JSeam2

import requests

def main():                
    # gen-witness and prove
    try:
        res = requests.post(
            url="https://archon-v0.ezkl.xyz/recipe",
            headers={
                "X-API-KEY": "CHANGE_ME",
                "Content-Type": "application/json",

JSeam2

import requests

def main():                
    # gen-witness and prove
    try:
        res = requests.post(
            url="https://archon-v0.ezkl.xyz/recipe",
            headers={
                "X-API-KEY": "REPLACE_ME",
                "Content-Type": "application/json",

JSeam2

import ccxt
import time
from datetime import datetime
import math
import logging
from typing import Dict, Optional, Literal

# Configure logging
logging.basicConfig(
    level=logging.INFO,

JSeam2

gpg --output secrets.out -d secrets.gpg
gpgconf --reload gpg-agent

JSeam2

gpg --output secrets.gpg --symmetric --no-symkey-cache --cipher-algo AES256 secrets

JSeam2

// SPDX-License-Identifier: MIT
pragma solidity ^0.8.17;

interface Verifier {
    function verify(
        uint256[] memory pubInputs,
        bytes memory proof
    ) external view returns(bool);
}

JSeam2

Gelkin Explained

Smart contracts on the EVM are very limited. This makes it challenging to create applications that require more compute and data beyond the state of the blockchain. Furthermore, data is mostly public, making it difficult to remain private. ZKP (Zero Knowledge Proofs) offer a way to scale Ethereum and help offer privacy where needed. However, writing custom circuits novel applications is difficult.

Using machine learning, developers don't need to write custom circuits and can specify intended behaviors and have a computer learn the circuit. Leveraging on the EZKL library, Gelkin helps developers extend their smart contracts with ZKML (Zero Knowledge Machine Learning). Developers can then extend their smart contracts by leveraging on existing verifiers or by deploying verifiers themselves through the service.

Through Gelkin, EZKL, and ZKML, smart contracts will be able to offer more complex functionality like:

On-chain credit scoring to offer more kinds of DeFi loan arrangements

Anomaly d

JSeam2

Sponsor Bounty Explanations

Covalent Unified API Endpoints

Covalent API was used to help provide data to train the ML models using the Gelkin

Developer Infrastructure/Tooling Mantle

Gelkin allows developers to deploy ZKML verifiers on Mantle network

Build on Pokt Network

Gelkin uses Pokt Network RPC endpoints to deploy the ZKML verifiers

JSeam2

import web3

w3 = web3.Web3()

prefix = "\x19Ethereum Signed Message:\n"
message = "Verify your Eth address. zveyIPmNyiaCNRjJ9hsbYqEJPb7gJTgNzm2ybM_F-SQ"
signed = "0x4481dffa200bc4c9cadad4083002585fbe20aebef1916aa7d58e66682860097747df3f7fb608c30ff19adbf52ab4ef2a8eabb553857373b0767d43afd83f09ea1c"

prefix = w3.toBytes(text=prefix)
msg = w3.toBytes(text=message)

JSeam2

contract ERC20Wrapper is ERC20, ERC1155Receiver {
    uint256 public tokenId;
    IShareToken public shareToken;
    IERC20 public cash;
    address public augurFoundry;

    /**
     * @dev sets values for
     * @param _augurFoundry A trusted factory contract so that users can wrap multiple tokens in one
     * transaction without giving individual approvals