Long-term knowledge accumulation This approach enables more comprehensive decision support than traditional AI systems. Mirror does not attempt to predict a single future. Instead, it evaluates multiple possible futures and identifies the most probable outcomes based on available information.
*** ## 5. Intelligence Simulation Mirror's first product is an Intelligence Simulation Platform. Users submit questions, challenges, strategies, or decisions. The system generates collaborative analysis from multiple specialized perspectives. Examples include: Business expansion strategies Market opportunities Investment decisions Product launches Policy planning Technology trends Risk assessment The objective is not to provide certainty. The objective is to improve decision quality.
*** ## 6. Mirror Flywheel Mirror becomes more valuable as its network grows. The ecosystem operates through a self-reinforcing cycle. Users create projects. Projects generate intelligence communities. Communities create simulations. Simulations generate knowledge. Knowledge improves collective intelligence. Improved intelligence attracts more users. More users create more communities. The network continuously strengthens itself. This creates a powerful long-term competitive advantage.
*** ## 7. Development Roadmap ### Phase 1 — Intelligence Simulation Platform Current Phase Objectives: Launch the Mirror platform for public users. Build: * Collective Intelligence Engine * Credit System * Subscription Plans * Future Simulation Tools Supported Payments: * PayPal * Creem Revenue Sources: * Credit purchases * Monthly subscriptions * Premium simulations Primary Goal: Validate product-market fit and build the initial intelligence network. *** ### Phase 2 — Web3 Ownership Infrastructure Objectives: Introduce ownership and portability of intelligence assets. Capabilities: * Wallet integration * Digital identity * Community ownership * Marketplace infrastructure * Decentralized asset management Users gain ownership over their intelligence communities and digital assets. Revenue Sources: * Marketplace fees * Premium assets * Community creation fees Primary Goal: Create an ownership layer for the intelligence ecosystem. *** ### Phase 3 — Mirror Infrastructure Network Objectives: Transform Mirror into an Intelligence Infrastructure Provider. Organizations and developers gain access to Mirror through APIs and hosted infrastructure. Capabilities: #### Simulation API Integrate Mirror simulations into external applications. #### Community API Create specialized intelligence communities. #### Enterprise Intelligence Infrastructure Deploy private intelligence networks. #### Hosted Intelligence Services Persistent intelligence communities running continuously on Mirror infrastructure. Mirror becomes a foundational intelligence layer for third-party products. Revenue Sources: * API usage * Enterprise licensing * Infrastructure subscriptions * Hosted intelligence services Primary Goal: Become the infrastructure powering next-generation AI applications. *** ### Phase 4 — Mirror Network Objectives: Launch the world's first Intelligence Social Network. Projects can publish intelligence communities to the public network. Communities can: * Exchange information * Collaborate * Debate ideas * Conduct research * Build collective knowledge Over time, a large-scale intelligence ecosystem emerges. The network becomes increasingly valuable as participation grows. Revenue Sources: * Premium network services * Community subscriptions * Enterprise participation * Sponsored ecosystems Primary Goal: Establish a global intelligence network. *** ### Phase 5 — Autonomous Intelligence Economy Long-Term Vision Intelligence communities become autonomous economic participants. Potential activities include: Research Consulting Data analysis Knowledge generation Business intelligence Market forecasting Communities may collaborate, provide services, and generate value within the ecosystem. Primary Goal: Create a self-sustaining digital economy powered by collective intelligence. *** ## 8. Business Model Mirror operates a diversified revenue model. ### Consumer Revenue * Credits * Subscriptions * Premium simulations ### Marketplace Revenue * Community assets * Templates * Digital ownership services ### Infrastructure Revenue * API consumption * Enterprise licensing * Hosted intelligence services ### Network Revenue * Premium participation * Community services * Future ecosystem products This structure allows Mirror to scale across both consumer and enterprise markets. *** ## 9. Market Opportunity Artificial Intelligence is entering a new era. The next generation of AI will not be defined solely by larger models. It will be defined by networks of intelligence working together. Mirror operates at the convergence of: * Artificial Intelligence * Decision Intelligence * Agent Systems * Digital Ownership * Social Networks * Enterprise Infrastructure These markets collectively represent hundreds of billions of dollars in future economic value. Mirror aims to become a foundational layer within this emerging category. *** ## 10. Fundraising Strategy Mirror intends to pursue staged fundraising aligned with product maturity. ### Seed Round Focus: * Core engineering * Infrastructure * Product development ### Strategic Round Focus: * Ownership infrastructure * Marketplace development * Ecosystem expansion ### Growth Round Focus: * Infrastructure platform * Enterprise adoption ### Scale Round Focus: * Global network expansion * International partnerships Capital Allocation: 40% Engineering 25% Infrastructure 15% Research & Development 10% Operations 10% Ecosystem Growth *** ## 11. Governance As the ecosystem matures, governance may gradually decentralize. Areas of governance may include: Protocol upgrades Network standards Community policies Infrastructure development The objective is sustainable growth while preserving innovation. *** ## 12. Conclusion The Internet connected information. Social media connected people. Artificial intelligence connected humans and machines. Mirror seeks to connect intelligence itself. What begins as a simulation platform evolves into a network of collective intelligence, an ownership ecosystem, an infrastructure layer, and ultimately a new digital society where intelligence can collaborate at global scale. Mirror is building the foundation for the next generation of intelligence networks. The future will not be shaped by isolated AI systems. The future will be shaped by collective intelligence. Mirror is building that future. # What Is Mirror? Mirror is an AI simulation and decision intelligence platform designed to help businesses test decisions before making them in the real world. Instead of simply asking AI what might happen, Mirror allows teams to simulate how markets, customers, competitors, stakeholders, and other relevant actors could react to a decision over time. Businesses can use Mirror to explore questions such as: * How might customers react to a new product? * What happens if we increase or decrease pricing? * How could competitors respond to a market entry? * What objections could emerge during a product launch? * How might public sentiment evolve around a new narrative? * What risks could appear before a major business decision? The objective is not to predict one guaranteed future. The objective is to **simulate multiple plausible reactions, expose uncertainty, identify risks, and make the next decision clearer.** ### Mirror Is a Scenario Simulation Platform, Not a Chatbot Traditional AI assistants are excellent at answering questions, summarizing documents, generating ideas, and analyzing information. But many important business decisions cannot be answered by a single prompt. The outcome depends on how different participants react to one another. A customer reacts to a price change. A competitor responds to a new product. Investors react to market signals. Media amplifies a narrative. Communities influence public perception. Each reaction can create another reaction. Mirror is designed for this type of problem. Instead of generating only one answer, Mirror creates an environment where multiple AI-driven actors can interact, respond, and evolve across a scenario. This transforms AI from an **answer engine** into a **decision simulation system**. ### How Mirror Works Mirror structures simulations around four core layers. #### Layer 1: Decision Context Every useful simulation begins with a clearly defined decision. The user provides the information needed to describe the environment being tested. This might include: * Product information * Market research * Pricing strategy * Customer profiles * Competitor intelligence * Launch plans * Internal reports * Public narratives * Industry research * Policy documents * Strategic assumptions Mirror works best when the simulation is focused around a specific question. For example: **“How could the market react if we launch this product at $99?”** is more useful than: **“Tell me everything about this market.”** A bounded scenario creates a more useful simulation environment. ### Layer 2: Scenario Intelligence Mirror analyzes the available information and identifies the elements that influence the decision. Depending on the simulation, these may include: * Customers * Competitors * Investors * Influencers * Regulators * Media * Communities * Market segments * Business incentives * Constraints * Claims * Risks * Behavioral signals These relationships form the underlying intelligence structure of the simulation. Instead of treating information as disconnected documents, Mirror attempts to understand **who matters, what they care about, what influences them, and how those forces interact.** ### Layer 3: Multi-Agent Simulation Once the scenario is structured, Mirror can simulate how different actors may react. AI agents represent relevant perspectives inside the environment. Across multiple simulation rounds, those agents can respond to the decision, to the available evidence, and to reactions from other agents. This can surface patterns such as: * Customer objections * Adoption barriers * Competitive responses * Price sensitivity * Trust signals * Narrative amplification * Reputation risks * Missing evidence * Unexpected stakeholder reactions * Market pressure points The purpose is not to create artificial characters for entertainment. Each agent represents a perspective or behavioral force that could influence the outcome of the decision. As agents interact, Mirror can reveal second-order effects that may be difficult to see from a static analysis. ### Layer 4: Decision Forecast The simulation is translated into an actionable forecast. Instead of presenting a single prediction as certainty, Mirror helps operators examine: * Likely scenarios * Alternative outcomes * Key assumptions * Risk factors * Stakeholder reactions * Areas of uncertainty * Missing information * Potential opportunities * Recommended next tests The result gives decision-makers a structured view of **what could happen and why**. This is particularly important because real markets are rarely deterministic. There is usually no single future. There are multiple plausible futures with different probabilities, dependencies, and consequences. Mirror helps teams explore those possibilities before committing resources in the real world. ## Why Not Just Ask ChatGPT? A chatbot answers a question. Mirror simulates a system. That distinction becomes important when outcomes depend on interaction. For example, you could ask a chatbot: **“Will customers like this pricing strategy?”** The chatbot might analyze market principles and provide a reasonable answer. Mirror approaches the problem differently. It can model several relevant customer groups, competing products, pricing expectations, trust signals, market conditions, and stakeholder incentives. Those actors can then react to the proposed pricing strategy and to each other. Instead of only receiving an answer, the decision-maker can examine the **behavioral dynamics behind the outcome.** ### Mirror Makes the Reasoning Behind the Simulation Inspectable AI forecasts become significantly more useful when users can understand why the system produced them. Mirror is designed to expose the structure behind a simulation. Operators should be able to examine: * Which actors influenced the outcome * Which assumptions were important * Which incentives shaped behavior * Which risks appeared repeatedly * Which evidence changed reactions * Where uncertainty remains This makes simulation outputs easier to challenge, refine, and rerun. A simulation should not be treated as an oracle. It should be treated as a **decision laboratory**. ## Mirror vs Chatbots vs Traditional Forecasting ### Chatbots Best suited for: * Direct questions * Research * Summaries * Brainstorming * Content generation * Individual analysis The AI primarily responds to the user's prompt. ### Traditional Forecasting Traditional forecasting often relies on: * Historical datasets * Statistical models * Analyst assumptions * Financial models * Trend extrapolation These methods are extremely valuable when reliable historical data exists. However, they may struggle with decisions where human reaction significantly changes the outcome. ### Mirror Mirror focuses on **interactive scenario simulation**. It combines decision context, behavioral actors, AI agents, scenario dynamics, and structured forecasting. The goal is to understand not only: **“What could happen?”** but also: **“Who could cause it, why might they react that way, and what happens after they react?”** ## Where Mirror Fits in the AI Forecasting Landscape Mirror sits at the intersection of several emerging technologies: #### AI Simulation Testing hypothetical decisions inside simulated environments. #### Multi-Agent Systems Using multiple AI agents to represent different actors, incentives, and perspectives. #### Decision Intelligence Using data, models, AI, and structured reasoning to improve decision-making. #### Scenario Planning Exploring multiple possible futures rather than relying on one prediction. #### Forecasting Estimating the likelihood and consequences of potential outcomes. Together, these capabilities allow Mirror to help organizations move from: **Ask → Answer** toward: **Decision → Simulation → Evidence → Action** ## When Should You Use Mirror? Mirror is most valuable when **reaction changes the outcome**. ### Product Launch Simulation Before launching a product, simulate how different customer segments, competitors, reviewers, and market participants could respond. Teams can identify: * Positioning problems * Customer objections * Adoption barriers * Competitive reactions * Messaging opportunities before launch day. ### Pricing Strategy Pricing decisions create behavioral responses. Mirror can help test scenarios such as: * Price increases * Discount strategies * Subscription changes * Freemium models * Premium positioning * Competitor undercutting Instead of evaluating price only through spreadsheets, teams can explore how different market participants might react. ### Market Entry Simulation Entering a new market creates uncertainty around: * Customer demand * Competition * Local expectations * Distribution * Regulation * Positioning * Pricing Mirror can simulate these forces before a company commits significant capital. ### Public Opinion Forecasting Organizations can test how narratives may spread across different audiences. Potential simulations include: * Corporate announcements * Brand positioning * Reputation issues * Public campaigns * Industry narratives The system can help identify where narratives may gain support, encounter resistance, or change as they spread. ### Crisis Communication Rehearsal During a crisis, every communication can create another reaction. Mirror can be used before publishing a response to simulate reactions from: * Customers * Media * Employees * Investors * Online communities * Other stakeholders This gives communication teams an opportunity to identify potential escalation paths before releasing a statement publicly. ### Competitive Strategy Businesses can also simulate how competitors might respond to moves such as: * Entering a new category * Reducing prices * Launching new features * Expanding geographically * Changing distribution * Introducing a new business model The goal is to anticipate second-order consequences rather than evaluating the company's action in isolation. ## Mirror Does Not Predict the Future This distinction is important. No AI system can perfectly predict complex markets, human behavior, or future events. Mirror should not be viewed as a machine that tells users exactly what will happen. Instead, Mirror helps teams explore: **What might happen.** **Why it might happen.** **Which assumptions matter.** **Which risks deserve attention.** **What information is missing.** **What should be tested next.** The value of simulation is not certainty. The value is reducing blind spots before making consequential decisions. ## What Should You Review Before Trusting a Simulation? Before acting on a Mirror simulation, decision-makers should ask several questions. #### Are the important actors represented? If a stakeholder capable of changing the outcome is missing, the simulation may also miss an important reaction. #### Are the incentives realistic? Actors should behave according to plausible motivations, constraints, and interests. #### Are the assumptions visible? Important assumptions should be identifiable so operators can challenge them. #### Is there enough evidence? Weak evidence creates weak simulations. Teams should understand which conclusions are well supported and which remain speculative. #### Does the simulation reveal something actionable? A useful simulation should improve the next decision. It may reveal that the team should: * Gather more evidence * Change positioning * Adjust pricing * Test another audience * Modify the launch strategy * Prepare for a specific competitive response * Run another scenario If the next decision becomes clearer, the simulation has created value. ## From Prediction to Simulation For decades, businesses have tried to understand the future through market research, surveys, historical data, financial models, consultants, and forecasting systems. Those tools remain valuable. AI introduces another capability. Businesses can now begin creating simulated environments where multiple intelligent actors react to hypothetical decisions before those decisions become real. This changes the question. Instead of asking: **“What does AI think we should do?”** organizations can ask: **“What happens if we do this?”** Then they can test another scenario. And another. Each simulation becomes an experiment. ## Simulate Before You Decide The most expensive part of many business decisions is discovering the mistake after the decision has already reached the market. A failed launch. The wrong price. Unexpected competitor behavior. Customer backlash. Weak demand. Poor positioning. A misunderstood narrative. Mirror gives teams a place to explore those possibilities earlier. Define the decision. Build the scenario. Simulate the reactions. Inspect the evidence. Challenge the assumptions. Then decide. **Don't guess the future. Simulate it first.** # How to Use Mirror to Simulate a Business Decision Learn how to use Mirror to turn a real business decision into an AI simulation, test market reactions, uncover risks, and review possible outcomes before acting. ## How to Use Mirror When You Have One Decision to Test You do not need a perfect dataset, a complicated forecasting model, or dozens of scenarios to start using Mirror. You need **one real decision worth testing**. It might be: * Should we launch this product? * Is this the right price? * Should we enter this market? * How might customers react to this positioning? * What happens if a competitor responds aggressively? * Could this announcement create reputational risk? Mirror turns that decision into a structured simulation where relevant actors, incentives, assumptions, and possible reactions can be explored before the decision reaches the real world. The goal is not to ask AI for an answer. The goal is to **rehearse the decision before you make it.** ### Use Mirror Like a Scenario Rehearsal, Not a Chatbot The easiest mistake when using an AI simulation platform is treating it like a traditional chatbot. A chatbot typically follows this pattern: **Question → Answer** Mirror is designed around a different workflow: **Decision → Context → Simulation → Reactions → Evidence → Decision** Instead of asking: **“Is this a good product launch?”** define the situation you actually want to test: **“How might our target customers and competitors react if we launch this product at $99 next month?”** The second question creates a bounded scenario. It tells the simulation: * what decision is being considered, * who may react, * what variable is changing, * and what outcome matters. That gives Mirror something useful to simulate. ## The First-Run Process For your first Mirror simulation, keep the process simple. Start with one decision and gradually improve the simulation as new information emerges. ### Step 1: Choose One Decision Window Start with one decision that has a clear boundary. Good simulation questions usually involve a specific action, audience, market, product, or time horizon. For example: **Weak question:** > What should our company do next? This is too broad. **Better question:** > How might existing customers react if we increase our subscription price by 20% next quarter? Or: > What reactions could emerge if we launch Product X in the UAE targeting premium customers? The more clearly the decision is defined, the easier it becomes to identify the actors and forces that could affect the outcome. #### A useful decision should answer four things **What are we considering doing?** **Who could react?** **What environment are we operating in?** **What outcome are we trying to understand?** You do not need perfect answers. You simply need enough boundaries for the simulation to represent the problem realistically. ## Step 2: Add the Decision Context Next, give Mirror the information required to understand the world around the decision. Depending on the simulation, useful context can include: * Product information * Market research * Customer profiles * Competitor analysis * Pricing information * Positioning * Launch strategy * Historical performance * Industry reports * Survey results * Public narratives * Internal assumptions * Policy documents * Strategic memos You do not need to upload everything your company knows. More information is not automatically better. The objective is to provide the **most relevant evidence for the decision being tested.** For a pricing simulation, for example, customer behavior and competitor pricing may matter much more than a 100-page corporate presentation. For a crisis simulation, stakeholder expectations, previous communications, public sentiment, and the proposed response may matter more. Think of the information as the boundaries of the simulated world. ## Step 3: Write the Simulation Question This is one of the most important steps. A good simulation question does not ask Mirror to simply provide advice. Instead, it defines an event and asks what may happen because of it. #### Product launch Instead of: > Should we launch this product? Try: > How might our target customer segments and major competitors react during the first 90 days if we launch this product with the proposed positioning and pricing? #### Pricing Instead of: > What is the best price? Try: > How might existing customers, prospective customers, and competitors respond if we increase the subscription price from $49 to $69? #### Market entry Instead of: > Should we enter Dubai? Try: > What customer, competitive, and market reactions could emerge if we enter the Dubai market with this offer and positioning? The principle is simple: **Simulate an action, not an abstract question.** ## Step 4: Review the Scenario Before Trusting the Forecast Do not jump directly to the final forecast. First inspect the structure of the scenario. Ask whether Mirror is considering the forces that actually matter. For example: #### Are the important actors present? A pricing simulation might need: * Existing customers * New prospects * Premium customers * Price-sensitive customers * Competitors * Sales teams * Distribution partners A public narrative simulation might instead need: * Customers * Media * Influencers * Communities * Employees * Investors * Regulators Different decisions require different actors. #### Are their incentives realistic? A competitor does not react like a customer. An investor does not respond like a journalist. A price-sensitive customer may behave differently from a loyal enterprise customer. The simulation becomes more useful when these incentives and constraints reflect the real decision environment. #### Is something important missing? Look for: * Missing customer segments * Hidden dependencies * Weak evidence * Unrepresented competitors * Regulatory constraints * Incorrect assumptions * External events that could influence behavior Finding a missing variable before the simulation is itself useful intelligence. ## Step 5: Run the Simulation and Inspect the Reactions Once the scenario is sufficiently structured, run the simulation. Mirror can then explore how different actors may respond to the proposed decision and how those reactions may influence one another. This is where multi-agent simulation becomes useful. A customer reaction may influence another customer. A competitor action may change market expectations. Media coverage may amplify a narrative. Negative feedback from one group may affect another stakeholder. These interactions can produce **second-order effects** that are easy to miss when analyzing a decision statically. Instead of looking only at: **Action → Outcome** Mirror helps explore: **Action → Reaction → Counter-reaction → Emerging outcome** ## Step 6: Read the Forecast as a Decision Map Do not look only for one headline prediction. The most valuable output is often the structure around the forecast. Look for: * Likely reaction patterns * Alternative scenarios * Important assumptions * Risks * Opportunities * Behavioral differences between actors * Evidence gaps * Unexpected reactions * High-impact variables * Areas of uncertainty The objective is not to discover a guaranteed future. It is to improve your understanding of the decision before committing to it. ## Give Mirror the Job You Need the Simulation to Do Different decisions require different types of analysis. Before running a simulation, decide what you actually want to learn. ### Reaction Scan Use Mirror to identify how different actors may initially respond. For example: > Simulate how our major customer segments may react to the proposed product launch and identify the strongest positive and negative reactions. This is useful when you are trying to discover responses you may not have considered. ### Risk Discovery Ask Mirror to focus on what could go wrong. For example: > Simulate the launch and identify reaction paths that could create adoption, reputation, pricing, or competitive risks. This turns the simulation into a form of strategic stress testing. ### Assumption Testing Every business plan contains assumptions. You may believe: * Customers will accept the price. * Competitors will not respond quickly. * Demand will continue growing. * A particular message will build trust. * One customer segment will behave like another. Mirror can help challenge those assumptions. For example: > Identify which assumptions in this market-entry strategy are most vulnerable when different stakeholders begin reacting. ### Scenario Comparison Sometimes the question is not whether to act. It is **which version of the decision is stronger**. For example: #### Scenario A Launch at $79. #### Scenario B Launch at $99 with premium positioning. #### Scenario C Launch at $99 with an introductory discount. Simulation can help reveal how changing one variable affects different actors and reaction paths. ## How to Know Whether a Mirror Run Was Useful A simulation does not need to perfectly predict reality to create value. A useful run should improve your decision. Ask: #### Did the simulation reveal a reaction we had not considered? Unexpected responses are often more valuable than confirmation of existing beliefs. #### Did it expose an important assumption? If the entire strategy depends on one uncertain assumption, that assumption should become a priority for validation. #### Did it identify missing evidence? The result might reveal that you need: * More customer research * Better competitor intelligence * Price sensitivity data * Regulatory information * Additional market evidence That is not a failure. Knowing **what you do not know** improves the next decision. #### Did it change what we would test next? A good simulation should often generate another experiment. For example: **Simulation** ↓ Customers appear highly price-sensitive. ↓ **Next test** Run three pricing scenarios. ↓ Premium positioning reduces price resistance. ↓ **Next test** Compare two positioning strategies. Simulation becomes an iterative decision process rather than a one-time report. ## Mistakes That Make Simulations Weaker The quality of the decision environment matters. Several common mistakes can reduce the usefulness of a simulation. ### Asking Questions That Are Too Broad Avoid: > What will happen to our company? Prefer: > How could our target customers respond if we increase pricing by 15% during the next renewal cycle? Specific decisions produce more interpretable scenarios. ### Providing Too Much Unrelated Information Uploading everything available can introduce irrelevant context. Choose evidence that affects the decision. **Focused context beats a messy archive.** ### Treating the Forecast as Certainty A simulated outcome is not a guaranteed prediction. Markets contain uncertainty. People change their minds. Competitors behave unexpectedly. External events intervene. Use the forecast as a structured hypothesis that can be examined and tested. ### Ignoring Alternative Scenarios Do not focus only on the most likely-looking path. Some lower-probability scenarios may have significantly larger consequences. A useful decision process considers both: **likelihood** and **impact.** ### Running One Simulation and Stopping The first simulation should often create the second. Change an assumption. Change the price. Change the audience. Remove a competitor. Add new evidence. Change the positioning. Then rerun the scenario. The objective is not to generate one perfect forecast. The objective is to progressively understand the decision space. ## A Simple Mirror Workflow A practical workflow looks like this: **1. Define the decision** ↓ **2. Add relevant evidence** ↓ **3. Identify the actors** ↓ **4. Define the simulation question** ↓ **5. Run the scenario** ↓ **6. Inspect reactions** ↓ **7. Review assumptions and risks** ↓ **8. Change one important variable** ↓ **9. Run another scenario** ↓ **10. Make a better-informed decision** This turns simulation into an iterative decision discipline. ## Questions Before Your First Simulation ### Do I need perfect data before using Mirror? No. You need enough relevant information to create a meaningful scenario. However, the quality of the simulation depends partly on the quality of the evidence and assumptions provided. When important evidence is missing, that uncertainty should be treated as part of the result rather than hidden. ### What should I simulate first? Start with a real decision that is: * Important enough to matter * Specific enough to define * Influenced by human or market reactions * Still possible to change Good first simulations include pricing changes, launches, positioning decisions, market entry, campaigns, or competitive moves. ### Is Mirror predicting the future with certainty? No. Mirror explores plausible futures based on the scenario, evidence, actors, and assumptions available to the simulation. Its purpose is not certainty. Its purpose is to help decision-makers identify reactions, risks, assumptions, and alternative outcomes before acting. ### How many scenarios should I run? There is no universal number. Start with your baseline scenario and then change the variables that appear most influential. For example: **Baseline:** $99 price **Scenario 2:** $79 price **Scenario 3:** $99 + premium positioning **Scenario 4:** competitor reduces price Comparing simulations is often more valuable than treating one run as the definitive answer. ### Can Mirror replace real-world testing? No. Simulation should complement real-world evidence, not replace it. Use Mirror to identify what deserves testing, which assumptions matter, and where risks may exist. Then validate important findings through customer interviews, market research, experiments, analytics, or other real-world evidence. ## The Best Simulation Starts With a Real Decision AI becomes more useful when it moves beyond answering abstract questions. A real decision gives the system something concrete to test. A proposed price. A product launch. A new market. A positioning strategy. A competitor move. A public announcement. The process is straightforward: **Define the decision.** **Provide the evidence.** **Simulate the reactions.** **Inspect the assumptions.** **Compare possible outcomes.** **Test what matters next.** The goal is not to eliminate uncertainty. It is to understand uncertainty **before it becomes expensive.** ## Test the Decision Before the Market Does Every consequential business decision eventually encounters reality. Customers react. Competitors respond. Markets change. Narratives spread. Assumptions break. Mirror gives teams an environment to explore those reactions before committing the decision to the outside world. **Don't wait for the market to run the experiment for you.** **Simulate it first.** # How Mirror Simulates the Future Learn how Mirror transforms real-world evidence into structured scenarios, AI agents, multi-round simulations, and decision forecasts you can inspect before acting. Mirror does not attempt to predict the future by generating one answer from one prompt. Instead, it builds a structured representation of a decision environment, identifies the actors and forces that matter, simulates how they may react over time, and turns those interactions into an inspectable decision forecast. The basic principle is simple: **Model the environment.**\ **Model the actors.**\ **Simulate the reactions.**\ **Inspect what emerges.** This allows teams to explore not only what might happen, but also **why it might happen, who could influence the outcome, and which assumptions could change the result.** ### Mirror Simulates by Making the Hidden Scenario Structure Visible Most strategic decisions contain an invisible system underneath them. Consider a product launch. The outcome may depend on: * Customer expectations * Price sensitivity * Competitor behavior * Market timing * Brand trust * Influencers * Distribution * Product positioning * Media coverage * Existing alternatives A normal AI prompt often compresses all of those factors into a single response. Mirror takes a different approach. It attempts to make the underlying decision environment explicit before interpreting the outcome. The objective is to expose: **Actors → Incentives → Relationships → Reactions → Outcomes** Once that structure becomes visible, the scenario can be simulated rather than simply described. ## An AI Scenario Simulation Turns Evidence Into a Reviewable Model of Reaction At a high level, a Mirror simulation can be understood through four components. | Component | What it represents | | ------------ | ------------------------------------------------------------------------------------------------------ | | **Input** | Evidence, assumptions, decision context, market information and the action being tested | | **Actors** | Customers, competitors, stakeholders, communities or other entities capable of influencing the outcome | | **Dynamics** | How actors interact, respond, adapt and influence one another | | **Output** | Plausible reaction paths, risks, assumptions, opportunities and decision-relevant insights | The important difference is the middle. Mirror does not simply transform: **Input → Answer** It explores: **Input → Actors → Interactions → Emerging Outcomes** That interaction layer is where simulation becomes useful. ## From Real-World Evidence to Simulated Reaction A Mirror simulation can be broken into five core stages. ### Step 1: Define the Scenario Every simulation starts with a decision. The user defines the action or change that should be tested. Examples include: > What happens if we increase our subscription price by 20%? > How might customers react if we launch this product in Dubai? > What happens if our main competitor releases a cheaper alternative? > How could different stakeholders respond to this announcement? The objective is not to describe an entire industry. It is to establish a **bounded decision environment**. A good scenario identifies: * The proposed action * Relevant audience * Market or environment * Important constraints * Time horizon * Decision objective This becomes the foundation of the simulation. ## Step 2: Identify Actors, Motives and Constraints Once the scenario is defined, Mirror identifies the actors capable of influencing the outcome. Depending on the simulation, these could include: * Existing customers * Prospective customers * Competitors * Investors * Employees * Partners * Regulators * Journalists * Influencers * Communities * Distribution channels * Market segments But identifying actors alone is not enough. Each actor exists within a set of incentives and constraints. A customer may care about: * Price * Trust * Convenience * Status * Switching costs * Product quality A competitor may care about: * Market share * Customer retention * Pricing pressure * Brand positioning * Strategic differentiation An investor may care about: * Growth * Risk * Margins * Market size * Execution capability Mirror attempts to model these differences because different incentives produce different reactions. ## Step 3: Build the Scenario Model The next stage connects the pieces. Mirror structures relationships between: * Actors * Claims * Incentives * Market forces * Evidence * Constraints * Dependencies * Potential reactions Think of this as a **scenario graph** or decision model. It makes relationships that may otherwise remain hidden easier to inspect. For example: **Price increase** ↓ Price-sensitive customers become more likely to churn ↓ Competitor gains an acquisition opportunity ↓ Competitor increases advertising ↓ Customer switching becomes easier ↓ Retention risk increases The important insight is not merely that a price increase could create churn. It is the **reaction chain** created after the original action. This is where simulation begins to reveal second-order effects. ## Step 4: Run Multi-Agent Interaction After the decision environment has been structured, Mirror can simulate how the actors respond. This is the multi-agent layer. Rather than asking one AI model to represent every perspective simultaneously, different agents can represent different participants or behavioral perspectives within the scenario. They react to: * The original decision * Available evidence * Their own incentives * Environmental conditions * Other actors * Previous reactions The simulation can continue through multiple rounds. For example: #### Round 1 A company increases its price. Customers react. #### Round 2 Competitors observe customer dissatisfaction and introduce promotions. #### Round 3 Customers compare alternatives. #### Round 4 The company changes messaging or offers incentives. #### Round 5 Different customer segments respond differently. The outcome is no longer generated from one isolated action. It emerges from interaction. This is why multi-agent simulation is particularly useful for situations where: **reaction changes the outcome.** ## Step 5: Convert Simulation Into a Decision Forecast The raw interaction between AI agents is not the final product. The objective is to convert simulated behavior into something a decision-maker can use. Mirror analyzes the simulation and surfaces patterns such as: * Most plausible reaction paths * Important disagreements * Adoption barriers * Competitive threats * Behavioral differences * Reputation risks * Price sensitivity * Trust signals * Opportunities * Critical assumptions * Missing evidence * High-impact uncertainties The resulting forecast is better understood as a **decision map** rather than a single prediction. It should help answer: **What might happen?** **Why might it happen?** **Who could drive the outcome?** **What could change the outcome?** **What should we test next?** ## The Scenario Model Is an Audit Surface One important advantage of structured simulation is inspectability. When an AI system provides only a final recommendation, it can be difficult to understand what produced the answer. A simulation should expose more of the decision structure. Before trusting the result, operators should be able to examine: * Which actors were included * Which actors were excluded * What relationships were modeled * Which incentives were important * What assumptions influenced the run * Which evidence supported those assumptions This makes the scenario model an **audit surface**. Instead of simply asking: > Do I believe the AI? the operator can ask: > Does the simulated world represent the real decision environment well enough? That is a much more useful question. ## Agent Rounds Expose Movement Static analysis is useful for understanding a situation at one point in time. Simulation adds another dimension: **movement.** Actors react. Other actors observe those reactions. Behavior changes. Information spreads. Competitors adapt. Trust increases or decreases. Narratives evolve. This creates a distinction between: **State analysis** and **dynamic simulation.** A normal market report may tell you that customers are price sensitive. A simulation can explore what happens after: 1. You increase the price. 2. Customers complain. 3. A competitor sees the opportunity. 4. The competitor lowers its price. 5. Online discussion amplifies the difference. 6. Your premium customers remain. 7. Price-sensitive customers begin switching. The sequence matters. Sometimes the most important outcome occurs several reactions after the initial decision. ## Why Multi-Round Simulation Matters Many strategic effects are second-order or third-order. The immediate reaction may not be the most important one. Consider a company launching an aggressive discount. #### First-order effect Customers respond positively. #### Second-order effect Competitors lower their prices. #### Third-order effect The entire category becomes more price-sensitive. #### Fourth-order effect Margins decline across the market. A single AI answer might recognize some of these possibilities. Multi-round simulation is specifically designed to explore how one reaction can create another. This allows Mirror to investigate **reaction paths**, not only direct consequences. ## A Useful Forecast Answers Bounded Questions A Mirror forecast should not attempt to answer everything. The strongest simulations focus on questions that can be connected to an actual decision. For example: #### What changed because of the decision? Which actors altered their behavior after the simulated action? #### Who reacted most strongly? Which customer groups, competitors or stakeholders were most sensitive? #### Which risks repeated across different paths? A risk that appears across multiple simulations may deserve more attention. #### Which assumptions influenced the outcome? What must be true for the simulated scenario to occur? #### Where does uncertainty remain highest? Which parts of the decision require more evidence? #### What should we test next? The simulation should lead toward another decision, experiment or evidence-gathering activity. ## Mirror Is Not a Single-Future Prediction Engine Complex systems rarely have one inevitable future. Market outcomes depend on: * Behavior * Timing * Competition * Information * External events * Incentives * Randomness * Decisions made by other participants For this reason, Mirror should not attempt to produce: > The future will be X. A better framing is: > Given these assumptions, actors and conditions, these are the reaction paths worth considering. That distinction matters. Simulation is most valuable when uncertainty remains visible. ## Scenario A, B and C Can Produce Different Futures Consider a company planning a new software product. The baseline offer is: **$99/month** Mirror could simulate multiple environments. #### Scenario A — Baseline Launch at $99. #### Scenario B — Lower Price Launch at $69. #### Scenario C — Premium Strategy Launch at $129 with stronger premium positioning. #### Scenario D — Competitive Response Launch at $99 while the largest competitor reduces its price by 25%. #### Scenario E — Stronger Social Proof Launch at $99 with significantly stronger customer evidence and testimonials. The purpose is not necessarily to identify one mathematically perfect answer. The objective is to understand: **Which variables materially change behavior?** That information can be extremely valuable before a real launch. ## Operator Checks Before Trusting a Simulation Human judgment remains an essential part of the workflow. Before making a decision based on a simulation, review the environment that produced it. ### Are the right actors represented? Missing an important stakeholder can distort the reaction path. ### Are actor incentives realistic? Agents should reflect plausible motivations rather than arbitrary personalities. ### Is the evidence relevant? More data does not necessarily mean better simulation. Relevant evidence matters more. ### Are assumptions visible? Hidden assumptions make forecasts difficult to evaluate. ### Are alternative scenarios considered? One simulation should not automatically become the decision. Test the variables that matter. ### Are uncertainty and evidence gaps visible? A strong decision system should expose uncertainty rather than disguise it. ## What Makes a Simulated Forecast Different From a Summary? A summary describes existing information. A simulation asks what may happen **after something changes**. For example, a summary might say: > Customers in this category are price sensitive and competitors offer cheaper alternatives. A simulation asks: > What could happen if this company increases its price by 20% while competitors maintain their current prices? The difference is: **Summary → What is known** **Simulation → What could emerge** Both are useful. They solve different problems. ## Simulation Is Not the Same as Prediction Prediction generally tries to estimate a future outcome. Simulation explores the system that could produce different outcomes. That means Mirror is interested not only in: **the result** but also: **the path to the result.** For strategic decision-making, that path can be as valuable as the final forecast. Knowing that demand might fall is useful. Understanding that demand might fall because price-sensitive customers move first, competitors amplify the price difference, and social proof deteriorates gives a team significantly more information about what it could do next. ## When Should You Run Another Simulation? Run another scenario when you discover a variable capable of materially changing the outcome. Examples include: * Different price * Different launch timing * Different positioning * Different customer segment * New competitor * New market condition * Different message * Stronger evidence * Regulatory change * Different distribution strategy A useful process might look like: **Simulation 1** ↓ Discover critical assumption ↓ **Simulation 2** Change that assumption ↓ Discover sensitive actor ↓ **Simulation 3** Change that actor's environment ↓ Compare outcomes This turns simulation into iterative decision exploration. ## Questions About the Mirror Simulation Engine ### Is Mirror predicting the future with certainty? No. Mirror generates plausible reaction paths based on the scenario, evidence, actors and assumptions available to the system. Its purpose is to help decision-makers understand possible outcomes and uncertainty—not claim certainty about future events. ### What makes Mirror different from a normal AI analysis? Traditional AI analysis generally evaluates information and generates an answer. Mirror creates a structured scenario, models relevant actors and incentives, and explores how reactions may evolve through interaction. This allows teams to investigate dynamic behavior rather than only static information. ### Why does the scenario structure matter? Because the quality of a simulation depends on what the system believes exists inside the simulated world. If important actors, incentives, constraints or relationships are missing, important reaction paths may also be missing. Making the structure inspectable allows operators to challenge the simulation before relying on its output. ### Why do multiple agent rounds matter? Many outcomes emerge through chains of reaction. Customers respond to companies. Competitors respond to customers. Media responds to controversy. Markets respond to competitors. Multiple simulation rounds help reveal these second-order and higher-order effects. ### Does Mirror replace traditional forecasting models? No. Statistical forecasting remains highly valuable for problems with strong historical datasets and measurable patterns. Mirror is designed for a complementary class of problems where **behavior, interaction and strategic reaction influence the outcome.** ### When should I run another simulation? Run another simulation whenever an important assumption, variable or actor could materially change the outcome. Comparing scenarios is generally more useful than assuming a single simulation represents the future. ## From AI Answers to Simulated Worlds Generative AI made it possible to ask increasingly sophisticated questions. Simulation represents the next step. Instead of only asking an AI system: > What should we do? teams can create a representation of the environment and ask: > What might happen if we do this? Then change the conditions. Run it again. Compare the outcomes. Challenge the assumptions. Gather additional evidence. Repeat. This transforms AI from a system that produces answers into an environment for **testing decisions**. ## Simulate the Reaction Before Making the Decision Every strategic decision eventually enters a system filled with other actors. Customers respond. Competitors adapt. Investors reassess. Communities discuss. Markets move. Narratives change. The decision itself is only the beginning. Mirror is designed to explore what may happen **after the decision enters that system**. **Model the environment.** **Simulate the actors.** **Observe the reactions.** **Inspect the assumptions.** **Compare the possible paths.** Then make the decision with a better understanding of what could happen next. **Don't just predict the future. Simulate how it could unfold.** # What Is Multi-Agent Simulation? Learn what multi-agent simulation is, how AI agents interact across scenarios, and how Mirror uses multi-agent AI to explore market reactions, risks, and business decisions. Multi-agent simulation is a method for modeling complex systems by representing different participants as separate agents that can observe, react, adapt, and influence one another over time. Instead of asking an AI model to produce one answer about what might happen, a multi-agent simulation asks a different question: **What happens when multiple actors begin reacting to the same event—and to each other?** Those actors might represent: * Customers * Competitors * Investors * Employees * Regulators * Journalists * Influencers * Communities * Partners * Market segments * Other stakeholders Each agent can represent a different perspective, objective, incentive, constraint, or behavioral pattern within the simulated environment. The result is not simply an AI-generated opinion. It is a **dynamic scenario where reactions can create additional reactions.** ### Multi-Agent Simulation Is Useful When Interaction Changes the Outcome Many real-world decisions cannot be understood by analyzing each participant independently. Imagine a company increases the price of its product. The immediate effect might seem straightforward: **Company raises price → customers react.** But the real market may behave differently. Customers complain. Competitors notice. A competitor launches a discount. Customers begin comparing alternatives. Online discussion amplifies the price difference. The company changes its messaging. Some customers leave. Others perceive the higher price as a premium signal. The final outcome emerges from the interaction between all of these actors. That means the real structure is closer to: **Decision** ↓ **Reaction** ↓ **Reaction to the reaction** ↓ **Adaptation** ↓ **New market behavior** Multi-agent simulation is designed to explore this kind of system. ## Multi-Agent Simulation Represents Participants as Separate Actors At the core of a multi-agent simulation are the agents themselves. An agent represents a participant or behavioral perspective inside the scenario. Each agent may be defined by characteristics such as: * Goals * Incentives * Preferences * Constraints * Available information * Relationships * Behavioral tendencies * Memory of previous interactions The agents do not all behave identically. That distinction is essential. A price-sensitive customer should not necessarily respond like a premium customer. A competitor should not reason like an investor. A journalist may respond to a corporate announcement differently from an employee. A regulator operates under entirely different incentives from a consumer. By separating these perspectives, simulation can represent **heterogeneous behavior** rather than averaging everyone into one generic response. ## Goals, Incentives and Constraints Matter An AI agent becomes more useful when its behavior is grounded in the scenario. Consider four actors reacting to the same product launch. #### Customer Potential goals: * Get value for money * Reduce risk * Solve a problem * Compare alternatives Potential constraints: * Budget * Switching costs * Lack of information * Existing contracts #### Competitor Potential goals: * Protect market share * Retain customers * Undermine differentiation * Defend pricing Potential constraints: * Product roadmap * Margins * Brand positioning * Organizational speed #### Investor Potential goals: * Evaluate growth * Understand risk * Assess market opportunity * Protect returns Potential constraints: * Capital * Time horizon * Portfolio strategy #### Media Potential goals or tendencies: * Identify significant developments * Find conflict or novelty * Explain implications * Follow audience interest The same event can therefore produce very different reactions. That diversity is one of the core advantages of multi-agent simulation. ## Agents Can Remember What Happened Earlier Multi-round simulation becomes much more meaningful when agents respond to previous events rather than restarting from zero every time. Consider a simplified scenario. #### Round 1 A company launches a product at $99. #### Round 2 Customers criticize the price. #### Round 3 A competitor launches a $69 alternative. #### Round 4 Customers compare both products. #### Round 5 The original company adds premium features. #### Round 6 Different customer segments reassess the offer. The reaction in Round 6 depends partly on what happened in Rounds 1–5. This introduces an important property: **state.** The simulated environment evolves. Participants react not only to the original decision but also to the history created by the simulation. ## Why People Use Multi-Agent Simulation Multi-agent simulation can be useful whenever an outcome depends heavily on interaction. ### Product Launches A launch involves much more than customers deciding whether they like a product. Possible actors include: * Buyers * Competitors * Reviewers * Influencers * Sales teams * Distribution partners * Existing users Their reactions can reinforce or weaken one another. ### Pricing Decisions Pricing can trigger: * Customer churn * Competitor discounts * Premium perception * Switching behavior * Sales objections * Changes in acquisition economics A pricing decision therefore creates a system of reactions. ### Market Entry Entering a new market may trigger responses from: * Local competitors * Customers * Partners * Regulators * Media * Incumbent brands Multi-agent simulation can help teams explore how those reactions interact. ### Public Opinion and Narrative Spread Public narratives rarely move in a straight line. Different communities interpret information differently. Some groups amplify a message. Others reject it. Influencers reframe it. Media adds another interpretation. Multi-agent simulation provides a way to examine how narratives might evolve across different actors. ### Policy Impact Policy changes frequently affect multiple stakeholder groups with conflicting incentives. Simulation can help explore potential reactions from: * Citizens * Businesses * Regulators * Industry organizations * Investors * Communities ### Crisis Communication During a crisis, one response can trigger another. A company statement may affect customers. Customer reactions may attract media coverage. Media coverage may influence investors. Employee responses may create additional attention. This makes crisis communication a natural candidate for scenario simulation. ## Multi-Agent Simulation Is Different From One-Shot AI Output This distinction is important. A traditional AI interaction often looks like: **Prompt → Model → Answer** For example: > How might customers react to our price increase? The model generates one synthesized response. Multi-agent simulation follows a different structure: **Scenario** ↓ **Multiple actors** ↓ **Independent reactions** ↓ **Actor-to-actor interaction** ↓ **Multiple rounds** ↓ **Emerging patterns** ↓ **Forecast** Consider the difference: | | One-Shot AI | Multi-Agent Simulation | | --------------- | --------------------------------- | -------------------------------------------- | | **Question** | What could happen? | What could emerge as different actors react? | | **Actors** | Often combined into one response | Represented as separate perspectives | | **Interaction** | Limited | Actors can influence one another | | **Time** | Mostly static | Can evolve across rounds | | **Output** | One synthesized answer | Reaction paths and emerging patterns | | **Best for** | Research, analysis, brainstorming | Dynamic scenarios and strategic decisions | Neither approach is universally better. They solve different classes of problems. ## When One-Shot AI Is Enough Multi-agent simulation should not be used simply because it is more sophisticated. If you need to: * Summarize a report * Explain a concept * Rewrite content * Brainstorm ideas * Extract information * Analyze a simple question a normal AI model may be completely sufficient. Simulation becomes valuable when: **the behavior of one actor changes what another actor is likely to do.** That is the key threshold. ## Multi-Agent Simulation vs Agent-Based Modeling The terms are related but should not always be treated as identical. Traditional **agent-based modeling (ABM)** has been used for decades in fields including: * Economics * Epidemiology * Transportation * Ecology * Sociology * Urban planning Traditional ABM often defines explicit mathematical or programmatic rules. For example: > If price increases above threshold X, agent type A has probability Y of leaving. Large numbers of agents then interact according to those rules. AI-powered multi-agent simulation can introduce a different mechanism. Agents may use language models to interpret: * Natural-language evidence * Complex motivations * Qualitative context * Social signals * Previous interactions * Unstructured information This allows simulated actors to respond to scenarios that are difficult to express entirely through fixed rules. The two approaches can also complement each other. ## Multi-Agent Simulation vs Multi-Agent AI Systems There is another important distinction. A **multi-agent AI system** does not necessarily simulate a market or social environment. For example, one AI agent could research information. Another could write code. Another could review the code. Another could create documentation. That is still a multi-agent system. But the agents are collaborating to complete a task. In a **multi-agent simulation**, agents instead represent participants or forces within a simulated environment. The purpose is to observe what emerges from their interaction. So: **Multi-Agent Workflow** Agents collaborate to complete work. **Multi-Agent Simulation** Agents interact to explore system behavior. Mirror primarily applies multi-agent techniques to the second problem: **decision and scenario simulation.** ## How Mirror Uses Multi-Agent Simulation Mirror turns multi-agent simulation into a structured decision workflow. The process can be understood through three major stages. ### 1. Build the Actor Model Mirror starts by identifying the participants that matter to the decision. Depending on the scenario, the system may represent: * Customer segments * Competitors * Stakeholders * Communities * Investors * Media * Regulators * Partners The important question is not: > How many agents can we create? It is: > Which actors are capable of materially changing the outcome? More agents do not automatically create a better simulation. Relevant actors matter more than raw agent count. ## 2. Run Reaction Rounds Once the actors and scenario are defined, interaction can begin. Each round creates a new state of the simulated environment. A simplified sequence might look like this: #### Initial event Company launches Product X. #### Agent reactions Customers evaluate it. Competitors respond. Influencers discuss it. #### Environmental change Customer sentiment shifts. Competitor positioning changes. More information becomes available. #### Next agent round Agents react again based on the updated environment. This process allows the system to search for: * Reinforcing behavior * Conflict * Coalition formation * Adoption patterns * Resistance * Narrative spread * Competitive responses * Behavioral divergence ## 3. Review the Forecast The raw conversation between agents is not necessarily useful on its own. Mirror translates the simulation into a decision-oriented forecast. The operator should be able to inspect: * Major reaction paths * Strongest objections * Repeated patterns * Areas of agreement * Areas of disagreement * Critical actors * Risks * Opportunities * Assumptions * Evidence gaps * Uncertainty The value comes from transforming complex interaction into something a human decision-maker can review. ## Multi-Agent Simulation Can Reveal Second-Order Effects One of the most important benefits of simulation is identifying consequences beyond the immediate response. Consider this scenario: **A SaaS company reduces its price by 30%.** The obvious prediction might be: > More customers will buy. But simulation might expose a longer chain. #### First-order effect Demand increases. #### Second-order effect Competitors lower prices. #### Third-order effect Customers begin evaluating the entire category primarily on price. #### Fourth-order effect Premium positioning becomes harder to maintain. #### Fifth-order effect Customer acquisition increases but margins deteriorate. The first effect looked positive. The later effects may change the strategic conclusion. That is why second-order reasoning matters. ## Emergent Behavior Is the Important Part In complex systems, outcomes can emerge that were not explicitly programmed as the target result. This is called **emergent behavior**. For example, no individual simulated participant may intend to create a negative narrative. But: * Some customers complain. * An influencer notices. * A community amplifies the complaint. * Media begins reporting it. * Competitors use the criticism in positioning. Collectively, these independent reactions can create a larger narrative. The result emerges from the system. This type of behavior is one reason multi-agent simulation can be useful for studying: * Markets * Social dynamics * Public opinion * Competitive strategy * Adoption * Reputation * Organizational behavior ## The Actor Model Is an Audit Surface A simulation becomes easier to evaluate when the user can inspect who is inside it. Before trusting a run, review the actor structure. Ask: #### Are the important actors present? If a major competitor is missing, competitive reaction may be underestimated. #### Are actors differentiated enough? Treating all customers as one identical group can hide meaningful behavior. #### Are the incentives realistic? An agent should respond according to plausible motivations and constraints. #### Are relationships represented correctly? Some actors influence others more strongly. An industry analyst may have greater narrative influence than an average customer. #### Is important evidence missing? Agents grounded in weak or incomplete information can produce weak simulations. The actor model should therefore be something operators can challenge. ## More Agents Do Not Automatically Mean Better Simulation It can be tempting to assume: **100 agents > 10 agents.** That is not necessarily true. The quality of a simulation depends on factors such as: * Actor relevance * Behavioral differentiation * Evidence quality * Scenario design * Interaction structure * Memory * Constraints * Model quality * Number of rounds Ten well-defined actors can sometimes reveal more than hundreds of poorly differentiated agents. The objective should be **representative behavior**, not maximum agent count. ## A Practical Example: Product Launch Simulation Suppose a company wants to launch a new AI productivity tool. The proposed price is: **$29/month** Mirror could create several actor groups. #### Early adopters Interested in innovation and willing to tolerate imperfect features. #### Price-sensitive users Interested in productivity but resistant to another subscription. #### Enterprise buyers Concerned about security, reliability, integration, and compliance. #### Existing competitors Interested in protecting users and positioning. #### Technology influencers Interested in novelty, differentiation, and demonstrable results. Now introduce the launch. #### Round 1 Early adopters experiment with the product. Enterprise buyers ask about security. Price-sensitive users compare alternatives. #### Round 2 Influencers publish initial reactions. Competitors emphasize existing integrations. #### Round 3 Positive influencer coverage increases awareness. Enterprise security concerns become a recurring objection. #### Round 4 The company improves security messaging and documentation. #### Round 5 Enterprise sentiment changes, while price-sensitive users remain unconvinced. The simulation could reveal that: **security evidence is a bigger launch constraint than price.** That finding gives the team something concrete to test before spending heavily on acquisition. ## Prompt Template for a Multi-Agent Simulation A simple structure can help define the scenario: > Simulate how relevant actors may respond if **\[organization]** takes **\[action]** in **\[market/environment]**. Represent the major stakeholder groups separately, account for their different incentives and constraints, allow reactions to influence subsequent rounds, identify second-order effects, and summarize the major outcomes, assumptions, risks, uncertainties, and evidence gaps. For example: > Simulate how customers, competitors, influencers, and enterprise buyers may respond if an AI productivity company launches a new product at $29 per month in the US market. Run multiple reaction rounds and identify adoption drivers, objections, competitive responses, second-order effects, and the assumptions most likely to change the outcome. The scenario becomes significantly more useful when the decision is specific. ## Questions About Multi-Agent Simulation ### What is multi-agent simulation? Multi-agent simulation models a system using multiple autonomous or semi-autonomous agents representing different participants, perspectives, or behavioral groups. Those agents interact within a shared environment, allowing researchers or decision-makers to explore how collective outcomes may emerge from individual reactions. ### How is multi-agent simulation different from one-shot AI output? One-shot AI generates a response from a single interaction. Multi-agent simulation models several actors separately and allows their reactions to influence subsequent reactions. This makes it more appropriate for dynamic problems where interaction affects the outcome. ### When is multi-agent simulation useful? It is particularly useful when decisions involve multiple participants with different incentives. Examples include: * Market launches * Pricing strategy * Competitive response * Public opinion * Crisis communication * Policy analysis * Market entry * Organizational behavior ### Is multi-agent simulation the same as agent-based modeling? They overlap but are not always identical. Traditional agent-based modeling often uses explicitly programmed behavioral rules. AI-driven multi-agent simulation can use language models and unstructured evidence to represent more complex qualitative behavior. Hybrid approaches can combine both. ### Is multi-agent simulation the same as an AI agent workflow? No. A multi-agent workflow typically uses specialized agents to collaboratively complete a task. Multi-agent simulation uses agents to represent actors inside a simulated system so that their interactions can be observed. ### Can AI agents perfectly reproduce human behavior? No. AI agents are models of possible behavior. They do not perfectly reproduce individuals or guarantee how real people will react. Simulation should therefore be treated as a structured method for exploring plausible behavior, not as exact human prediction. ### How does Mirror use multi-agent simulation? Mirror uses multiple simulated actors to represent relevant participants within a decision environment. Those actors can react across multiple rounds, allowing Mirror to identify interaction patterns, second-order effects, risks, assumptions, uncertainties, and potential outcomes. The resulting simulation is converted into a decision-oriented forecast for human review. ### How many agents should a simulation have? There is no universal ideal number. The appropriate number depends on the complexity of the decision and the number of meaningfully different actor groups. A smaller group of well-defined agents can be more useful than a much larger group of poorly differentiated agents. ### Can multi-agent simulation predict the future? Not with certainty. Multi-agent simulation explores plausible outcomes under defined assumptions and conditions. Its purpose is to improve understanding of how a system might behave—not determine one guaranteed future. ## From Individual AI Agents to Collective Behavior The most interesting part of multi-agent simulation is not the intelligence of any individual agent. It is what emerges when those agents interact. One participant reacts. Another adapts. A third amplifies the reaction. The environment changes. Agents respond again. Over time, the system may produce patterns that are difficult to identify through static analysis alone. That is why multi-agent simulation is becoming relevant to a broader shift in AI: from systems that merely **answer questions** to systems that can **model interactions**. ## Mirror Uses AI Agents to Test Decisions, Not Replace Decision-Makers The purpose of Mirror is not to delegate strategic judgment to autonomous agents. It is to provide decision-makers with another environment in which hypotheses can be tested. A simulation can reveal: * reactions you did not anticipate, * assumptions you should challenge, * stakeholders you overlooked, * evidence you still need, * and scenarios worth testing before committing resources. Human operators remain responsible for interpreting the results. The simulation expands the decision space. It does not eliminate judgment. ## When Interaction Changes the Outcome, Simulate the Interaction Many important business decisions are not isolated events. They enter systems. A product launch enters a competitive market. A price change alters customer behavior. A public statement enters an information network. A policy change creates stakeholder responses. A market-entry decision causes incumbents to adapt. The final outcome emerges from those interactions. That is the fundamental idea behind multi-agent simulation. **Model the actors.** **Define their incentives.** **Let them react.** **Observe what emerges.** **Challenge the assumptions.** Then use the resulting evidence to make a better-informed decision. **When reaction changes the outcome, simulate the reaction.** # FAQ #### Is Mirror an AI forecasting platform? Yes. Mirror is an AI simulation and forecasting platform designed to help teams explore how markets, customers, competitors, stakeholders, and other actors may react to a decision. Rather than producing a single prediction, Mirror uses scenario simulation to surface plausible outcomes, risks, assumptions, and alternative paths. #### Is Mirror a multi-agent simulation platform? Yes. Mirror uses multi-agent simulation to model how different actors and perspectives may respond within a scenario. Agents can interact across multiple rounds, helping reveal second-order effects, conflicting incentives, behavioral patterns, and reactions that may not appear in a traditional one-shot AI analysis. #### How is Mirror different from ChatGPT or other AI chatbots? Chatbots are primarily designed to answer questions, summarize information, generate content, and reason from a prompt. Mirror is designed to **simulate what may happen after a decision is made**. Instead of asking only: **“What do you think will happen?”** Mirror allows teams to explore: **“How might different actors react, how could those reactions influence each other, and what outcomes could emerge?”** #### What can I simulate with Mirror? Mirror can be used for scenarios where human or market reactions influence the outcome, including: * Product launches * Pricing decisions * Market entry * Customer demand * Competitive strategy * Go-to-market decisions * Public opinion * Brand narratives * Crisis communication * Policy impact * Strategic business decisions The most useful simulations start with a clearly defined decision or hypothesis. #### What information should I provide to Mirror? Provide information relevant to the decision you want to test. Depending on the scenario, this may include: * Product briefs * Market research * Customer insights * Pricing information * Competitor intelligence * Business plans * Research reports * Launch strategies * Policy documents * Public narratives * Internal assumptions A focused scenario generally produces more useful results than uploading large amounts of unrelated information. #### Can Mirror predict the future? No AI system can predict complex human behavior or market outcomes with certainty. Mirror should be treated as a **decision simulation system**, not an oracle. Its purpose is to identify plausible scenarios, reactions, assumptions, risks, and evidence gaps so teams can make better-informed decisions before committing resources in the real world. #### How should I evaluate a Mirror simulation? Before acting on a simulation, review several factors: * Are the important actors represented? * Are their incentives realistic? * Are important assumptions visible? * Is the underlying evidence sufficient? * Are alternative scenarios considered? * Where is uncertainty highest? * Does the simulation reveal a clearer next action? A strong simulation should make the decision easier to examine—not hide uncertainty. #### Who should use Mirror? Mirror is useful for teams making decisions where reactions, competition, uncertainty, or multiple stakeholders can affect the outcome. Typical users may include: **Founders, product teams, marketers, strategists, market researchers, consultants, investors, communications teams, and business leaders.** Mirror is particularly useful before decisions that are expensive, difficult to reverse, or exposed to significant market uncertainty. #### Is Mirror a replacement for market research? No. Mirror is better viewed as a complementary decision-intelligence layer. Traditional market research provides evidence about customers, markets, competitors, and historical behavior. Mirror can use that evidence to explore hypothetical scenarios and simulate how different actors might respond to a future decision. The stronger the underlying evidence, the more useful the simulation can become. #### When should I use Mirror instead of a traditional forecast? Traditional forecasting is particularly effective when reliable historical data can be used to estimate future outcomes. Mirror becomes useful when the outcome depends heavily on **interaction and behavioral response**—for example, how customers respond to a price change, how competitors react to a launch, or how a narrative evolves after entering the public market. In those situations, the question is not only: **“What is likely to happen?”** but: **“What happens when everyone starts reacting?”** *** # About App.monnfts.com Monnfts helps creators mint and auction their digital artworks as NFTs on the BSC blockchain. As collectors bid, they fuel a new system of value for online expression. Core businesses of MONNFTS include: investing in top NFT platforms and artworks, incubating leading NFT artists to build a bridge between world-class artists and the NFT world; sponsoring galleries, organizing art exhibitions or publications, and setting up awards to support art creation and criticism; establishing art collections, etc. By applying blockchain technology into the NFT world, MONNFTS Fund expands the use cases of blockchain. The Fund purchases well-known traditional and NFT artworks as underlying assets, and works to improve the NFT trading standard. It provides support to government agencies, universities, and industry elites in issuing policies that govern the healthy development of the industry. There are now three major use cases in the blockchain industry, namely value exchange, collaboration, and record-keeping. The unique NFT fits in all three of the use cases. It is not only a hot trend in the blockchain industry but also a critical element that is very likely to revolutionize the industry and take it to the next level. As a blockchain enterprise, MONNFTS will invest in artworks and promote traditional and digital art by leveraging its strengths in technology, market, industry and policy research, with the aim of introducing blockchain into diverse use cases. Our team is founded by a passionate group of veteran technologists and creators who’ve seen first hand the inequality in the immense value created by digital creators, and what they actually make. Our goal is to change that. **The MONNFTS Token (MON) on App** Monnfts is a creator-centric NFT marketplace and issuance platform that utilizes the MONNFTS token (MON) to empower users who actively interact with the protocol. Users on the Monnfts platform can easily create non-fungible tokens (NFTs) for unique digital items like artworks. The platform offers users a fully-featured marketplace that is filtered and sorted using different categories to create a smooth and easy user experience. Anyone can create and post NFTs on the Monnfts marketplace. The MON token is a fundamental pillar of the platform as it is used as the default currency to buy/sell NFTs and for participating in the governance of the protocol. # NFT Glossary #### N**on-fungible token (NFT)** “**Fungibility**” means that units of a currency or commodity are alike and indistinguishable. Examples of fungible currencies are $1 bills, each of which is alike and represents the same value. On the other hand, non-fungible tokens represent unique assets whose value is independent from one another. For example, an NFT might represent a piece of unique digital artwork, a Mickey Mantle baseball card, or a share of physical North Carolina real estate. Despite this difference, NFTs can be exchanged in the same manner as any other token on a cryptonetwork. The ability to represent unique assets greatly enhances the composability and functionality of cryptonetworks, since many real-world assets are non-fungible. In turn, this enables blockchains to support more flexible economies. #### ERC721 Pioneered by CryptoKitties, ERC721 was the first standard for representing non-fungible digital assets. ERC721 is an inheritable Solidity smart contract standard, meaning that developers can easily create new ERC721-compliant contracts by importing it from the OpenZeppelin library (we have a helpful tutorial on creating your first ERC721 contract here). ERC721 is actually relatively simple: it provides a mapping of unique identifiers (each of which represents a single asset) to addresses, which represent the owner of that identifier. ERC721 also provides a permissioned way to transfer these assets, using the transferFrom method. If you think about it, these two methods are really all you need to represent an NFT: a way to check who owns what and a way to move things around. There are a few other bells and whistles to the standard (some of which turn out to be very important for NFT marketplaces), but the core of ERC721 is quite basic. #### ERC1155 ERC1155, pioneered by the Enjin team, brings the idea of semi-fungibility to the NFT world. With ERC1155, IDs represent not single assets but classes of assets. For example, an ID might represent “swords”, and a wallet could own 1,000 of these swords. In this case, the balanceOf method would return the number of swords owned by a wallet, and a user can transfer any number of these swords by calling transferFrom with the “sword” ID. One advantage of this type of system is efficiency: with ERC721, if a user wanted to transfer 1,000 swords, they would need to modify the smart contract’s state (by calling the transferFrom method) for 1,000 unique tokens. With ERC1155, the developer need only call transferFrom with quantity 1,000 and perform a single transfer operation. This increased efficiency, of course, comes with the loss of information: we can no longer trace the history of an individual sword. Note also that ERC1155 provides a superset of ERC721 functionality, meaning that an ERC721 asset could be built using ERC1155 (you’d simply have a separate ID and quantity 1 for each asset). Due to these advantages, we’ve recently witnessed the growing adoption of the ERC1155 standard. OpenSea recently developed a repository on Github for getting started with the ERC1155 standard.Anatomy of the ERC20, ERC721, and ERC1155 standards. ERC20 maps addresses to amounts, ERC721 maps unique IDs to owners, and ERC1155 has a nested mapping of IDs to owners to amounts. **Crypto Art** It uses blockchain technology to serve or create digital works of art. There are two different directions for the combination of blockchain and Art: the first is the digitization and mapping of traditional works of art on the chain. Blockchain technology is used to solve the problem of copyright and traceability of works of art, which belongs to the internal application of the industry. The other is the original encryption art based on the public chain. Such works are launched on the chain in the form of NFT token, and are curated, distributed and auctioned through the virtual space and trading platform of the public chain. **IPFS** IPFS is a distributed file system for storing and accessing files, websites, applications and data. Most NFT files such as pictures, music and videos are stored through IPFs protocol. **Royalty** All NFTs automatically pay royalties to their creators when they are sold. It's still a developing concept, but it's one of the most powerful. Every time NFT is sold, the original owner of Euler beats origins gets an 5% royalty. Some platforms, such as foundation and Zora, support collecting royalties from their artists. # Monnfts trading fees **Transaction fee** Monnfts's model is simple - we take 2,5% of every transaction that happens on Monnfts. That's it. **About royalty** You can set a royalty. This means you can earn revenue every time your NFT sells on Monnfts. **Mint NFT** Users and partners can mint NFTs for low fees at any time. # How To Mint NFT The process is straightforward. The AI will take care of all the technical aspects of it, so don’t worry about how it’s actually minted. 1. First, go to [app.monnfts.com](https://app.monnfts.com) and navigate the top right bar – click on Connect. A new page will appear, prompting you to connect your wallet. Choose MetaMask. ![connect your wallet](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2FHSTHy8iXkgG2VXEnq2y8%2FScreen%20Shot%202022-04-05%20at%202.30.47%20PM.png?alt=media\&token=3c81225f-3b06-4272-8293-a13f1ddef8c8) 2\. Find and Click “Create” in the menu bar. ![Click “Create”](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2FIKGodqmOZiOOzxgN8mEH%2FScreen%20Shot%202022-04-05%20at%202.36.48%20PM.png?alt=media\&token=83d62e0b-d3ed-4702-9101-a50c2a686d88) 3\. Upload a new piece of art this can range from images, audio, or 3D models. ![Choose file](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2FwMdRxz5fci7cE0jJsvRn%2FScreen%20Shot%202022-04-05%20at%202.36.56%20PM.png?alt=media\&token=2515049c-a361-42a1-9089-3bd7e41d187b) 4\. Add details to your item: name, a description and choose Category. ![Add details to your item](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2FJxCbk7vo41XQp351dm1U%2FScreen%20Shot%202022-04-05%20at%202.39.36%20PM.png?alt=media\&token=0e1b6ca0-cac9-49c5-a91c-8b743f6f397c) 5\. Add collection for the NFT: \+ Create a new collection: complete all required data such as Logo image, Banner image, name, description then click Create. ![Create a new collection](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2FxroRqzJEyQIax9Ot3hvI%2FScreen%20Shot%202022-04-05%20at%202.40.49%20PM.png?alt=media\&token=6f7be8f6-4dda-4f0e-81e9-5e5bd2c65dc8) \+ Choose an already existing collection ![](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2FYIj2QgtZOQUlvfwOSHCr%2FScreen%20Shot%202022-04-05%20at%202.42.05%20PM.png?alt=media\&token=72da02ca-2a49-4a78-a48d-c3824ad66d62) 6\. Set price and Click Create: Notice: the platform service fee will be 2.5% of the total price \+ When the Metamask window pops up, click the Confirm button in the contract for: Mint token ![](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2F2XCCasaMGiBBRsxL2NtG%2FScreen%20Shot%202022-04-05%20at%202.47.00%20PM.png?alt=media\&token=a01daf1f-cc10-4fbc-bc82-876a22556b2d) \+ Set approval for all ![](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2F2JasdHzpVUNHgKel1mxG%2FScreen%20Shot%202022-04-05%20at%202.48.37%20PM.png?alt=media\&token=2e656cb1-56ec-49d5-aebb-abac13354576) \+ Create sell order ![](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2Fsv44VuCT8wTH7K2SEYPl%2FScreen%20Shot%202022-04-05%20at%202.48.45%20PM.png?alt=media\&token=784004a3-6a7f-4ebc-82e8-4a1042183810) 7\. Edit profile: choose an avatar, cover image, write a name, description, and contacts…and then click the Update profile button ![Hit Profile](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2FvWZb7nG8cx1TeH1Gr0ru%2FScreen%20Shot%202022-04-05%20at%202.50.35%20PM.png?alt=media\&token=320091b7-068c-408f-8b43-a17d40e955a6) ![Click Edit profile](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2FA0hGTYTGqZ7iPzjnyJfb%2FScreen%20Shot%202022-04-05%20at%202.50.46%20PM.png?alt=media\&token=c13cdb63-57e4-41cf-aa14-49c005362fc1) ![Set Avatar and Set Image Cover](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2FgrefcWJBQfjmIBOgR451%2FScreen%20Shot%202022-04-05%20at%202.50.56%20PM.png?alt=media\&token=5f47b3ef-1767-43c3-85b5-083f733fba69) # Royalty **How does Royalty work in Monnfts?** Whenever you create an NFT you can set a certain percentage as royalty for secondary sales. For example, you create an NFT and sell it for 0.2 BNB and set a royalty at 10%. When it comes to secondary sales, your buyer then sells your NFT at a higher price point for 0.5 BNB. Here, the royalty system kicks in. As the original content creator, you receive 10% of that sale, being 0.05 BNB. When the creator(A) sends/transfers an NFT to B, A will receive a royalty of 10% of the NFT if B sells the NFT to C. **How to set royalties？** 1.Enter App.monnfts.com, find and Click “Create” in the menu bar. ![](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-Mio1-rDAL3IsoQDatd4%2F-Mk_HYcAOy2SMNaxrtGY%2F-Mk_Nznk84S6H-6Jk_g1%2F01.jpg?alt=media\&token=e5af691d-f3f2-4023-9cbc-abd8d2d942c8) 2.Click on icon User, find to the NFT information. Click button Royalty and fill in the royalty. ![](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-Mio1-rDAL3IsoQDatd4%2F-Mk_HYcAOy2SMNaxrtGY%2F-Mk_O8gEfF57HsmererX%2F02.jpg?alt=media\&token=80af00fb-23d3-4489-a832-dfbbb3e00481) ![](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-Mio1-rDAL3IsoQDatd4%2F-Mk_HYcAOy2SMNaxrtGY%2F-Mk_ONSadwmg7ohUxtBG%2F03.jpg?alt=media\&token=3decc0ba-8e98-4a75-a4b9-f2e5ef2ee10e) 3.Click “Set Royalty”. # General information This is your source of info for all things Monswap ![](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2Fymg4d25X4qIJWJXAbXor%2Fmoncake.jpg?alt=media\&token=eccefd94-64fa-4eef-abad-42e180a6993e) MonSwap is one of several protocols attempting to combine the popular features of decentralized finance (DeFi) and non-fungible tokens (NFTs) into an easy-to-use platform. The BSC dapp offers a range of DeFi services, along with a launchpad and a non-fungible token or NFT supermarket. Additionally, by providing liquidity to the MonSwap protocol, users earn $MonCake(CAKE) rewards from trading fees. **Protocol#**\ The Monswap Protocol: A suite of persistent, non-upgradable smart contracts that together create an automated market maker, a protocol that facilitates peer-to-peer market making and swapping of BEP-20 tokens on the Binance smart chain. **Introduction#**\ Monswap was built to be a decentralized exchange (Decentralized Exchange – DEX). On Monswap, users are empowered to exchange BEP-20 standard tokens (Binance Smart Chain's token standard). **AMM#**\ The Monswap protocol using an Automated Market Maker (AMM), sometimes referred to as a Constant Function Market Maker, in place of an order book. AMM replaces the buy and sell orders in an order book market with a liquidity pool of two assets, both valued relative to each other. As one asset is traded for the other, the relative prices of the two assets shift, and a new market rate for both is determined. In this dynamic, a buyer or seller\ trades directly with the pool, rather than with specific orders left by other parties. **Trade#**\ Instantly swap crypto tokens: no registration or account needed **Low transaction fees#**\ Monswap runs on the Binance Smart Chain platform, one of the blockchains with much lower transaction costs than Ethereum or Bitcoin. **Decentralized#**\ Make trade directly from your wallet app. Monswap decentralized exchange unlike centralized exchanges like Binance, Coinbase, Gateio, ... Monswap doesn't keep your coins as you trade, you own 100% of your own crypto. **Earn Trading Fees#**\ Earn trading fees when you stake your tokens in Liquidity Pools (LPs). **Earn CAKE Token#**\ Get CAKE Token rewards for stake your tokens on liquidity pools.\ Get CAKE Token rewards for stake NFT Pop on liquidity pools. **Launchpad#**\ Another Unique feature of MonSwap is the Launchpad. It is a feature which allows users to get access to newly launched projects on the Binance Smart Chain(BSC). # Why Monswap?
Monswap allows Monnfts liquidity providers to earn trading fees and claim **$MonCake(Cake)** without locking **$MON** themselves. Liquidity providers can receive **$Cake** and liquidity mining rewards with minimal effort. If you would like to stake **$Cake**, Monswap lets users receive trading fees as well as a share of boosted **$Cake** received by liquidity providers. This allows for a better balance between liquidity providers and **$MON** stakers as well as better capital efficiency. **Monswap has no withdrawal fees** and minimal performance fees which is used to pay for gas and distributed to **$MON** stakers. **$MON** stakers and liquidity providers also receive liquidity mining rewards in the form of **$Cake**. # MonCake (CAKE) **Name:** MonCake\ **Ticker:** CAKE\ **Chain:** BNB Smart Chain (BEP20)\ **Contract Address:** [0x3de53f9c4f6884a0474f527fcd2164af0bb78a80](https://bscscan.com/address/0x3de53f9c4f6884a0474f527fcd2164af0bb78a80) ### TOKEN DISTRIBUTION ![Token Distribution](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2FCaR8knYjE1gvTTGX171h%2Ftokenditribution.png?alt=media\&token=34b374e6-4d97-4ee1-83bf-3436dea1a938) **MonCake** is the core token that powers the **MonSwap** ecosystem. **MonCake token mint mechanism:** The amount of tokens minted is based on the total amount in the liquidity pool. This serves purposes: 1. Ensure the rights of the people who come in later. 2. Ensure against inflation the $MON token, makes Mon's price growth sustainable. Earn **$Cake** from Farms, and Pools, buy $MON on the exchange, then explore its use cases: * Stake $MON in Pools to earn free tokens. * Stake $CAKE in Pools to earn more $CAKE. * Use MON-CAKE, MON-BNB, CAKE-BNB, BUSD-BNB in Farms to earn more $Cake. **How do I convert CAKE to MON?** Here, you can swap **CAKE** to **MON**, cash out **CAKE** and **MON** tokens and make other multiple transactions. ![](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2FTTT4cmI5ZNq2LX2xblN5%2FSWAPcake.png?alt=media\&token=c05c5245-d63a-4943-b8e8-f0694fd3411c) # How to create a MetaMask Wallet **MetaMask** is a cryptocurrency wallet used to interact with the Ethereum blockchain. It can be accessed through an app or through a browser extension. **Step 1: Download MetaMask wallet**\ Go to and click on “Download”. Choose your preferred browser or mobile application and install the MetaMask extension. MetaMask supports **iOS, Android native apps along with Chrome**, **Firefox**, **Brave** and **Edge browser extensions**. **Step 2: MetaMask wallet installation**\ Click on the MetaMask extension and click on “Get Started”. ![](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2Fd7WQ1Dstk6mzf3F9WyEL%2FBLTl9tHzNavnrEEr9JQ.png?alt=media\&token=fd213218-dd06-413a-b0cc-d4a98dc7eff8) You can either import an existent wallet using the seed phrase or create a new one. ![](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2FrbYBY79lyveD17srMt5H%2F1*WlhO7biI4EUJ6T4694zZ0w.png?alt=media\&token=8f8c902f-cc72-4455-a79f-9eafb4911f58) **Step 3: How to create a new MetaMask wallet**\ Click on “Create a Wallet” and on the next window click on “I agree” if you would like to help improve MetaMask or click on “No Thanks” to proceed. ![](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2Faby2Sakhz8BwMfpDX3kS%2F1*dV3dsDItlJsfBW0b91vmhg.png?alt=media\&token=3025fb64-5d87-43c3-9da6-4b530201a755) **Step 4:** **Create a strong password for your wallet.** ![](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2FTlqEztI9aYvcJFbhvPYh%2F1*K4dgyUFa_roMqZvHLLQiZA.png?alt=media\&token=57564605-b2f5-4482-9e91-a1ee262fcb0a) **Step 5: Securely store the seed phrase for your wallet**\ Click on “Click here to reveal secret words” to show the seed phrase. * MetaMask requires that you store your seed phrase in a safe place. It is the only way to recover your funds should your device crash or your browser reset. We recommend you write it down. The most common method is to write your 12-word phrase on a piece of paper and store it safely in a place where only you have access. **Note: if you lose your seed phrase, MetaMask can’t help you recover your wallet and your funds will be lost forever.** * Never share your seed phrase or your private key to anyone or any site, unless you want them to have full control over your funds. ![](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2Fn1HrADqOZXkqoP8rr9Kq%2F1*ok1mCMpgN0Lgx8SZmJbC8A.png?alt=media\&token=1508f436-71aa-45ac-a0e6-2aa12dd12969) Click on “Next”. **Step 6: Seed phrase confirmation**\ Confirm your secret backup phrase by clicking on each word in the order in which the words were presented on the previous screen. Click on “Confirm” to proceed. ![](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2FInlPuYVfCkOwgK6ppmPd%2F1*GnHzTDd3AfKetaCeDPOUhg.png?alt=media\&token=0b19eeb2-749e-440e-8fe9-f9dabedeebfc) ![](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2F8zgeFpSmSpEJldzMKLzh%2F1*9HgmXnJwXtQsHEq29zn88w.png?alt=media\&token=546f4c5b-ec56-46a2-bd56-f13998b558f3) **Congratulations! Your MetaMask wallet has been set up successfully.**\ You can now access your wallet by clicking on the MetaMask icon at the top-right-end corner of your preferred browser. # How import a wallet to your MetaMask account? In order to import an existing wallet to a MetaMask account, follow these simple step-by-step instructions. You will need to install the MetaMask extension for your favorite browser, import a wallet using private key or JSON file and add PBL tokens to the list. ### Step 1. Install the MetaMask extension 1.Open [https://metamask.io](https://metamask.io/) website or search for “Metamask extension” in your favorite browser (you can install Metamask for [Chrome](https://chrome.google.com/webstore/detail/nkbihfbeogaeaoehlefnkodbefgpgknn), [Firefox](https://addons.mozilla.org/en-US/firefox/addon/ether-metamask/) and [Opera](https://addons.opera.com/en/extensions/details/metamask/) browsers). ![](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2Ffk9GsKDpLxh3dvawme7E%2FRExle77LA.png?alt=media\&token=59275931-792f-48d6-8e2a-5efb803fb36c) 2.Click **Add to Chrome** to install MetaMask as Google Chrome extension (same as for other browsers). 3.Click **Add Extension**. ![](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2Fo2oTNgdAfdp44g5flEob%2FZskdXH0enKQ.png?alt=media\&token=b3fef1d8-1002-4377-8879-b25b13b791b2) ![](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2FVv01dhipH5lgSAZBkyuY%2FkMKT0TJFi5iQ.png?alt=media\&token=76070d42-99ad-48ed-9df2-6e32f9d5212e) That’s it! You have successfully installed the MetaMask extension. Now, it is time to add your existing cryptocurrency wallet. ### Step 2. Import a wallet to MetaMask There are two ways how to export an existing account. If you have a seed words (mnemonic phrase) you can export your wallet straight away: 1. Click on the icon in the upper right corner to open MetaMask extension, read and accept the terms. Click **Continue**. 2. Click **Import With Seed Phrase**. 3. Enter your wallets seed phrase (mnemonic phrase). 4. Enter a strong password. 5. Click **Import**. ![](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2FW3f30w4PJfw36pWLUhC2%2FNHlLjKeg.jpeg?alt=media\&token=2a93617d-4021-44a7-b8df-c32937187c7a) ![](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2Fmt6Kly5V0E3qeWfMFG0P%2FHakVfZXXA.jpeg?alt=media\&token=a7117b30-14d8-4d9d-9348-ee7bad093975) ![](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2FJs9MMKHx0HVGTS6UyNcx%2FYNNdXCYsw.jpeg?alt=media\&token=ad450ff5-3938-44a4-a089-6b7474bc4cf0) You have successfully imported your wallet to MetaMask! ### Alternative option — import a wallet using a private key or JSON file If you have an existing wallet address but you don’t have a mnemonic phrase, you can import your wallet address to MetaMask using a private key or JSON file. 1. After creating an account, click on the“**Account**” button in the upper right corner (see screenshots below). 2. Click **Import account**. 3. Select preferred entry type. If you want to import an account using your wallet’s private key, simply paste your private key string and click **Import**. ![](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2FNRUTkOy5YDq3jpKAoXrZ%2FfxtOwPk7IvQ.jpeg?alt=media\&token=d3392615-6d92-4aab-a390-d10968fe71b6) ![](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2FfBcVy3owKdd0GiE3UbUp%2Fd-qIBA_Pbw.jpeg?alt=media\&token=09cf7eeb-effc-4194-ab15-bb2ac2b69df2) ![](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2FAtYZRYuqNmqosj0HRSAd%2FDPhwBfkZH-RQ.jpeg?alt=media\&token=a6f38648-2dd5-4277-9ced-bd30b705e003) That’s it! You have successfully imported your cryptocurrency wallet to your MetaMask account. # Connect Your MetaMask With Binance Smart Chain 1\. Go to setting page ![](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2FuAhZWUsjYU1KpWinxtBn%2Fpasted%20image%200.png?alt=media\&token=48c8c775-cc3c-40fa-ab04-a29ce1f445c0) 2\. Go to networks Then click on ‘Networks’ in the settings menu. A list of networks that your MetaMask wallet is connected to will now appear. Now click on the ‘Add Networks’ button in the bottom of the window. ![](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2FKXcDO3BbAoBT7lrJd3kz%2F02-Step-3.png?alt=media\&token=ab1e13d4-1ad8-4911-b5e8-031ae256af60) 3\. Add the network details In this window, you will enter the details of the network to be added. MetaMask asks you to enter: Network Name, New RPC URL, ChainID, Symbol and Block Explorer URL. To add the Binance Smart Chain network, enter the following information: • Network Name: *Smart Chain*\ • New RPC URL: [*https://bsc-dataseed.binance.org/*](https://bsc-dataseed.binance.org/)\ • Chain ID: *56*\ • Symbol: *BNB*\ • Block Explorer URL:[*Binance (BNB) Blockchain Explorer*](https://bscscan.com/) ![](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2F0VHTILTVXvsTwzeeqYn8%2F03-Step-3.png?alt=media\&token=3aea7e8e-0612-4c37-a142-5a66fffcf5e0) Please note that all transactions on the Binance Smart Chain are paid with Binance Coin (BNB). *4.* Connect your MetaMask to the BSC network ![](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2FRoritvXfaorgGx7uVLBI%2F4-Step-4-BSC.png?alt=media\&token=9c2d0860-cc5c-4c94-bfe6-9355cf0468bf) Congratulations, you have just connected your MetaMask wallet to the Binance Smart Chain network! # CryptoPops Inspired by CryptoPunks, one of the world's first famous NFT projects with artworks encrypted on the Ethereum blockchain platform, our dev team has started researching and developing an NFT collection with the famous Pop Art fashion design style. As a result, CryptoPops was born under the intellectual property of Monnfts, with the works encrypted based on the blockchain platform of Binance. We believe that with CZ's excellent leadership, the collection will grow and reach more people, taking advantage of Binance's competitive advantage of low gas fees. Therefore, we believe CryptoPops will stand out in the NFT market. ### **Crypto Pops Society** The Crypto Pops Society is a collection of 5.000 unique Pops with 64×64 pixel art images, generated algorithmically on the Binance Smart Chain (BCS). Most are Human, but there are a few rarer types mixed in: Zombies, Animals, Robot and even the odd Constellation. There are exactly 5,000 of them, each with their own ostensible personality and unique combination of distinctive, randomly generated features. ![Cryptopops collection](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2FLo0U6zMERDt94UsSSwDx%2Fpoparts.png?alt=media\&token=c35ba02b-57e4-4a1f-86ca-1884546fc180) ### **Types and Attributes** A combination of different attributes mixed together makes sure each one of the 5,000 is entirely unique. ![](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2FVF5BKmb92l8a8rFpdyq2%2FScreenshot%202022-03-04%20at%2015-46-31%20Crypto%20Pops%20Society.png?alt=media\&token=757ca302-7937-46af-b0a1-cfba5e94136b) ### **The ability to control** Developing blockchain projects is different from conventional projects. Developers after announcing and releasing the product will almost no longer have the right to interfere and modify. It is the investors who will be responsible for the changes they have made to the smart contract. At the same time, the fixed number of 5,000 Pops makes Pops valuable and limited. Even if creators could make more Pops, they would not be recognized in the market. This has proven equality and fairness for everyone when entering this playground. Because once created, the manufacturers locked it up and the ownership control code was injected into the BSC blockchain network. By the time the Pops are released on the blockchain, the producer has lost his control to create more or own the Pops. ### Community and society Currently, Monnfts is raising funds for the rights and child protection association affiliated with Unicef. We will spend 20% of our profits to help disadvantaged children in the country and around the world. Besides, to promote and support investors $MON has come with difficulties with the Dev team. We will put 20% of profits into liquidity and accelerate the development of MON token value. The rest of the money raised Monnnfts focuses on developing the $MON ecosystem

2024 Roadmap for the CryptoPops NFT Collection

## Privilege 1\) It is the only NFT inspired by the only Pops Art style in the world. 2\) Chance to get $20,000 when reaching 3500 POP holdings. 3\) The collectors will receive a gift t-shirt from Monnfts. ![](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2FNTabZUXUK8CBZBiHGc1G%2Faothun02.jpg?alt=media\&token=7868bfa4-76f2-4253-9f1a-30c07ce55949) ![](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2FMk79RD5m0kwhSpJRdvVL%2Faothun01.jpg?alt=media\&token=b019ca94-52cb-4754-9550-229e2176b239) ### **Details and FAQ** **How do refunds work with NFTs ?** \ All NFT sales are final. However, token holders can resell their NFTs on secondary markets like OpenSea and OKX. Additionally, you will receive 2 NFTs on SOL and Base, which you can swap on NFTX. Don't miss out on the Meme Coin POP airdrop! **Which blockchain will be used ?**\ CryptoPops will be deployed on the Binance Smart Chain.\ Contract: 0xE8568408C33f9A407Fe403754c48c257FA250b1A **Do you charge any fees for transactions?**\ No. We charge no fees for Cryptopops transacted through the built-in market beyond the ones charged by BNB (gas). **What will be the price?**\ The Price of Minting will be 0.3 BNB + Gas Fees **How do I get a Pop?**\ Along with designing the CryptoPops themselves, Monnfts also created their own marketplace on the CryptoPops website. That’s where users make bids, buy, and sell [CryptoPops](https://pop.monnfts.com). You can interact with the marketplace by installing and connecting your MetaMask. Then CryptoPops will also enter App.monnfts.com, a leading NFT marketplace on BSC blockchain in Asia. **Why CryptoPops?**\ When you buy an CryptoPops, you’re not simply buying a simple NFT, you are getting access to a community where benefits and utilities will increase with the time. **How to own Cryptopops?**\ Similar to cryptocurrencies, the team created an exchange where investors can exchange, buy, and bid on pops on its marketplace created for Cryptopops. To make transactions, investors need to connect to the MetaMask wallet. Besides, there is still a “pops for sale” market at Monnfts, Binance. Here, the pops sold will be in the form of BEP-20 tokens to be tradable on NFT exchange platforms **What Rights Do I Have To The Artwork?**\ You are free to do with your CryptoPop as your heart desires. By purchasing a CryptoPop, you are granted full and complete rights to do whatever you want with your NFT and the artwork included, commercially or otherwise. **Will the team mint NFTs ?**\ The team will mint: \ \+ 100 NFTs to help sustaining the activity on the longrun and reward the faithfull soldiers that have been helping from firstday.\ \+ State 1: 2005 CryptoPops will be minted and sold in Mystery Boxes.\ \+ State 2: The remaining 2795 POPs will be minted and sold again. \ \+ 200 NFTs for the community, influencers and partners giveaways. # Affiliate Marketing – M2C Market to Customers (M2C) Business Model ![Make $MON Online with M2C](https://997701854-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mio1-rDAL3IsoQDatd4%2Fuploads%2F9fqSGJ483wAkq4uWbzVm%2FAffiliate-Marketing.jpg?alt=media\&token=22df0672-45d0-4808-9b43-c6575c1cda66) Currently, there are many people who make money from the Internet. From the famous Youtubers and Vloggers in the world such as Pew Die Pie, Smosh to the 7-year-old Ukrainian girl Diana who has ten million followers on social media. They have earned millions of dollars every year. In Vietnam, there are also many successful Youtubers who earned great income from this job. Another effective strategy of making money is from the affiliate marketing program applied by the world's largest businesses such as Amazon, eBay, Alibaba, etc. It has helped many people earn thousands of dollars every month. Even the giants like Facebook or Tiktok have also opened many online money-making programs for users. M2C is Binance's blockchain-based platform that forms a decentralized Web3 as an intermediary that helps businesses easily advertise products and services to millions of customers through an affiliate marketing program. At the same time, M2C also makes it easy for MMOs to earn money by performing simple tasks such as downloading apps, viewing websites, playing games, taking surveys, recommending products or services, or saving money through the Cashback shopping program. For more specific, M2C is an online money-making platform, where businesses and users both can make money. Businesses will reach many potential customers, while members participate to earn income by spending their efforts to advertise, find customers for businesses. At M2C, there will be 3 main objects: 1. Enterprises: The companies that need to advertise and approach potential customers. 2. Members: People who want to make money online. 3. M2C: The platform that connects the two above objects. Cost per order (CPO) is the average amount of MON token that we spend to drive any customer to purchase an NFT in our platform. Cost per acquisition (CPA) is the average amount of MON token that we spend to drive a new customer to purchase a product or service. At the moment, we are spending average $31 for each new user. # MonGames: Ninja Jumping Are you ready to unleash your inner ninja and embark on an adrenaline-pumping adventure? Look no further than Ninja Jumping, a captivating action game developed and produced by Monterdesign. Since its release on February 11, 2020, for Android, Ninja Jumping has garnered attention for its engaging gameplay and unique reward system. * Developer: Monstudio * Manufacturer: Monstudio * Operating system: iOS, Android * Genre: action * Release date: October 10, 2020
**Join the Ninja Jumping Community:** Step into the Ninja Jumping community, where daring players can showcase their skills in jumps and spins. The game promises rewards for the boldest ninjas, making it an exciting journey for players seeking both challenges and recognition. **How to Get Started:** 1. Download Ninja Jumping: Head to Google Play and download Ninja Jumping to your device using the provided link. Install the game to kickstart your adventure. 2. Choose Your Ninja: Once in the game, select your favorite ninja character. Each ninja comes with unique abilities, allowing you to tailor your gameplay to your preferred style. 3. Master High Jumps: In Ninja Jumping, the objective is simple yet thrilling — jump as high as you can by navigating rotating circular platforms. The higher you soar, the more points you accumulate. But beware of falling; a misstep means starting over. Can you reach the top? 4. Compete for Top 10 Rankings: Every month, the top 10 players are rewarded with MonCake, the in-game cryptocurrency. Your score determines your rank, and competing globally adds a competitive edge to your jumping skills. 5. Claim Your MonCake Rewards: If you secure a spot in the top 10 at the end of the month, follow these steps to claim your MonCake rewards: * Open the Ninja Jumping game. * Navigate to the Rewards section. * Locate your name on the leaderboard. * Click the “Claim” button to initiate the process. * Your MonCake will be deposited into your in-game wallet. **6. Use Your MonCake:** **MonCake is the core token that powers the MonSwap ecosystem.** **MonCake token mint mechanism: The amount of tokens minted is based on the total amount in the liquidity pool. This serves purposes:** 1. **Ensure the rights of the people who come in later.** 2. **Ensure against inflation the $MON token, makes Mon's price growth sustainable.** **How do I convert CAKE to MON?** **Here, you can swap CAKE to MON, cash out CAKE and MON tokens and make other multiple transactions.**
**Game Mechanism: Unleash Your Inner Ninja in an Endless Jumping Adventure!** Embark on a thrilling journey where you embody the spirit of a ninja, navigating through a world filled with challenges and excitement. As you step into the shoes of these agile warriors, your mission is clear: conquer the skies by leaping through a myriad of obstacles. The game introduces you to a mesmerizing array of rotating circular platforms that become your playground. Your goal is simple yet exhilarating — jump as high as you can. Each successful jump adds points to your score, propelling you further into ninja greatness. However, tread carefully, for a misstep sends you plummeting back to the beginning. To thrive in Ninja Jumping, you must hone your skills in evasion and precision. Dodge falling obstacles with the grace of a ninja, demonstrating your mastery over each jump. The platforms may seem like mere stepping stones, but as the game progresses, they become a challenging terrain that only the most skilled ninjas can conquer. Ninja Jumping is not just a solo quest. Compete with friends and players worldwide to claim the title of the ultimate ninja jumper. Soar to new heights, outpace your rivals, and secure a spot in the coveted top 10 rankings. Should you prove yourself among the top 10 players at the end of each month, the game rewards you with MonCake, an in-game cryptocurrency. This isn’t just about jumping; it’s about rising through the ranks, showcasing your ninja prowess, and reaping the sweet rewards of your daring escapades. **Character Features: Customize Your Ninja, Master Unique Abilities, and Conquer the Skies!** In the dynamic realm of Ninja Jumping, the excitement goes beyond the adrenaline-pumping jumps. The game offers a delightful twist by allowing players to not only embody the spirit of a ninja but also customize their experience through a diverse range of ninja characters. As you immerse yourself in the endless jumping adventure, you earn in-game currency for your daring feats. What makes this feature captivating is the ability to utilize your hard-earned currency to unlock and play as distinct ninja characters. Each character comes with its own set of unique abilities, adding a layer of strategy to your gameplay.
Picture this: a nimble ninja with enhanced agility to navigate obstacles swiftly, or a powerhouse ninja capable of smashing through barriers with ease. The choice is yours. The game’s character roster transforms each jump into an opportunity to explore different playstyles, keeping the gameplay fresh and engaging. Earning and unlocking new characters becomes a dynamic part of the Ninja Jumping experience. It’s not just about reaching new heights; it’s about discovering which ninja best complements your playstyle and enhances your chances of conquering the challenging obstacles in your path. So, jump, earn, and customize your ninja squad. Unleash the potential of each character to overcome the ever-intensifying challenges. Ninja Jumping isn’t just a test of skill; it’s a personalized adventure where your choice of character shapes the way you soar through the skies and leave your mark in the leaderboard rankings. **Gameplay and Currency Conversion:** Discover the dynamic gameplay of Ninja Jumping and dive into MonCake’s utility within the MonSwap ecosystem. Learn how to swap CAKE to MON, execute transactions, and engage in various in-game activities. Ninja Jumping transcends being just a game; it’s an opportunity to test your ninja skills and earn tangible rewards. Whether you aim for the thrill of high jumps or the allure of MonCake, Ninja Jumping offers a unique gaming experience. Download Ninja Jumping now, exclusively available on Android, and become the ultimate ninja jumper. Stay tuned for its upcoming release on iOS and additional games with similar gameplay.