CoinReal

uses on-chain rewards to build a fair ecosystem

Built at Chromion: A Chainlink Hackathon

Created on 28th June 2025

•

CoinReal

uses on-chain rewards to build a fair ecosystem

The problem CoinReal solves

CoinReal Pain Points and Solutions Summary

Core Pain Points

Critical issues facing current crypto communities:

Extremely Unfair Value Distribution: Most value is captured by platforms and top influencers, while ordinary users contributing content and interactions receive almost no returns.
Lack of Transparent Lottery Mechanisms: Existing reward distribution systems are essentially black boxes, making it impossible for users to verify fairness and creating trust crises.
Difficulty for Project Teams to Obtain Real Feedback: Crypto project teams struggle to get valuable, authentic user feedback from communities, affecting product iteration and market decisions.
Lack of Effective User Participation Incentives: Traditional platforms lack transparent, verifiable reward mechanisms, limiting enthusiasm from both users and creators.

Innovative Solutions

CoinReal leverages blockchain technology to create a transparent, fair, and win-win incentive model:

✅ Engage to Earn Participation Model: Users directly earn CRT tokens through real interactions like commenting and liking, making every participation valuable.

✅ Chainlink VRF Ensures Fair Distribution: Utilizes Chainlink Verifiable Random Function for lottery draws, completely solving trust issues in traditional black-box lotteries with all random results verifiable on-chain.

Technical Implementation:

Interface Definition:
AutoVRFInterface
defined in
background/src/Campaign.sol
lines 12-17

Contract Initialization: AutoVRF contract connection established in
background/src/Campaign.sol
line 132

Lottery Execution:
getVRF()
function called for fair lottery in
background/src/Campaign.sol
line 283

Dedicated VRF Service: Independent deployment at
chainlink_auto/autoVRF.sol
(Address: 0x7593F3782435ceab38e9cBA065AB6233244EDD9C) implements complete Chainlink VRF integration, featuring
getCampaignLuckers()
function specifically designed for Campaign lottery draws

✅ AI-Powered Sentiment Tagging System: Combines Chainlink Functions with Gemini AI to automatically generate sentiment tags (positive/negative) for comments, providing valuable real user feedback data to project teams.

Technical Implementation:

Interface Definition:
AutoTagInterface
defined in
background/src/Project.sol
lines 10-17

Contract Initialization: AutoTag contract connection established in
background/src/Project.sol
line 101

AI Analysis Trigger:
getCommentFlag()
function called to trigger AI sentiment analysis in
background/src/Project.sol
line 147

Callback Implementation:
updateCommentFlag()
callback function implemented in
background/src/Project.sol
line 153 to receive AI analysis results

Dedicated AI Service: Independent deployment at
chainlink_auto/antoTag.sol
(Address: 0x4cf76ab799BDA2A205Bef7f3F40F2538C9169Fe9) uses Chainlink Functions to call Gemini AI API for sentiment analysis

✅ Automated Reward Distribution: Implements automated Campaign management and reward distribution through Chainlink Automation.

Technical Implementation:

VRF Automation:
checkUpkeep()
and
performUpkeep()
functions implemented in
chainlink_auto/autoVRF.sol
to automatically detect VRF completion status and distribute rewards

AI Automation: Automation functionality implemented in
chainlink_auto/antoTag.sol
to automatically process AI analysis results and call project contracts to update comment tags

Campaign Management: Automatically detects Campaign end times and triggers reward distribution processes

✅ On-Chain Proof of Contribution: All user activities including participation, content creation, and interactions are recorded on-chain, making contributions transparent and verifiable with automatic reward distribution via smart contracts.

✅ Win-Win Ecosystem for All Parties:

Users: The more they contribute, the more they earn, boosting engagement
Platform: Real user activity drives traffic and influence
Project Teams/Sponsors: Incentives help rapidly attract users, increase project visibility, and obtain real market feedback

One-Sentence Summary

CoinReal leverages transparent, on-chain incentives powered by Chainlink's infrastructure to break Web2 platform monopolies, creating a fair, open, and sustainable win-win ecosystem for users, platforms, and project teams.

Key Chainlink Integration Highlights

Triple Integration: Seamlessly combines VRF, Functions, and Automation for a complete decentralized solution
Production Ready: Both VRF and AI services are deployed and operational on Avalanche Fuji testnet
Innovative Use Cases: Novel application of Chainlink Functions for crypto sentiment analysis and VRF for transparent community rewards
Scalable Architecture: Modular design allows for easy expansion and integration with additional Chainl

Challenges we ran into

Chainlink Integration Challenges and Solutions

Challenges and Innovative Solutions

1. Local Testing Difficulties Due to Subscription Dependencies

Challenge: Both Chainlink VRF and Functions require valid subscription IDs to function properly, making local unit testing with

forge test

impractical and severely impacting development efficiency and test coverage.

Solution: Implemented a modular deployment architecture where Chainlink services are independently deployed on testnets and called through interfaces with fixed addresses, rather than being deployed alongside the main project contracts. This approach not only resolves testing issues but also enhances system maintainability and scalability.

VRF Service Independent Deployment:
0x7593F3782435ceab38e9cBA065AB6233244EDD9C
AI Analysis Service Independent Deployment:
0x4cf76ab799BDA2A205Bef7f3F40F2538C9169Fe9

2. Chainlink Functions JavaScript Code Limitations

Challenge: Chainlink Functions imposes strict size and complexity limitations on input JavaScript code. Complex AI calling logic frequently resulted in unknown revert errors, affecting functionality implementation.

Solution:

Code Optimization: Streamlined JavaScript code by removing unnecessary logic and comments
Gas Optimization: Increased gasLimit from default to 300,000 to ensure sufficient computational resources
Error Handling: Implemented robust error handling mechanisms to ensure valid results even in edge cases

3. Functions Return Value Length Limitations

Challenge: Chainlink Functions truncates overly long returned strings, creating limitations for applications requiring detailed AI analysis results.

Solution:

Standardized Output: Designed a concise tagging system (POS/NEG/NEU) using abbreviations instead of full descriptions
Prompt Engineering: Explicitly required standardized short tags in AI prompts, avoiding verbose explanations
Mapping Mechanism: Implemented
tagToFlag()
function in smart contracts to convert string tags to numeric identifiers, further optimizing storage

4. VRF Multi-Random Number Request Cost Optimization

Challenge: Directly requesting multiple VRF random numbers results in high gas fees and extended processing times, negatively impacting user experience.

Solution:

Seed Expansion Algorithm: Request only one VRF random number as a seed
Deterministic Generation: Use
keccak256(seed + requestId + index)
to generate any number of pseudo-random numbers
Cost Efficiency: Reduced multi-random number request costs by over 95% while maintaining randomness quality

5. Asynchronous Result Retrieval Automation Challenges

Challenge: Both VRF and Functions are asynchronous operations requiring manual result retrieval through view functions, creating user experience issues for building automated DApps.

Solution:

Automation Integration: Designed all Chainlink services as Automation-compatible contracts
Automatic Callback Mechanism: Implemented
checkUpkeep()
and
performUpkeep()
functions to automatically detect completion status and trigger callbacks
Network Optimization: Selected Avalanche as deployment network, leveraging its fast block confirmation time (2 seconds) to reduce finalization delays
State Management: Designed comprehensive state tracking mechanisms to ensure every request is properly processed and called back

Technical Innovation Highlights

By solving these challenges, we achieved:

Fully Automated User Experience: Users don't need to manually query results; all operations are automatically completed through Automation
Cost-Optimized Random Number Generation: Significantly reduced costs for multi-random number scenarios
Efficient AI Integration: Achieved millisecond-level sentiment analysis response
Production-Ready Architecture: Modular design provides excellent maintainability and scalability

These solutions not only address current project technical challenges but also provide valuable reference patterns for other developers integrating Chainlink services.

Tracks Applied (2)

Onchain Finance

CoinReal leverages technologies like Chainlink Automate, Chainlink VRF, Chainlink Functions, and Gemini AI to break the ...Read More

Avalanche Track

As a content platform, our users need to interact with the project frequently, which typically requires significant gas ...Read More

Avalanche

Technologies used

Solidity

React

Node.js

Next.js

Remix IDE

Chainlink

Tailwind CSS

Discussion

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