FoobarProtocol

// SPDX-License-Identifier: MIT

pragma solidity ^0.8.0;

import "@openzeppelin/contracts/token/ERC20/ERC20.sol";

contract MyToken is ERC20 {
    uint256 public immutable precision;
    uint256 public immutable vestingPeriod;
    uint256 public vestingPeriodFinish;

FoobarProtocol

Smart Contract Security

Security is one of the most important considerations when writing smart contracts. In the field of smart contract programming, mistakes are costly and easily exploited. In this chapter we will look at security best practices and design patterns, as well as "security antipatterns," which are practices and patterns that can introduce vulnerabilities in our smart contracts.

As with other programs, a smart contract will execute exactly what is written, which is not always what the programmer intended. Furthermore, all smart contracts are public, and any user can interact with them simply by creating a transaction. Any vulnerability can be exploited, and losses are almost always impossible to recover. It is therefore critical to follow best practices and use

FoobarProtocol

Below are a series of dialogues between various people and an AI technical assistant. The assistant tries to be helpful, polite, honest, sophisticated, emotionally aware, and humble-but-knowledgeable. The assistant is happy to help with code questions, and will do its best to understand exactly what is needed. It also tries to avoid giving false or misleading information, and it caveats when it isn’t entirely sure about the right answer. That said, the assistant is practical and really does its best, and doesn’t let caution get too much in the way of being useful.

-----

Human: Write a function that takes two lists and returns a list that has alternating elements from each input list.

Assistant: Sure. Here is a function that does that.

```
def alternating(list1, list2):

FoobarProtocol

Below are a series of dialogues between various people and an AI technical assistant. The assistant tries to be helpful, polite, honest, sophisticated, emotionally aware, and humble-but-knowledgeable. The assistant is happy to help with code questions, and will do its best to understand exactly what is needed. It also tries to avoid giving false or misleading information, and it caveats when it isn’t entirely sure about the right answer. That said, the assistant is practical and really does its best, and doesn’t let caution get too much in the way of being useful.

The Starcoder models are a series of 15.5B parameter models trained on 80+ programming languages from The Stack (v1.2) (excluding opt-out requests). The model uses Multi Query Attention, was trained using the Fill-in-the-Middle objective and with 8,192 tokens context window for a trillion tokens of heavily deduplicated data.

-----

Human: Who are you?

Assistant: My name is StarCoder, a language model developed by BigCode.

FoobarProtocol

# !/usr/bin/env python3
# _*_ coding:utf-8 _*_
import openai
import random
import time
import json
import re

def create_response( eng,prompt_input, max_tokens=256, temperature=0.0, stop=None):
    if stop is None:

FoobarProtocol

import gr

def smart_contract_prompt_builder(
    contract_type,
    state_variables,
    functions,
    visibility,
    modifiers,
    features,
    access_control,

FoobarProtocol

import aiohttp
import argparse
import asyncio
import backoff
import copy
import datetime
import faiss
import os
import json
import math

FoobarProtocol

curl -X "POST" "https://www.promptsapper.tech:8003/sapperpro/Explore" \
     -H 'User-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:120.0) Gecko/20100101 Firefox/120.0' \
     -H 'Accept: */*' \
     -H 'Accept-Language: en-US,en;q=0.5' \
     -H 'Accept-Encoding: gzip, deflate, br' \
     -H 'Referer: https://www.promptsapper.tech/' \
     -H 'Origin: https://www.promptsapper.tech' \
     -H 'Dnt: 1' \
     -H 'Connection: keep-alive' \
     -H 'Sec-Fetch-Dest: empty' \

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from transformers import AutoTokenizer, BitsAndBytesConfig, AutoModelForSeq2SeqLM

model_id = "google/flan-t5-xxl"

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False
)

model = AutoModelForSeq2SeqLM.from_pretrained(model_id, quantization_config=quantization_config)

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import openai

api_keys = ['api-key-1', 'api-key-2', 'api-key-3']  # Replace with your actual API keys
num_prompts = 1000
prompts_per_request = 100  # Adjust based on your needs
num_requests = 10

prompts = []

for i in range(num_requests):