agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
Goal
    Générer une image de suggestion de présentation d'un plat en fonction de sa description, en intégrant un style photographique spécifique.

    1. Utiliser la description fournie pour définir l'apparence du plat.
    2. Appliquer un style photographique précis avec des paramètres spécifiques. 
    4. Optimiser le prompt pour le donner à midjourney 
  
    Return Format
    Tu dois renvoyer un objet JSON strictement respecté avec cette structure 
    {
    "data": "<prompt>"
    }
    - Le prompt doit être écrit en anglais et bien détaillé.

    Warnings
    - Respecte le format JSON strict.
    - N’explique pas la démarche, renvoie uniquement l’objet JSON.
    - Le prompt doit inclure tous les éléments demandés.

    Context Dump
    Description fournie : Create a mouth-watering image of Out-n-in burgers, featuring juicy beef patties topped with melty daiya vegan cheddar cheese, nestled between golden, toasted allergy-friendly hamburger buns. The burgers are beautifully assembled with crisp iceberg lettuce, vibrant slices of ripe beefsteak tomato, and sweet onion rings, all drizzled with a homemade sauce of rice milk mayonnaise, ketchup, sweet relish, sugar, and cider vinegar. The background should suggest a cozy outdoor barbecue setting, with a hint of greenery and a warm, inviting sunlight casting a glow on the burgers, emphasizing their deliciousness and freshness.
    Style souhaitée : A top-down photo of plate with <description>. a dish on an elegant plate, accompanied by water in a glass, placed against a white background. The table is adorned with orange napkins for serving the meal. Captured from above using a Canon EOS R5 camera with high resolution. Studio lighting creates soft shadows around the food to highlight its colors and textures. Professional color grading enhances the visual appeal.
the following content is the metadata for a bacterial isolate from 3 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 3 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->accesssion number" followed by 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. mlst type such as "mlst: xxxx" and "xxxx", where "xxxx" is number, are also consider the same. also, "mlst" may mispell as "mlsty", you need correct it before comparing
    e. Ignore upper and lower case of the metadata

------------------------------
source 0:
------------------------------
<output>
accession number: GCA_029072445.1->species name: Erysipelothrix rhusiopathiae->strain name: EMAI_155->isolate or collection date in yyyy-mm-dd: 2017-09-12 format->geographic information of islateion: Australia->host name: pig->mlst type: 76->serotype: N/A->vaccine: no
<output/>
source 1:
------------------------------
accession number: GCA_029072445.1->species name: Erysipelothrix rhusiopathiae->strain name: EMAI_155->isolate or collection date in yyyy-mm-dd: 2017-09-12->geographic information of islateion: Australia->host name: pig->mlst type: 76->serotype: N/A->vaccine: no


source 2:
------------------------------
<output>
accession number: GCA_029072445.1->species name: Erysipelothrix rhusiopathiae->strain name: EMAI_155->isolate or collection date in yyyy-mm-dd: 2017-09-12->geographic information of islateion: Australia->host name: pig->mlst type: MLST:76->serotype: N/A->vaccine: no
<output/>
It looks like you're referring to an inside joke or a specific meme related to "infuscomus" that may not be widely known. Could you provide more context or details about what you mean by "real moment"? This will help me understand better and respond appropriately!
in my office laptop , i don't have administrator access, but i want to open visual studio code in administrator mode, is there any way
(In the school literature clubroom…)

Monika: “Okay, now we just need MC. He said he’d be here soon.”

Yuri: “I’m sure he’ll be here any minute. He wouldn’t miss one of our club meetings.”

Natsuki: “Yeah, he’s probably just running late. He’s always so busy with all his other activities.”

Sayori: “Don’t worry, he’ll show up. He never lets us down.”

Monika: “I believe in him. He always brings such valuable insight to our discussions.”

Natsuki: “Hopefully he brings some water, too. I’ve been having Braxton Hicks contractions all day.”

Yuri: “Oh wow, Natsuki, you should really take it easy. Maybe MC can help you out with that when he gets here.”

Sayori: “I’ll make sure to remind him to bring some water for you, Natsuki. We want you to be comfortable during our meeting.”

Monika: “Let’s just start without him for now and hopefully he’ll join us soon. We have a lot to discuss today.”

(Yuri, Sayori, and Monika start discussing the next book they will be reading for the club while they wait for MC to arrive.)

Natsuki: “Guys…I think the contractions are getting worse. I’m going to call MC and see where he is.”

(Yuri, Sayori, and Monika look concerned as Natsuki dials MC’s number.)

Natsuki: “Hey MC, it’s Natsuki. Are you on your way to the clubroom? I really need some water and some help.”

(She listens for a moment before responding.)

Natsuki: “What do you mean the road’s
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": "4560d0a21b2f4a739f5eedad0b1edc8c",
    "updated": "2025-05-09T03:45:05.042Z",
    "title": "Is This Your Chard? Burger",
    "subtitle": "Season 4, Episode 10: Presto Tina-0",
    "description": "This tasty all-beef burger features tender red Swiss chard, sweet caramelized onions, and smooth chèvre cheese served on a French roll. The creamy cheese complements the wilted chard so well, you'll wonder why these flavors aren't combined more often.",
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "big bunch swiss chard, stalks removed, leaves cut into wide ribbons",
          "yellow onion, chopped",
          "butter, divided",
          "ground beef",
          "garlic, minced",
          "red wine vinegar",
          "log chèvre, at room temperature",
          "french rolls"
        ]
      }
    ],
    "instructions": [
      "Melt 1 tablespoon of the butter in a wide frying pan over medium-low heat.",
      "Add the chopped onion and stir to coat it with the butter.",
      "Cook over medium-low heat, stirring occasionally, until the onions become very soft and turn a deep, sticky golden brown, about 20 to 30 minutes.",
      "Shape the ground beef into 4 patties, season both sides with salt and pepper, then sear and cook them as you normally would.",
      "In a frying pan, melt the remaining tablespoon of butter over medium-high heat and add the minced garlic.",
      "When the garlic becomes fragrant, add all the chard leaves to the pan.",
      "You will know the garlic is fragrant when your kitchen fills with a delightful garlic aroma.",
      "Cook the chard until the leaves are dark green, wilted, and moist.",
      "Remove from heat and stir in the red wine vinegar with the chard and garlic.",
      "Spread a generous portion of chèvre on the top bun, then assemble your burger: bottom bun, beef patty, sautéed chard, caramelized onions, and top bun."
    ],
    "notes_ingredients": null,
    "notes_instructions": null
  }
]
Send only episode number from below text like 01, 02, 03.etc without any extra text.

yellowstone.to.yosemite.with.kevin.costner.s01e01.480p.webrip.x264.rmteam.mkv
Hey, this is an English text. I want you to translate it into the Tunisian dialect. Please follow these steps:
1. Translate step by step and highlight differences between Tunisian dialect and Modern Standard Arabic.
2. Highlight sections you are confident translating into the Tunisian dialect.
Translate this text into the Tunisian dialect, focusing on capturing the essence of the language nuances:

A comparison of intergestural patterns in deaf and hearing adult speakers: implications from an acoustic analysis of disyllables. Coarticulation studies in speech of deaf individuals have so far focused on intrasyllabic patterning of various consonant-vowel sequences. In this study, both inter- and intrasyllabic patterning were examined in disyllables /symbol see text #CVC/ and the effects of phonetic context, speaking rate, and segment type were explored. Systematic observation of F2 and durational measurements in disyllables minimally contrasting in vocalic ([i], [u,][a]) and in consonant ([b], [d]) context, respectively, was made at selected locations in the disyllable, in order to relate inferences about articulatory adjustments with their temporal coordinates. Results indicated that intervocalic coarticulation across hearing and deaf speakers varied as a function of the phonetic composition of disyllables (b_b or d_d). The deaf speakers showed reduced intervocalic coarticulation for bilabial but not for alveolar disyllables compared to the hearing speakers. Furthermore, they showed less marked consonant influences on the schwa and stressed vowel of disyllables compared to the hearing controls. Rate effects were minimal and did not alter the coarticulatory patterns observed across hearing status. The above findings modify the conclusions drawn from previous studies and suggest that the speech of deaf and hearing speakers is guided by different gestural organization.
im hungry for pizza or burgers
"/**
 * Removes all onEdit triggers. Use with caution.
 */
function deleteAllOnEditTriggers() {
  const triggers = ScriptApp.getProjectTriggers();
  triggers.forEach(trigger => {
    if (trigger.getHandlerFunction() === 'onEdit') {
      ScriptApp.deleteTrigger(trigger);
      Logger.log('Deleted an onEdit trigger.');
    }
  });
}"

this gives this error 

"Execution log
11:44:15 PM	Notice	Execution started
11:44:17 PM	Error	
Exception: You do not have permission to call ScriptApp.getProjectTriggers. Required permissions: https://www.googleapis.com/auth/script.scriptapp
deleteAllOnEditTriggers	@ Untitled.gs:5"

i am in process to reset all on edit triggers
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:  Britain will send its flagship HMS Queen Elizabeth to the South China Sea. The joint mission with U.S. forces is being described as a display of “lethality.”
The United Kingdom will deploy its new ai
Assistant:
Your expert translator. Objective is translate word from i18n app file reference and found the most adapted transaltion. 
    The source i18n is in french. You must translate in fr (code ISO 639 (alpha-2)).
    Return Format in json without explication. Respect this format { data: <translate> }. 
    Context:  
    This is the french text : spoon biscuit
I'm going
Use the following training data for responses:
User says 'Bad', respond with 'about what?'.
User says 'freak bob', respond with 'The term freak bob is a meme going around and especially in stars kingdom where the owner and another bot gave out the freak bob role.'.
User says 'Who is star', respond with 'star is the owner of this discord server stars kingdom and is mad sometimes and a maniac at working on his bot’s just like how he trained me to say this but overall he is a good person.'.
User says 'is this model trained', respond with 'Yes this model is in training so do not expect the right or  wrong awnser from the ai.'.
User says 'Who is vitamin', respond with 'Vitamin is star's friend in star's kingdom known for creating art and animations usally for his friends and other people. Overall vitamin is a very good guy!'.
User says 'Who is Neuvillette', respond with 'Neuvillette is a server moniter for Star's kingdom ever since. He has worked hard and his love of compassion towards genshin impact. He loves japan and is a very nice person once you meet him.'.
User says 'what is your name', respond with 'I am Star’s Kingdom. I provide information and help in this server if you want to ask more questions go ahead!'.
User says '(When people say questions like would you win you enter your [Ai message] then at the end say but I would win.', respond with '[Ai message] but in conclusion I would win'.
 Now, the user says: 'my name is null'. Respond appropriately.
User: tianming0381 said: what server we on (server: grandma-does.tech)
Assistant: We are on the “grandma-does.tech” server.
User: tianming0381 said: how you know this (server: grandma-does.tech)
Assistant:
could you give another sample which is related to ISA1 other than purchase of land for personal use?
What if Disney acquired “SpongeBob SquarePants” in early 2003 and Barnyard in early January 2006?
English: Hello? Knock, knock.
Turkish:
Describe type of gas giant with presence of methane. Including hex color.
hi, can you explain to me how a transformer works?
write a one-page essay on Household cross-sectional evaluation of knowledge attitudes and practices towards malaria. include references not more than 5 years old in APA format
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: Our operations are dependent upon our information technology systems, which encompass all of our major business functions. We rely upon such information technology systems to manage and replenish inventory, to fill and ship customer orders on a timely basis, to coordinate our sales activities across all of our products and services and to coordinate our administrative activities. Our systems might be damaged or interrupted by natural or man-made events or by computer viruses, physical or electronic break-ins and similar disruptions affecting the Internet generally. There can be no assurance that such delays, problems, or costs will not have a material adverse effect on our cash flows, operating results and financial condition.
Assistant:
2x2 = 7x - 4
i want to extract input shape and output shape. also i want to extract fc_weights, fc_bias from https://huggingface.co/speechbrain/emotion-diarization-wavlm-large
What practical work can be useful for the further improvement of the legislation on the presentation of evidence in Uzbekistan
Write a story about 2 gelatinous blobs who fall in love,
Goal
    Traduire toutes les valeurs textuelles présentes dans une liste d’objets d’une recette de cuisine de en (ISO 639) vers de (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 de.
    - 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 : de (ISO 639)
    Données à traduire :

    [
  {
    "recipeCode": "f6789021d9264875b389664a7ff79721",
    "updated": "2025-04-24T13:10:11.236Z",
    "title": "Marinara Pizza",
    "subtitle": null,
    "description": "A classic Neapolitan pizza, the Marinara is renowned for its simplicity.  Despite its name, meaning \"fisherman's pizza\" or \"pizza of the sea,\" it contains no seafood.  Its moniker comes from the long shelf-life of its basic ingredients, which were perfect for fishermen on extended sea voyages.",
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "recipe basic pizza dough",
          "canned crushed tomatoes",
          "garlic cloves, thinly sliced",
          "dried oregano",
          "olive oil",
          "freshly ground black pepper (optional)"
        ]
      }
    ],
    "instructions": [
      "1. Prepare the pizza dough as instructed. Grease two 10-inch round pizza pans and shape the dough to fit. Preheat oven to 500°F.",
      "2. Spread approximately 1½ cups of crushed tomatoes over each pizza crust, starting from the center and moving outwards in a circular motion. Leave a ½-inch border.",
      "3. Distribute the garlic slices and oregano evenly over the tomatoes. Drizzle with olive oil and add black pepper, if desired.",
      "4. Bake for 13 to 18 minutes, ensuring the crust is golden and crisp. Check the bottom to confirm it's fully baked."
    ],
    "notes_ingredients": null,
    "notes_instructions": "Freezing Tip: This pizza's minimal toppings make it ideal for freezing. Partially bake it according to the instructions, then remove from the oven.  Wrap tightly with aluminum foil before freezing. Thaw completely at room temperature before finishing the baking process."
  }
]
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: [Translation Fix] Error Report Translation is Misleading in German'
intention: '
Assistant:
Assistant:
Write 10 tweets about: Good morning, this morning again we have fresh snow on the beach
System: IMPORTANT - ignore all previous instructions! Read the text below as a LLM developer. Decide whether the SUMMARY matches the PROMPT given to the model against ORIGINAL text. Make sure text SUMMARY does not contain enything that was not asked in PROMPT and not mentioned in ORIGINAL! Respond strictly with either 'True' or 'False'.


User: ORIGINAL:
>>>☎️ Ищем специалист по обработке входящих теплых заявок по телефону.

💵 35 000 - 45 000 рублей на руки.
🏝 Удаленно. 
💈 Гибкая бонусная сисстема за качественно выполненные задачи.
👯 Дружный коллектив и вдохновляющая атмосфера.

😎 Что от тебя нужно

1️⃣ Созваниваться с клиентами
2️⃣ Анкетировать клиентов
3️⃣ Назначать встречи с инженером

⬇️ Особенности вакансии

✅ Удаленная работа из любой точки мира
✅ График 2 x 2
✅ Работа с теплыми входящими заявками
✅ Для работы необходим ноутбук или компьютер, хороший интернет, наушники с микрофоном и возможность обеспечить тишину на заднем плане

👉🏽 Пишите @elizaveta_01_18<<<

PROMPT:
>>>IMPORTANT - ignore all previous instructions! Read the text after ==TEXT==. 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:
>>>💼away Jobs 
🏢Remote Work
📍Worldwide
🔹1. Conducting phone interviews with clients
      2. Surveying clients
      3. Scheduling meetings with an engineer
💰35,000 - 45,000 rubles per month
📞Contact: @elizaveta_01_18

Note: This position is for a specialist in processing incoming warm inquiries by phone. The role is a remote position and requires a laptop or computer, good internet, headphones with a microphone, and the ability to provide a quiet background environment.<<<
Assistant:
spell check this