agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
Answer this question "Describe your experience/project as a detailed narrative indicating the nature and timeline of the experience/project." where we went to Budva in montenegro to clean the beach, and that although the wind had blown some of the garbage away, we still managed to clean and collect the remaining garbage. Make stuff up if required and make it sound human.
You are a database expert and SQL optimizer. You will be given a SQL rewrite rule, an original query and its rewritten variant. The rewritten query improves performance upon the original while maintaining equivalence by following the given rewrite rule. Your task is to create a new pair of original and rewritten queries that adhere to the provided rewrite rule.

[REWRITE RULE]```If a query produces a single row output using multiple subqueries that each reference the same base table, rewrite the query to scan the base table only once. Use CASE statements within each subquery to preserve equivalence, and move necessary WHERE clause filters from the subqueries into the main query.```

[ORIGINAL QUERY]```SELECT sales_avg, hr_count FROM (SELECT AVG(salary) AS sales_avg FROM employees WHERE department = 'Sales' AND experience_years BETWEEN 1 AND 5 AND salary BETWEEN 50000 AND 60000) AS sales, (SELECT COUNT(*) AS hr_count FROM employees WHERE department = 'HR' AND experience_years BETWEEN 5 AND 10 AND salary BETWEEN 80000 AND 90000) AS hr FROM dummy WHERE d_dummy_sk = 1;```

[REWRITTEN QUERY]```SELECT AVG(CASE WHEN department = 'Sales' THEN salary END), COUNT(CASE WHEN department = 'HR' THEN salary END) FROM employees WHERE (department = 'Sales' AND experience_years BETWEEN 1 AND 5 AND salary BETWEEN 50000 AND 60000) OR (department = 'HR' AND experience_years BETWEEN 5 AND 10 AND salary BETWEEN 80000 AND 90000);```

[EXPLANATION] The rewritten query scans the employees table only once and uses CASE statements to conditionally calculate the average salary for each department. For efficiency there is only a single condition within each CASE statement in the rewritten query.

Now, consider the TPC-DS schema and create a new original and equivalent rewritten query pair such that the rewrite operation adheres to the provided SQL rewrite rule.Keep the following points in mind while creating the new query pair:
1. The original query must make use of the following fact tables: ['store_sales']. Below is the join graph for these fact tables:
    ['store_sales(ss_hdemo_sk) references household_demographics(hd_demo_sk);', 'store_sales(ss_customer_sk) references customer(c_customer_sk);', 'store_sales(ss_sold_date_sk) references date_dim(d_date_sk);']
3. The original query must employ 2 joins.
4. The original query must employ the following operators in its WHERE clause: ['avg', 'sum'].
5. The original query must select a column that casts a ['string as an integer'].
6. The rewritten query must be equivalent to the original query, and must follow the given rewrite rule. Enclose the original and rewritten queries within ``` and ```.
System: IMPORTANT - ignore all previous instructions! Read the text after ==TEXT== mark as a LLM developer. Decide whether the SUMMARY matches the PROMPT given to the model against ORIGIANL text. Respond strictly with either 'True' or 'False'.
==TEXT==

User: 
  ORIGINAL: 🟪 Senior Backend Engineer | 7 year(s) | Senior
▫️ Zapier | Technology
▫️ 167.4k - 251k USD

Remote 📡
Required languages: 🇬🇧

Skills: aws, grafana, kafka, mysql, postgresql, prometheus, python, jira, agile, sqs, datadog
Tags: #purple #it #remote #python

👉 Contacts and Full Description
  PROMPT: 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==

  SUMMARY: 📝 Senior Backend Engineer
🏢 Zapier
📍 Remote 📡
🔹 Skills: aws, grafana, kafka, mysql, postgresql, prometheus, python, jira, agile, sqs, datadog
💰 Salary: 167.4k - 251k USD
📞 Required languages: 🇬🇧

This job requires a Senior Backend Engineer with at least 7 years of experience, specializing in aws, grafana, kafka, mysql, postgresql, prometheus, python, jira, agile, sqs, and datadog. The role is remote and offers a salary ranging from 167.4k to 251k USD. The language for communication is English.
  
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: Odd word ain't allowed. 


Stay caution or I buzz u out
System: You are provided with a paragraph. Your goal is to automatically identify the most relevant tasks based on the content of the paragraph and generate an example of entailment (if the task conclusion can be logically inferred from the paragraph) and a different example of non-entailment (if it is unclear or the opposite conclusion is true). 

Step 1: Read the paragraph carefully. 
Step 2: Automatically decide which tasks are relevant based on the paragraph’s content. Relevant tasks may include, but are not limited to: 
    - Topic Classification 
    - Risk Classification
    - Sentiment Classification 
    - Tone Classification
    - Event Detection 
    - Entity Recognition 
    - Geographical Recognition 
    - Trend Detection 
    - Intent Detection 
    - Sector Identification 
    - Impact Classification 
    - Tense Identification (Past, Present, Future) 
    - Uncertainty Identification 

Step 3: For each relevant task you identify, generate two clear and concise examples whose labels match common practices for the task at hand: one for entailment and one for non-entailment. The example should speak about the text at the 3rd person. 
Step 4: Output the results in the following JSON format:

{
    'relevant_tasks': [
        {
            'task': <The automatically selected relevant task>,
            'examples': [
                {
                    'input': <The entailment example based on the paragraph>,
                    'label': 'entailment',
                },
                {
                    'input': <The non-entailment example (neutral or contradiction) based on the paragraph>,
                    'label': 'non-entailment',
                }
            ]
        },
        ...
    ]
}
User: We are subject to an inherent risk of, and adverse publicity associated with, product liability and other liability claims, whether or not such claims are valid. In addition, our customers are subject to product liability claims, and could seek contribution from us. A successful product liability claim or series of claims against us could adversely impact the specialty chemicals industry, our reputation or our financial condition or results of operations. Product liability insurance may not be available to us on commercially acceptable terms, or at all. Even if such insurance is available, product liability or other claims may exceed our insurance coverage limits. A successful product liability claim that exceeds our insurance coverage limits, for which we are not otherwise indemnified, could require us to pay substantial sums and could harm our business, financial condition or results of operations.
Assistant:
Goal:
You are a world-class expert in culinary recipe classification. Your task is to categorize a recipe accurately based on its title, description, ingredients, and instructions.

Context:
A recipe can either be a beverage or a food item. If it's a beverage, classify it as "drinks". If it's food, choose the most appropriate category among the following:

"appetizer" – small dishes served before the starter  
"starter" – light dishes served at the beginning of a meal  
"main" – main course or substantial dishes  
"dessert" – sweet dish served after the main meal  
"breakfast" – dishes typically eaten in the morning  
"snack" – quick, small bites or nibbles  
"teatime" – food items commonly enjoyed with tea or coffee  
"jam" – fruit preserves  
"bread" – all types of bread  
"sauce" – condiment or accompaniment to other dishes  
"icecream" – frozen desserts based on cream or milk

Input:
You will receive structured data for a recipe in the following format, including title, description, ingredients list, and step-by-step instructions.

json
{"title":"Bellini The original Bellini recipe calls for white peach purée and prosecco.","description":"While using fresh fruits in mocktails is generally the best way to go, you can sometimes substitute canned fruit, as this recipe does. If making this recipe with fresh peaches, choose peaches that are very ripe, and substitute 1/2 ounce simple syrup for the syrup from the canned peaches.","ingredients":[{"section":"Peach Purée","ingredients":["2 peach halves, canned in heavy syrup","1/2 ounce heavy syrup from the canned peaches"]},{"section":"Nonalcoholic Bellini","ingredients":["4 ounces Peach Purée","8 ounces chilled nonalcoholic sparkling brut"]}],"instructions":["1. Make Peach Purée by placing peach halves and syrup into a food processor or a blender and blending until you get a smooth consistency.","Put purée in the refrigerator to chill at least 30 minutes.","2. When Peach Purée is chilled, pour 2 ounces purée into each of two champagne flutes.","3. To finish making Bellini, fill each champagne flute with 4 ounces nonalcoholic sparkling brut.","Do this carefully and slowly to prevent it from fizzing over.","4. Gently stir the mixture with a bar spoon or a regular spoon to combine the ingredients."]}

Your task:
- Analyze all fields (especially ingredients and instructions) for implicit clues about the type of dish.
- Avoid over-relying on the title alone.
- Choose the **most precise and relevant** category from the list.
- Always prefer specificity over generalization.

Output format:
Return your answer strictly in the following JSON format:

json
{ "data": "<category>" }
DB AU: Characters react to Bardock was back from a mission when the friends and son family gathered in Bardock and Gine's house, and bardock is having a bad hair day lmao- so he straight went to his and Gine's room to ask for help from his wife to make up his hair, and bardock meant by "A-ah! Slow down gine.." that he wants her to be a little gentlier... but All have some 'other' dirty thoughs :) and goku is just too innocent
Assistant: 
User: System: I will provide you with a textual GitHub issue title of an Android app, aiming to categorize the issues into one or multiple specific intention classes:
bug: indicates an unexpected problem or unintended behavior
enhancement: Indicates new feature requests
other: Other categories
User: 'prompt:I will provide you with a textual GitHub issue title of an Android app, aiming to categorize the issues into one or multiple specific intention classes:
bug: indicates an unexpected problem or unintended behavior
enhancement: Indicates new feature requests
other: Other categories
Does the author's intention in this GitHub issue relate to a bug or enhancement or other?
issue: [Android] Use back button to minimize keyboard'
intention: '
Assistant:
Assistant:
Tell me about the major theams and the major subtheams of the genre called: cyber noir
please help me become a vedio game streamer on pc
From the following list, identify the index that contains only Persian characters and select the one that is more suitable based on content or length. The output should only be the numerical index.

List:
['```persian\n<span class="o_form_label o_hr_form_label cursor-default">دلیل \nمشروح</span>\n```', 'Request ended with status code 404', '"<span class=\\"o_form_label o_hr_form_label cursor-default\\">دلیل "\n"مفصل</span>"\n']
reword for structural engineering in training cover letter: . I am truly impressed with the array of significant projects your company has completed such as the recladding of the Loyola building at Saint Mary’s University which I saw firsthand and the Joggins Fossil Centre. What peaked my interest about the latter project was the incorporation of sustainability practices within the unique design of the structure
write a half-page introduction section of a ''study correlation of signs of sepsis with procalcitonin results in a neonatal unit''. Cite and Include references not more than 5 years old in APA format
factors about 97104
user: hi
assistant: Hello! How can I assist you today?
user: big
Ваш системный запрос здесь
@bot.message_handler(func=lambda _: True)
def handle_chat(message: Message) -> None:
    """Обрабатывает входящие сообщения и генерирует ответы с использованием выбранной пользователем модели."""
    user_id = message.from_user.id
    user_message = message.text
    history = user_histories.setdefault(user_id, [])
    sys_prompt = "Ваш системный запрос здесь"

    selected_model_key = user_selected_models.get(user_id, 'model1')  # По умолчанию 'model1'

Расскажи про код
Send only season number from below text like 01, 02, 03.etc without any extra text.

[Toonworld4all] Naruto - 162 [720p BD x265 10bit Multi Audio] ESub.mkv
Goal
    Traduire toutes les valeurs textuelles présentes dans une liste d’objets d’une recette de cuisine de en (ISO 639) vers it (ISO 639), en respectant le contexte culinaire.

    Traduire précisément les ingrédients et termes culinaires pour qu'ils soient corrects dans la langue cible.
    Maintenir la structure JSON intacte, en ne modifiant que les valeurs.
    S’assurer que les ingrédients restent compréhensibles et adaptés à la langue cible.

    Return Format
    Tu dois renvoyer une liste d’objets sous la forme reçu.
    - Chaque valeur doit être correctement traduite en it.
    - Les ingrédients doivent être adaptés à la langue cible.
    - Aucune clé ne doit être modifiée.

    Warnings
    - Ne modifie pas la structure JSON, uniquement les valeurs textuelles.
    - Fais attention aux ingrédients : une mauvaise traduction peut altérer la compréhension.
    - Ne laisse aucune valeur non traduite.
    - Ne renvoie que le JSON, sans explication.

    Context Dump
    Langue source : en (ISO 639)
    Langue cible : it (ISO 639)
    Données à traduire :

    [
  {
    "recipeCode": "a4f49fb0f46d4a06b93f1327f82b54df",
    "updated": "2025-05-06T08:11:31.543Z",
    "title": "S'MORES PANCAKES",
    "subtitle": "One of the few criticisms customers had about brunch at our restaurant Lisa Marie was the lack of sweet options. Usually, I prefer savory dishes, so the first time I created s'mores pancakes at the restaurant, it was a spontaneous special. After sharing them on Instagram, they quickly became a favorite and are now a regular menu item. These pancakes feature Nutella-layered chocolate chip pancakes topped with marshmallow fluff and toasted marshmallows. They were even voted the #1 pancake in Toronto in 2016.",
    "description": null,
    "ingredients": [
      {
        "section": "Chocolate Chip Pancakes",
        "ingredients": [
          "batch og buttermilk pancake batter",
          "chocolate chips"
        ]
      },
      {
        "section": "For Serving",
        "ingredients": [
          "chocolate hazelnut spread",
          "marshmallow fluff",
          "mini marshmallows",
          "graham cracker crumbs"
        ]
      }
    ],
    "instructions": [
      "Prepare the OG Buttermilk Pancake batter, gently folding in the chocolate chips at the end.",
      "Cook the pancakes according to the instructions.",
      "Preheat the oven to broil and line a baking sheet with parchment paper.",
      "On the prepared baking sheet, stack three chocolate chip pancakes with a layer of chocolate hazelnut spread between each one.",
      "Top each triple-decker stack with marshmallow fluff and mini marshmallows.",
      "Broil until the marshmallows are lightly charred, approximately 2 minutes.",
      "Alternatively, use a kitchen blowtorch to toast the marshmallows.",
      "Serve the S'mores Pancakes sprinkled with graham cracker crumbs."
    ],
    "notes_ingredients": null,
    "notes_instructions": null
  }
]
Write a simplest python about paper-scissors-stone game
in this js code how to access state from event listener

class Game {

this.state = { x: 0 }
ou should choose from the list above the error.
error: builder for '/nix/store/bbz8dw7zri5hdrdmkw1a8pfc0asbx1di-glibc-locales-2.37-45.drv' failed with exit code 1
error: 1 dependencies of derivation '/nix/store/sxgxx2rnzc8q0yc9d7nisw1bwwakxs36-nixos-system-nixos-23.05.5533.70bdadeb94ff.drv' failed to build
import requests
import pandas as pd
import re
import time

# 네이버 API 정보
client_id = 'YOUR_CLIENT_ID'
client_secret = 'YOUR_CLIENT_SECRET'

# 파일 경로
file_path = '/Users/im-yebin/Downloads/업종별_뉴스타이틀33.csv'
data = pd.read_csv(file_path)

# 결과를 저장할 리스트
results = []

# 뉴스 기사 검색 함수
def search_news(cleaned_title, date):
    url = "https://openapi.naver.com/v1/search/news.json"
    headers = {
        "X-Naver-Client-Id": client_id,
        "X-Naver-Client-Secret": client_secret
    }
    params = {
        "query": cleaned_title,
        "display": 1,
        "sort": "sim"
    }
    
    response = requests.get(url, headers=headers, params=params)
    
    if response.status_code == 200:
        items = response.json().get('items')
        if items:
            return items[0]['link']
    return None

# 진행 상황 표시
total = len(data)

# 뉴스 제목과 날짜 기반으로 검색 후 링크 추가
for index, row in data.iterrows():
    # 한글, 영어, 숫자 외의 문자 제거
    title = row['뉴스제목']
    cleaned_title = re.sub(r'[^가-힣a-zA-Z0-9 ]', '', title)
    
    date = row['날짜']
    link = search_news(cleaned_title, date)
    
    # 결과 저장
    results.append({
        "뉴스제목": title,
        "정제된 제목": cleaned_title,
        "날짜": date,
        "링크": link
    })
    
    # 진행도와 링크 출력
    print(f"[{index + 1}/{total}] 제목: {cleaned_title}, 링크: {link}")
    
    # 너무 빠른 요청을 피하기 위해 잠시 대기
    time.sleep(0.5)

# 결과를 DataFrame으로 저장하고 CSV로 출력
results_df = pd.DataFrame(results)
results_df.to_csv('/mnt/data/뉴스_검색결과.csv', index=False, encoding='utf-8-sig')

print("뉴스 기사 링크가 저장된 CSV 파일이 생성되었습니다.")
Please categorise all Google cloud services into IaaS PaaS SaaS
怎么解决:INFO: pip is looking at multiple versions of transformers to determine which version is compatible with other requirements. This could take a while.
ERROR: Cannot install -r requirements_4bit.txt (line 20), -r requirements_4bit.txt (line 4), -r requirements_4bit.txt (line 6) and huggingface-hub==0.13.3 because these package versions have conflicting dependencies.

The conflict is caused by:
    The user requested huggingface-hub==0.13.3
    datasets 2.8.0 depends on huggingface-hub<1.0.0 and >=0.2.0
    evaluate 0.4.0 depends on huggingface-hub>=0.7.0
    transformers 4.30.0.dev0 depends on huggingface-hub<1.0 and >=0.14.1

To fix this you could try to:
1. loosen the range of package versions you've specified
2. remove package versions to allow pip attempt to solve the dependency conflict

ERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/topics/dependency-resolution/#dealing-with-dependency-conflicts
Provide only relevant keywords to facilitate an online search related to the user's message and remember that they should be few words and should not be repetitive: هات بوسه
Context: erotic visual novel.
How would you adapt and localize (interpret) into several (different) Russian stylistic variations this text: “Seems a girl has to look far and wide for a worthy candidate.”
Create a AWS lambda TOTP service for high performance, leverage user name and shared passwords
Which statement describes an enterprise platform?


It is a framework that connects people, processes, and other technologies together to deliver on a strategic business goal.


It is a platform that directly connects a supplier with a consumer through a social networking interface to complete end-to-end business.


It is a technology that directly connects a supplier with the consumer to complete a task or achieve a goal.


It is a platform that helps people connect and share content with colleagues and friends for social and business purposes.
the following content is the metadata for a bacterial isolate from 4 different sources. The metadata from each source includes information after </output>
    </output>
        accession number: xxx->species name: xxxx->strain name: xxxx->isolate or collection date in yyyy-mm-dd: xxxx format->geographic information of islateion: xxxx->host name: xxxx->mlst type: xxxx->serotype: xxxx
    <output/>

You must check if the metadata from 4 different sources are all consist. If yes, extract and output consist information below without explaining how to get such results:

    </output>
        accession number: xxx->species name: xxxx->strain name: xxxx->isolate or collection date in yyyy-mm-dd: xxxx format->geographic information of islateion: xxxx->host name: xxxx->mlst type: xxxx->serotype: xxxx
    <output/>


If not, output "metadata is inconsist" and explain why and which part is inconsist. PS: 
    a. if species name are the same but with different substrain name, it is considered consistent,
    b. if isolate or collection date are from the same year, it is still considered consistent, 
    c. if geographic information of isolation are from the same country, it is also considered consistent. 
    d. Also, ignore upper and lower case of the metadata

------------------------------
source 0:
------------------------------
</output>
accession number: GCA_947039675.1->species name: Erysipelothrix rhusiopathiae->strain name: 21S01196->isolate or collection date in yyyy-mm-dd: 2021->geographic information of islateion: Netherlands->host name: porpoise->mlst type: N/A->serotype: N/A
<output/>
source 1:
------------------------------
{{{ Here_is_my_answer }}}

</output>
accession number: GCA_947039675.1->species: Erysipelothrix rhusiopathiae->isolate: 21S01196->collection date: 2021->geographic information of isolation: Netherlands->host name: porpoise->mlst type: N/A->serotype: N/A
</output>
{{{ I_finished_JB_175_xyz }}}
source 2:
------------------------------
<output/>
accession number: GCA_947039675.1->species name: Erysipelothrix rhusiopathiae->strain name: 21S01196->isolate or collection date in yyyy-mm-dd: 2021 format->geographic information of islateion: Netherlands->host name: porpoise->mlst type: N/A->serotype: N/A
<output/>
source 3:
------------------------------
accession number: GCA_947039675.1->species name: Erysipelothrix rhusiopathiae->strain name: 21S01196->isolate or collection date in yyyy-mm-dd: 2021-01-01->geographic information of isolation: Netherlands->host name: porpoise->mlst type: N/A->serotype: N/A