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Guest Author: Hamber Luo
First principles is a problem-solving method designed to break down complex problems to their most basic principles or facts, and then build solutions from those fundamentals.
Based on first principles, the problems that a DEX needs to address are as follows:
Security and trust issues: DEXs ensure that users don’t have to trust only a single centralized entity to handle their funds. This means that even if the exchange is attacked, users’ funds remain safe.
Permission and review issues: DEXs allow anyone anywhere to trade without the scrutiny or permission of a central authority.
Liquidity issues: The order book model can offer a more traditional and familiar trading interface, which might attract more traders and thus increase liquidity.
Cross-chain issues: With the increase in assets on multiple chains, there’s a need for a platform capable of trading across multiple chains.
Privacy issues: DEXs can offer higher trading privacy than centralized exchanges because transactions aren’t conducted through centralized servers.
In order to achieve seamless use, a series of technical user experience (UX) and regulatory issues need to be addressed.
User-friendliness: Currently, many DEXs’ user interfaces (UIs) and experiences aren’t as consistent as those of centralized exchanges. To achieve widespread adoption, they need to become more user-friendly.
Performance and Scalability: DEXs need to handle a large volume of transactions, but Layer 1 blockchains limit their performance. Technological advancements, such as Layer 2 solutions, can help address this issue.
Education and Awareness: Most people are still unfamiliar with DEXs. Extensive education is needed to help people understand their benefits.
Regulations and Compliance: In some countries and regions, DEXs may face potential legal challenges or regulatory issues.
StarkEx and other ZK-rollup technologies have given a significant boost to DEXs in terms of performance and scalability.
Given these improvements, the main challenges now shift to user-friendliness, education and awareness.
Under the wave of AI, especially as large models like GPT gradually approach the era of AGI (artificial general intelligence at human level), we indeed see a series of social and technological changes.
Changes in user mindset can be explored by the following trends.
Increased expectation for instant gratification: As AI technology can quickly provide users with solutions and answers, their expectations for immediate feedback have also increased accordingly. This means traditional products and services may need to improve their response time to meet user needs.
New demands for entertainment: With the liberation of productivity, people have more leisure time. This leads to increased demands for entertainment and leisure products. Users not only expect products to entertain, but also to provide a more profound and diverse experience.
Pursuit of personalized experiences: The development of AI technology allows products to “understand” their users better, thereby offering a more personalized experience. Users now expect products to “know” them and to provide tailored experiences and services.
New methods of education and learning: With advancements in AI, learning and educational methods are also changing. People may increasingly come to rely on AI-assisted learning, seeking more efficient and customized learning methods.
Concerns about privacy: As the application of AI technology becomes more widespread, users’ concerns about privacy will also increase. For example, they may become more concerned about how products collect, use and ensure their data.
Defining the real and virtual: With the development of technologies like virtual reality and augmented reality, people may begin to blur the lines between “real” and “virtual.” This could result in users choosing products based on the sense of realism or immersion they offer.
Changes in social interaction: AI’s application in the social realm could alter the way people interact with each other. For example, people might interact more with AI, while interactions with real people may decrease.
Applying game mechanics to non-gaming environments is an innovative way to attract and retain users, increase user activity, enhance user experience and improve satisfaction.
Gamified reward system: Provides users with tokens, NFTs or other virtual items as rewards that encourage them to complete certain tasks, such as making their first trade, introducing new users or participating in community activities.
Interactive learning tasks: Game missions are designed so that users learn the basic operations and concepts of DEXs while completing tasks, such as simulating the real trading process through virtual trades.
Arenas and leaderboards: Creation of arenas for users to participate in simulated trading competitions, with top-ranked users receiving rewards. This not only increases interaction among users, but also provides a platform for them to learn and improve their trading skills.
Gamified tutorials and storylines: Create a storyline or character to guide users in understanding the history, workings and advantages of DEXs, making the educational process more engaging and appealing.
Community events and challenges: Holding regular community challenges and events to encourage user participation. This not only promotes community building, but also provides users with more opportunities for interaction and entertainment.
By applying game mechanics to a DEX, one can create a more appealing, interactive and fun environment, thus attracting more users and increasing retention.
However, it’s essential to ensure that gamified elements align with the core functionalities of the DEX, and to avoid distracting users from their primary trading activities.
Design the user education and interaction of DEX as an app-like game, focusing more on casual experiences rather than heavy-duty large games.
Gamification can permeate various business scenarios:
Wallet connection
Deposit and Withdrawal
Trading
Predicting market rise or fall
Mining
Social interactions
This gamified approach can effectively lower the entry barrier for DEXs, making them more accessible and easier to understand.
Trading itself is not a game, especially when real money is involved. However, gamifying certain elements of trading — especially within educational and training contexts — can help new users more easily understand and familiarize themselves with trading processes and strategies.
Lightweight Game Engine
Mini-games require the use of a game engine for development.
What we need here is a lightweight game engine like Unity for casual games, not game engines specifically for making games.
When considering integrating mini-games into non-gaming applications, there are several reasons to choose a lightweight game engine, as follows.
Simple learning curve: Lightweight game engines are often more concise, making the learning curve gentler for developers. Quick learning and output are key, especially when it comes to teams whose main background isn’t in game development.
Efficient performance: Casual mini-games often don’t have high performance demands. By omitting many unnecessary features, lightweight game engines can run smoothly on most devices, reducing battery consumption.
Rapid development and iteration: Lightweight engines tend to be more flexible and don’t require complex settings or configurations. This means you can move faster from prototype to final product and iterate more quickly.
Seamless integration with non-gaming features: When integrating mini-games into a primary application or platform, lightweight engines are typically easier to integrate. In addition, they don’t conflict with other parts of the application.
Lesser resource consumption: Lightweight engines generally don’t come with the hefty resources and files that large game engines do, which means the overall size and memory footprint of the application will be smaller.
Flutter & Flutter Flame
Flutter is an open-source UI software development tool kit developed by Google. Initially, it was designed to help developers build high-quality native apps for iOS, Android and the web.
However, it later expanded to other platforms, such as desktops.
The main features of Flutter include its use of the Dart language, a high-performance rendering engine, and a plethora of components and plugins to simplify the development process.
Flutter Flame is a lightweight game development framework built on the Flutter platform. Its aim is to provide a simple, user-friendly way to create games and game-related apps using Flutter.
The relationship between the two can be understood from the following perspectives.
Base Platform: Flame is built on Flutter, which means it leverages all of its features and benefits, such as cross-platform capabilities, high-performance rendering and a rich component library.
Development Language: Since Flame is designed for Flutter, it also uses Dart as its programming language.
Extensibility: Flame seamlessly integrates with other Flutter libraries and plugins, allowing developers to easily add audio, animations or other functionalities.
Objective: Although Flutter itself is a robust development framework, its primary focus is on creating applications, not games. That’s why there’s a need for a framework like Flame to fill this gap. Flame offers core game-related functionalities, such as the game loop, sprite rendering and collision detection.
Community & Resources: Since Flame is based on Flutter, it naturally benefits from Flutter’s strong community and resources. This means that developers have more resources to turn to when looking for tutorials, problem-solving or help.
In summary, while Flutter provides a cross-platform application development environment, Flutter Flame adds tools and features optimized for game development, making game development within Flutter feasible.
When considering game development with Flutter and deploying it to the web, there are two distinct strategies:
Use Flutter specifically for game development, and make the game run on the web.
Migrate the entire front end to Flutter.
Each strategy has its own advantages and considerations.
For instance, the first approach focuses on utilizing Flutter’s capabilities for game development, and ensures the game is optimized for the web.
The second approach is broader, implying a full adoption of Flutter for all front-end aspects, not just the game. This could offer a consistent UI/UX across the application, but may involve more extensive work in terms of migration and adaptation.
Gamification and social trading are currently hot topics in the fields of digital currency and DEXs.
To better understand the roles and potential of these two within the context of a DEX, let’s analyze them one by one.
Advantages
User Engagement: The introduction of competition, challenges and reward mechanisms can increase user engagement and loyalty.
Education: Novice users can more easily learn the intricacies and strategies of trading through gamification.
User Retention: Ongoing rewards and challenges can encourage users to participate and trade long-term.
Challenges
Oversimplification: Excessive gamification could lead users to overlook the risks of trading, potentially resulting in unwise decisions.
Sustainability: To maintain user interest, it’s necessary to continually update and add new game elements.
Advantages
Collective Wisdom: Users can make decisions based on the strategies and actions of other successful traders.
Community Building: Encourages interaction and communication among users, enhancing the cohesion of the community.
Reduced Barrier to Entry: Novices can quickly get started by mimicking experienced traders.
Challenges
Risk Amplification: If a large number of users follow a particular trader, it could amplify systemic risks.
Overreliance: Users could become overly dependent on other traders, instead of conducting their own research and analysis.
Privacy Concerns: Publicizing trading strategies and actions could potentially lead to privacy breaches or unwanted attention.
So, between gamification and social trading, which one is the direction of the future?
This actually depends upon the target audience and market positioning of the DEX.
If the goal is to attract more young users and beginners, gamification might be more appropriate, because it can provide a more relaxed and entertaining trading experience.
For professional traders who wish to share experiences and strategies, social trading may be more appealing.
However, the future direction isn’t to choose one over the other, but to combine the strengths of both.
A DEX that integrates elements of both gamification and social trading can offer an enjoyable and in-depth user experience, thus meeting the needs of different users.
In the wave of AI, users have already become accustomed to intelligent, automated experiences, and they expect more convenient, personalized services.
At the same time, the demand for entertainment and social interaction is constantly growing.
If we think from the perspective of slogans or external perceptions, I believe there are several strategies:
Gamification as primary, social trading as secondary: In this approach, the emphasis is on the fun and engagement of trading. For example:
“Trading: It’s just that fun!”
“Experience the most entertaining trading adventure.”
This approach suits those wanting to attract young users, beginners or those seeking a unique trading experience.
Social trading as primary, gamification as secondary: Here, one can stress the value of social aspects and the power of connection. For instance:
“Trade with the world.”
“Make trading more social.”
This strategy might attract those who wish to interact with others, and to share experiences and strategies.
Combine the two: One could also try to merge elements of gamification and social trading, creating a unique brand message. For example:
“Trade, have fun, and share freely.”
This strategy could attract a wide range of users, but might require more effort to ensure the clarity and appeal of the message.
Considering the shifts in user mindset under the wave of AI, I believe the third strategy to be the most promising, because in the AI era, users not only expect intelligent services but also seek more interactions and social opportunities.
Merging elements of gamification and social trading could offer users a trading experience that’s both fun and in-depth.
On-Device Machine Learning (also known as edge machine learning, or edge ML) refers to conducting machine learning computations directly on the user’s device, rather than on servers or cloud centers.
This brings about benefits such as enhanced privacy protection and lower latency. Applying edge machine learning to a DEX can offer advantages in multiple aspects.
Edge ML can help create a personalized trading experience that’s tailored to a user’s historical trading data and preferences.
Business Scenarios
Personalized Recommendations: Recommend new trading pairs or investment opportunities based on a user’s trading history and behavior.
Smart Notifications: Offer users targeted market trend updates and alerts.
This application develops a smart assistant that can provide real-time trading advice and market analysis.
Business Scenarios:
Trade Strategy Optimization: Analyze a user’s trading strategy and provide optimization suggestions.
Risk Management: Analyze market conditions in real time to help users manage their risks more effectively.
By executing computations on-device, a DEX can reduce the load on central servers, thus enhancing overall performance and efficiency.
Since data is primarily processed and stored locally, user privacy is better protected.
Business Scenarios
Private Transaction Analysis: Users can analyze their trading history locally without having to upload sensitive information to servers.
Using machine learning to detect game cheating is an emerging area of interest.
Cheating disrupts the fairness of games and can result in the departure of honest players.
Steps to Address Cheating
Data Collection: Gather behavior data from both regular players and known cheaters. This dataset could encompass parameters such as character movement speed, hit accuracy, resource acquisition rate, keystroke frequency and network behavior.
Feature Engineering: Based on the aforementioned data, create a set of features aiming to distinguish between regular players and cheaters. For instance, if a player completes a series of intricate tasks in an extremely short time, it might indicate cheating.
Model Training: Use labeled data (e.g., knowing which players cheat and which don’t) in order to train a classification model, such as decision trees, neural networks or support vector machines.
Real-Time Detection: During real-time gameplay, collect player behavior data and predict by using the trained model. If the model predicts a high likelihood of a player cheating, appropriate actions can be taken, such as warnings, account restrictions or other penalties.
Feedback Loop: Since cheaters may alter strategies in order to evade detection, it’s crucial to regularly update and retrain models. It’s possible to set up a feedback system for players to report suspicious behaviors, with these reports serving as new training data.
Consider Privacy: Ensure compliance with privacy regulations when collecting player data, and clearly inform players about the purpose of data collection.
Address False Positives: Any detection system can display false alarms. Therefore, ensure a low false-positive rate, and provide a mechanism for players to appeal against wrongful detections.
Edge ML creates possibilities for a more private, personalized and efficient DEX experience. Additionally, it reduces the centralized elements in a DEX’s operation, which aligns with the core philosophy of decentralization.
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