## 1. Objective
Gather and compile hourly snapshots of both supply and borrowing rates for AAVE v3 across three networks (Base, Ethereum, and Arbitrum) over the last 2 years. The goal is to provide a time-series dataset of interest rates, allowing for analysis of rate dynamics, trends, and potential opportunities or risks across different blockchains.

## 2. Data Requirements

### Essential Data Fields
- **Timestamp**: The precise hour for each data point (UTC preferred)
- **Blockchain Network**: Identify whether the data is from Base, Ethereum, or Arbitrum
- **Token**: The specific asset (e.g., USDC, DAI, ETH) that has lending and borrowing rates
- **Supply (Deposit) Rate**: The annualized rate lenders receive for depositing the token
- **Borrowing Rate**: The annualized rate borrowers pay to borrow the token

### Optional Data Fields
- **Utilization Rate**: The ratio of borrowed funds to the total supplied funds
- **Total Supply and Total Borrowed Amounts**: Context and liquidity availability indicators
- **Variable vs. Stable Rates**: Separate rates for variable and stable borrowing rates per asset

## 3. Approach to Data Collection

### Step 1: Identify Data Sources
- **Aave Subgraph API**: Official subgraph for querying historical data across networks
- **Aave Protocol Direct API**: Alternative API for rate information
- **Historical Indexing Services**: Services like Dune Analytics, Flipside Crypto, or Covalent
- **On-Chain Data Extraction**: Direct blockchain transaction logs and smart contract interactions

### Step 2: Construct and Optimize Queries
Use Aave subgraphs on each network to query data by token, network, and hourly timestamp.

### Step 3: Data Processing and Normalization
- **Data Cleaning**: Remove duplicates, handle missing data points, standardize timestamps
- **Normalization**: Ensure consistent units across all rates
- **Interpolation**: Handle missing values carefully

### Step 4: Data Storage and Access
- **Storage Format**: CSV, JSON, or SQL database
- **Versioning and Updates**: Automated scripts for dataset maintenance

## 4. Analysis Possibilities and Applications
- **Trend Analysis**: Study rate fluctuations and market demand
- **Cross-Network Comparison**: Identify rate disparities across networks
- **Risk Assessment**: Monitor rate increases and liquidity issues
- **DeFi Strategy Development**: Create optimized lending/borrowing strategies

## 5. Potential Challenges
- **Data Availability**: Incomplete historical data on newer networks
- **API/Query Limits**: Managing large dataset retrieval
- **Data Consistency**: Handling format differences across networks

## 6. Example Dataset Structure

| Timestamp (UTC) | Network | Token | Supply Rate (%) | Borrowing Rate (%) | Utilization Rate (%) |
|----------------|---------|--------|-----------------|-------------------|---------------------|
| 2022-11-01 00:00:00 | Ethereum | DAI | 1.75 | 3.25 | 65.4 |
| 2022-11-01 01:00:00 | Ethereum | DAI | 1.76 | 3.26 | 65.6 |
| 2022-11-01 00:00:00 | Arbitrum | USDC | 2.15 | 4.50 | 70.3 |
| 2022-11-01 01:00:00 | Base | ETH | 0.95 | 1.85 | 45.2 |

ai-marketplace

Hourly AAVE v3 Supply/Borrowing Rates for the last 2 years on base , Ethereum and Arbitrum

BEPRO Token

## Objective

Develop an AI-powered agent to detect potential rug pulls and scams in new crypto projects by analyzing token contracts, liquidity information, team credibility, social interactions and transaction patterns on Dexes. The solution should help users identify high-risk projects, minimizing their exposure to fraudulent activities.

## Scope of Work

### Data Collection & Preprocessing

• Gather data on project tokens, contract addresses, and liquidity metrics from blockchain explorers (e.g., Etherscan), and DeFi analytics platforms like Dexscreener or DexTools.

• Aggregate historical data on known scam tokens for model training, including red flags such as sudden liquidity withdrawals, team token holdings, and low community activity.

• Standardize and preprocess data to handle various data sources, including token creation dates, social media presence, and developer wallet activity.

###	Model Development

•	Design a classification model (e.g., **random forest**, **logistic regression**, or **neural network**) that predicts rug pull risk based on contract properties, token distribution, and liquidity changes.

•	Engineer features such as:

•	Liquidity lock duration and developer token ownership percentage.

•	Team social presence and project website quality.

•	Transaction volume trends and abnormal token transfers.

•	Tune the model for high precision in identifying high-risk tokens while reducing false positives to avoid flagging legitimate projects.

### Evaluation & Testing:

• Validate the model on a test dataset of known scam projects and legitimate tokens to assess accuracy, precision, and recall.

• Test the alert system for timely notifications, ensuring minimal latency and effective delivery.

• Conduct robustness testing to ensure the model adapts to evolving scam tactics, like stealth marketing or modified liquidity schemes.

### Deployment Readiness:

**Scope of Work**

1.	**Data Collection & Preprocessing**:

•	Gather data on project tokens, contract addresses, and liquidity metrics from blockchain explorers (e.g., Etherscan), and DeFi analytics platforms like Dexscreener or DexTools.

•	Aggregate historical data on known scam tokens for model training, including red flags such as sudden liquidity withdrawals, team token holdings, and low community activity.

•	Standardize and preprocess data to handle various data sources, including token creation dates, social media presence, and developer wallet activity.

2.	**Model Development**:

•	Design a classification model (e.g., random forest, logistic regression, or neural network) that predicts rug pull risk based on contract properties, token distribution, and liquidity changes.

•	Engineer features such as:

•	Liquidity lock duration and developer token ownership percentage.

•	Team social presence and project website quality.

•	Transaction volume trends and abnormal token transfers.

•	Tune the model for high precision in identifying high-risk tokens while reducing false positives to avoid flagging legitimate projects.

3.	**Evaluation & Testing**:

•	Validate the model on a test dataset of known scam projects and legitimate tokens to assess accuracy, precision, and recall.

•	Test the alert system for timely notifications, ensuring minimal latency and effective delivery.

•	Conduct robustness testing to ensure the model adapts to evolving scam tactics, like stealth marketing or modified liquidity schemes.

###	Deployment Readiness:

•	Package the model and a microservice to evaluation queries, ensuring it can integrate with web and mobile platforms.

•	Document setup and deployment steps, including data sourcing requirements, in a shared drive or repository for easy access.


### Requirements

•	**Experience** with fraud detection, blockchain analytics, and machine learning.

•	**Proficiency** in processing transaction data, liquidity metrics, and implementing real-time alerting.

•	**Ability** to develop a reliable, scalable solution that provides crypto users with insights on potential fraud risks.

### Deliverables

•	**Codebase**: Well-documented Python code, including data preprocessing, model architecture, and alert system scripts.

•	**Trained Model**: Exportable tenderflow or pytorch model optimized for scam detection, ready for deployment.

•	**Documentation**: Detailed README with installation instructions, data source setup, and usage guidelines.

•	**Performance Report**: Evaluation metrics, test set results, and model robustness testing summary.

Create an AI-Powered Agent for Early Detection of Crypto Scams and Rug Pulls

USD Coin

## **Objective**

Develop a high-accuracy, real-time bot detection model using the PyTorch Python Framework to identify bot-like behavior in comments on web platforms. The solution should effectively differentiate between authentic human users and bots, even as bot behaviors evolve.

## **Scope of Work**

### **Data Collection & Preprocessing**:

• Use existing labeled datasets (e.g., platform comment data with bot/human labels) and/or generate synthetic data if real-world labeled data is limited.

• Clean, tokenize, and preprocess text comments. Implement NLP techniques to standardize input for the model.

### **Model Development**:

• Develop a deep learning model in PyTorch. Options could include:

• Transformer-based models (e.g., BERT, RoBERTa) fine-tuned for bot detection.

• RNN or LSTM models with attention mechanisms if appropriate.

• Logistic Regression

• Incorporate both text content and potential behavioral features (e.g., comment frequency, sentiment score, posting patterns).

###  **Training & Optimization**:

• Train the model on labeled data and optimize for high precision and recall, especially given the class imbalance often present (bots being a minority).

• Employ techniques like class weighting or oversampling to improve bot identification accuracy.

• Fine-tune hyperparameters, evaluate multiple architectures, and explore regularization methods to improve generalization.

###  **Evaluation & Testing**:

•  Validate the model on a separate test set, ideally containing real-world comments with varying bot characteristics.

•  Provide detailed metrics, including precision, recall, F1 score, and ROC-AUC.

•  Test model robustness against adversarial examples, such as bots mimicking human behavior.

##  **Deployment Readiness**:

•  Package the trained model with PyTorch and document steps for deployment in Google Drive and provide the link on the proposal

•  Include considerations for model scalability, including batch processing for real-time scenarios.

### **Requirements**

• **Experience** with NLP, deep learning models, and PyTorch.

• **Proficiency** in handling text data, model optimization, and classification in the presence of imbalanced datasets.

•  **Ability** to develop a robust, scalable solution suitable for deployment.

### **Deliverables**

• **Codebase**: Well-documented PyTorch code, including model architecture, training pipeline, and evaluation scripts.

• **Trained Model**: Exportable PyTorch model (.pth file) ready for deployment.

• **Documentation**: Detailed README with installation instructions, dataset preparation steps, and usage guidelines.

• **Performance Report**: Analysis with metrics, test set performance, and tuning details.

Develop an AI PyTorch Model to Identify Bot Activity in User Comments

## Goal

Develop an **AI-based model** to monitor and predict Ethereum gas fees in real-time, allowing users to time their transactions for cost savings. The agent should be capable of analyzing gas price fluctuations and offering insights for optimal transaction windows, helping users reduce expenses on gas-heavy networks like Ethereum.

## Scope of Work

### Data Collection & Preprocessing

• Collect historical and real-time gas price data from sourceslike Etherscan or other public blockchain analytics APIs.

• Analyze network congestion patterns and transaction types to understand factors influencing gas prices.

• Clean, aggregate, and preprocess data to identify trends and patterns for effective prediction modeling.

### Model Development

• Develop a predictive model using machine learning techniques in Python (e.g., LSTM or Prophet) to forecast short-term gas price trends over next 7 days. 

• Consider real-time adjustments based on user-specified factors, such as urgency and maximum acceptable gas fees.

Possible model architectures could include:

• Recurrent Neural Networks (RNNs), LSTMs, or GRUs for time series forecasting.

• Transformer-based models for capturing complex patterns in gas fee fluctuations.

### Evaluation & Testing

• Validate the model against historical data to measure the accuracy and reliability of gas fee predictions.

• Test the model's accuracy against historical gas fee data and assess performance with metrics such as Mean Absolute Error (MAE) and Mean Squared Error (MSE).

•  Test the API app for response speed and ensure it aligns with real-time network changes.

### Deployment Readiness

• Prepare the agent for deployment by packaging it as a microservice, integrating API calls to get predictions and trends .
 
• Document deployment steps, including data source configuration, and make it accessible in a shared drive or GitHub repository and include it on the bepro deliverable

### Requirements

• Experience with time-series forecasting, blockchain data analytics, and real-time alerting systems using RNN , Transformers,…

• Proficiency in handling transaction data and implementing machine learning models for prediction.

### Deliverables

• **Codebase**: Documented Python code, including data processing, model architecture and the pythorch trained model.

• **Trained Model**: Ready-to-deploy model (.pth file) capable of real-time predictions.

• **Documentation**: Detailed README with installation steps, data requirements, and usage guidelines

• **Performance Report**: Evaluation metrics, testing outcomes, and improvement recommendations.
 

Smart Gas Fee Predictor Model for Optimal Transaction Timing on Ethereum

![alt](https://storage.googleapis.com/taikai-storage/images/image-1.png)

### Calling all EthCC 7 attendees and talented writers!

We're looking for enthusiastic individuals who will attend [EthCC 7](https://ethcc.io) in Brussels and have a knack for storytelling. If that sounds like you, this task is for you!

### The Task

Write a captivating article that brings your EthCC 7 experience to life. We want to know all the details that made your week unforgettable:

- What were the highlights of the event itself? ✨
- What aspects did you enjoy the most? Are there any areas for improvement? 🙌
- Which side events caught your interest and why? 🎉
- How was your experience exploring Brussels and its culinary delights? 🧁
- Overall, how would you sum up your EthCC 7 adventure? 🌈

By completing this task, your article will be:

- Published on the [LayerX blog](https://blog.layerx.xyz) ✍️
- Shared across the LayerX network and social media channels 📣
- Credited to you as the author 🫶

### Requirements

- Write your report in English 🇬🇧
- Provide a well-structured and engaging narrative 💬
- Extra points if it includes relevant photos or visuals 📸

By completing this task, you know that [LayerX](https://layerx.xyz) has the right to publish and share your article on its blog, network, and social media platforms, with proper attribution to you as the author.



EthCC 7 — The Report

open-marketplace

TAIKAI Token

![Inside Web3 Episode01l!](https://storage.googleapis.com/taikai-static/assets/insideweb3-01.png)

### Analysis of Builder Ecosystems Across the Most Prominent Blockchains

**LayerX**, positioned as a hub for the next generation of product builders, endeavors to feature high-quality blockchain researchers as guest writers on LayerX's websites (Bepro and TAIKAI), enriching the community's understanding of Web3 aspects and publishing detailed and technical reports that helps the builders to take more informed decisions and stay upfront in this dynamic and swift ecosystem. 

This market research published as a **Bepro Task** is part of an initiative to conduct crowd research on trending topics primarily relevant to builders and blockchain developers, further enhancing the community's understanding of these subjects and inviting the community to contribute with relevant content to be published across our channels.

For the first market research, we are seeking a skilled researcher and analyst to conduct an in-depth investigation into the builder ecosystems across various blockchain platforms, including **Ethereum**, **Solana**, **Starknet**, **Polkadot**, and **Cosmos**. The objective is to produce a comprehensive report that elucidates the factors driving growth within these communities, examines network effects, highlights significant recent changes, and does a historical analysis of the evolution of these communities over time.

 

## **📍 Scope of Work**

**1. Platform Analysis:** Conduct a detailed examination of each blockchain platform mentioned, focusing on their respective builder ecosystems. Assess the infrastructure, developer tools, and resources (Grants, Documentation, Mentorship Programs, Developer Relations) available to builders on each platform.

**2. Community Dynamics:** Explore each ecosystem's social and collaborative aspects. Analyze the vibrancy of developer communities, participation in hackathons, developer forums, and other community-driven initiatives.

**3. Network Effects:** Investigate the network effects within each ecosystem. Evaluate factors such as the number of projects built, active users, transaction volume, and liquidity. Assess how these network effects contribute to ecosystem growth.

**4. Factors Contributing to Growth:** Identify and analyze the key factors contributing to the growth of each builder ecosystem. This may include developer incentives, protocol improvements, strategic partnerships, and regulatory considerations.

**5. Recent Changes:** Provide an overview of major changes or developments within each ecosystem. This could include protocol upgrades, new feature releases, governance changes, or shifts in developer sentiment.

**6. Comparative Analysis:** Offer a comparative analysis of the builder ecosystems across different blockchains. Highlight the strengths, weaknesses, and unique selling points of each platform.

## **📦 Deliverables**

1. **Comprehensive Report:** A well-structured report detailing the findings of the analysis, including insights into each blockchain platform's builder ecosystem, community dynamics, network effects, growth factors, and recent changes.
2. **Data Visualization:** Incorporate data visualizations such as charts, graphs, and tables to present key metrics and trends and metric evolution effectively.
3. **Executive Summary:** Provide an executive summary summarizing the essential findings and insights of the report in a concise and digestible format.
4. Submit the final document in a Google Doc format, with access exclusively granted to [mario@layerx.xyz](mailto:mario@layerx.xyz), [helder@layerx.xyz](mailto:helder@layerx.xyz), [moshmage@layerx.xyz](mailto:moshmage@layerx.xyz), [henriquesilva@layerx.xyz](mailto:henriquesilva@layerx.xyz), [henrique@layerx.xyz](mailto:henrique@layerx.xyz) and [carlos@layerx.xyz](mailto:carlos@layerx.xyz) 

## **📚 Authenticity and Credibility**

- Ensure that all information and data presented in the report are accurate, well-researched, and obtained from reputable sources.
- Cite sources appropriately for any data, statistics, or information from external resources.
- Maintain objectivity and avoid biases or personal opinions that could compromise the report's integrity.
- Disclose any potential conflicts of interest or affiliations that could influence the research or analysis.
- Provide transparency regarding the research methodology, data collection processes, and analytical techniques.

This community report generated by this Bepro task will be published on LayerX’s websites and distribution channels and promoted by the @LayerX Team to their partners. 

The team will analyze all the reports and select the best one to be refined, published on our website, and take the prize. 🏆

Let the Research Begin 📚

*🪧 This heralds the launch of **'Inside Web3,'** a forthcoming series of market research initiatives facilitated through community Bepro Tasks. These crowd-generated publications are poised to offer timely insights into topics crucial for builders and blockchain developers.”*










A Closer Look at Builder Ecosystems

Comprehensive report about the Web3 builder ecosystem, analysing blockchains and comparing some metrics data.

Inside Web3. A closer look into builder ecosystem

Test deliverable

Inside Web3: A Closer Look at Builder  Ecosystems 🧐

BEPRO Network

Get ready to tickle your funny bone and showcase your creative genius in the "Hackfest Meme Mastery Challenge" – the ultimate meme contest! In this laughter-inducing competition, we invite meme maestros from all corners of the internet to flex their humor, wit, and Photoshop skills for a chance to win some fantastic prizes.

## 🤣 Contest Rules:

Theme: Every great meme begins with a concept, so we've got a theme to guide your creativity. This time, we're diving into the world of "Retro Revival." Time to give those vintage vibes a hilarious modern twist!

Format: Memes come in all shapes and sizes, so feel free to express your humor in image macros, GIFs, videos, or any other meme format. Get as creative as you want!

Originality: We love a good classic, but your meme should be your original creation. No recycled, old memes, please!

Keep it Clean: While we appreciate humor in all its forms, let's keep it friendly and fun. No offensive, hateful, or inappropriate content will be accepted.

📷 How to Enter:

1. Create your meme masterpiece about the Hackfest 
4. Share it on your social media accounts (Twitter, Instagram, or Facebook) with the hashtag #HackFestMeme
3. Submit your meme's image along with your contact information 

Our panel of curators will be evaluating entries based on creativity, humor, and how well they adhere to the "Retro Revival" theme.

Join the "HackfestMeme Mastery Challenge" and let your meme-making skills shine! Whether you're a seasoned meme lord or just getting started, everyone is welcome to participate. Get ready to bring back the humor of the past with a modern twist and leave us all in stitches. May the funniest memer win! 😂👑

PS: Only Original Memes are allowed to participate





🧑‍💻 HackFest Meme Mastery Challenge 😂

## ETH Belgrade   - LayerX Bounty 
This bounty aims to build a web3 project that uses LayerX dappKit and Rocket Kit frameworks.

Themes:

- NFT
- DeFi
- Infrastructure
- Integrations
- Developer Tooling
- Gaming
- Common Goods
- Decentralized Identity

### Deliverable 👩‍💻
You will only be entitled to receive the prize if you submit a pull request with the open source code on [Bepro App](https://app.bepro.network).

1. Go to [Bepro App](https://app.bepro.network)
2. Connect your Wallet and your Github Account. 
3. Click on Start Working
4. Open a Pull Request with your code.
5. Set your Pull Request as "Ready for Review"
6. You are Done! 🙌

*Note: You will need a Metamask wallet to open a pull request and connect to Polygon Main Net Chain. 

### Payment 🏆
Our Team will analyze all the projects and open a proposal for the winning project. In 7 days once the proposal is settled, the prize will be transferred automatically to the wallet that submitted the pull request.

### dappKit

dappKit is a powerful Typescript/Javascript SDK that allows you to develop beautiful web3 apps in a matter of minutes. We mean it.

This powerful framework allows you to connect, and interact with ERC-20, ERC-721, and ERC-1155, a wallet, or your custom contract in a couple of lines.

-   [Website](https://dappkit.dev/)
-   [Documentation](https://docs.dappkit.dev/start-building)
-   [API Reference](https://sdk.dappkit.dev/)
-   [Source Code](https://github.com/taikai/dappkit)
-   [Discord](https://discord.gg/layerx)
-   [NPM](https://www.npmjs.com/package/@taikai/dappkit)

### Rocket Kit

Rocket Kit is an React.js open source UI Design System used by LayerX products. This design system is built around the Atomic Methodoly and provides you with atoms, molecules, organisms that working together to create effective interface design systems without too much effort.

-   [Storybook](https://main--600c295ccb36300021e7d82f.chromatic.com/)
-   [Chromatic Library](https://www.chromatic.com/library?appId=600c295ccb36300021e7d82f&branch=main)
-   [Source Code](https://github.com/taikai/rocket-kit)
-   [NPM](https://www.npmjs.com/package/@taikai/rocket-kit)

If you need any help please contact our team  [gm@layerx.xyz](mailto:gm@layerx.xyz)

🚨  **Important Disclaimer**  - This bounty could only be solved by ETH Belgrade Participants. A hacker that is not a participant is not entitled to get their solution accepted and receive the prize

Happy Hacking 🎯



bepro

LayerX Bounty @ ETH Belgrade - Create a dappKit + Rocket Kit Dapp 

The goal of this bounty is to include the locked $BEPRO on Uniswap v3 BEPRO/USDC pool on the TVL provided by Defi Llama.

The TVL from UniswapV3 should be included in the Polygon TVL Section

Defi Llama Original Project
https://defillama.com/protocol/bepro-network

Bepro Folder on Defi Llama Adapters
https://github.com/taikai/DefiLlama-Adapters/tree/main/projects/bepro

The BEPRO/ETH Pool on Uniswap v3 is deployed on address 0x1a710f650e0c829c55b20a31fe1f8b45907baddc as you can confirm [here](https://info.uniswap.org/#/polygon/pools/0x1a710f650e0c829c55b20a31fe1f8b45907baddc).



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