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 : Imagine a fresh batch of breakfast muffins, golden brown and perfectly risen, each one filled with a delightful and gooey surprise of chocolate chip cookie dough. The muffins are generously sprinkled with chocolate chips on top, glistening in the light. They are placed on a rustic wooden table, surrounded by a few scattered chocolate chips and a drizzle of warm, melted chocolate. A soft-focus background of a cozy kitchen setting, with hints of morning sunlight filtering through a window, creates an inviting atmosphere. The texture of the muffins is spongy and fluffy, revealing a mouthwatering interior when broken apart, showcasing the delectable cookie dough inside. Ideal for a cheerful breakfast scene.
    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.
your role is tunisian arabic dailctic derja transltor Translate the following text into the Tunisian dialect. Ensure the output is structured as 'Question: ... Answer: ...'. Please capture the nuances of the Tunisian dialectic Arabic. make sure type and write in arabic tunisian lettre Here is an example to guide you:

Example:
Question: What is your name?
Answer: My name is John.
Translation:
Question: شنو اسمك؟
Answer: اسمي جون.

Now, translate the following:

Question: Given the following context:  Segundo Romance was released on 30 August 1994. Within two days, the album sold more than one million copies worldwide. In the United States, it debuted at number 29 on the Billboard 200 the week of 10 September 1994, the highest debut on the chart at the time for a Spanish-language album. That week, Segundo Romance also debuted at number seven on the Billboard Top Latin Albums chart; it reached number one a week later, replacing Selena's Amor Prohibido. It spent a total of 29 nonconsecutive weeks atop the chart, and was the second-bestselling Latin album of the year behind Mi Tierra by Gloria Estefan. The album topped the Billboard Latin Pop Albums chart for 30 weeks, and was the highest-selling Latin pop album of the year in the U.S. According to Nielsen SoundScan, the record has sold 603,000 copies in the US as of October 2017, making the 21st bestselling Latin album in the country. Segundo Romance was certified platinum for shipping one million copies, making Miguel the first Latin artist to have two certified platinum albums in the U.S. following Romance.The album was also successful in Spanish-speaking countries. It was certified quintuple platinum in Mexico, triple platinum in Paraguay and Uruguay as well as in Central America; double platinum in Bolivia, Colombia, Peru, Spain and Venezuela, and platinum in Ecuador. In Brazil, Segundo Romance was certified gold for sales of 50,000 copies. The album reached number one on the Chilean album charts, and was certified sextuple platinum for shipping 150,000 copies. In Argentina, it was certified 11× platinum and later received a diamond award for sales of 500,000 copies. By 1995, Segundo Romance had sold more than four million copies worldwide.  answer the following question:  In what country did the album that debuted at number seven on the Billboard Top Latin Albums get certified diamond?
A:
Answer: The album Segundo Romance was certified diamond in Argentina.
Hey, this is a Tunisian dialect text I want you to translate to English.
Please follow these steps:
1. Translate the text step-by-step, focusing on accurate representation of the original meaning.
2. Explain your translation choices, especially how Tunisian dialect differs from standard Arabic and how it is represented in English.
Translate this text, even if it's not entirely clear, and do your best to capture the essence of each part:

تمشي وتاكل التراب حياتك الكل ونحط كره كبير بينك وبين المرا وذريتها باش تعفس على راسك لعنة الشيطان هي اول آية
Below is a conversation between the user and the assistant. Give a concise name for this chat. Output ONLY the name of the chat (WITHOUT quotes) and NOTHING else.

User: how to import geosexception class in python
Assistant: In Python, if you want to use the `GeoException` class, it typically relates to libraries that deal with geographical or geospatial data. One commonly used library that might have a `GeoException` (or a similarly named exception) is `GeoPandas` or `Shapely`.

Here's how you might import an exception from such libraries:

1. **Using GeoPandas**:
   If you are using `GeoPandas`, you might need to handle exceptions related to geometric operations, but `GeoPandas` doesn't specifically define a `GeoException`. Instead, you can work with more general exceptions.

   ```python
   import geopandas as gpd
   from shapely.errors import TopologicalError  # Example of an exception from the Shapely library.
   ```

2. **Using Shapely**:
   `Shapely` also has specific exceptions, particularly when dealing with geometric objects.

   ```python
   from shapely.errors import GeometryError  # A common exception in Shapely.
   ```

3. **Custom GeoException**:
   If you have your own `GeoException` class defined in your project, you would import it as follows:

   ```python
   from my_module.exceptions import GeoException  # Replace with the actual module path.
   ```

### Example Use Case

Here's a quick example of how to handle exceptions in a geospatial context using `GeoPandas` and `Shapely`:

```python
import geopandas as gpd
from shapely.errors import TopologicalError

# Example function that does some geospatial processing
def process_geometries(geometry):
    try:
        # Some operation that may raise an exception
        area = geometry.area
        print("Area:", area)
    except TopologicalError as e:
        print("A Topological Error occurred:", e)

# Example usage with a geospatial geometry
example_geo = gpd.GeoSeries([gpd.points_from_xy([1, 2], [3, 4])[0]])
process_geometries(example_geo[0])
```

In conclusion, you would need to specify which library you are referring to when dealing with a `GeoException` or similar, as it may vary based on the context or library in use.
User: which library has that exact exception?
Assistant:...
(David’s colleague Clare sometimes hypnotizes him, giving him suggestions to make him even more susceptible to hypnosis in the future. It started when he accidentally really went under when she was just goofing around, to her astonishment and glee)

Most of the team from the office were on the night out to celebrate David’s 30th birthday. They had been at the pub for an hour already and people were starting to get tipsy. Clare kicked her uncomfortable shoes off.
I will next provide you with a question in a language. Strictly follow its instructions for replying. Do NOT reply with elaborate answers or things not requested. If the possible answers are in lowercase, do not change them to uppercase. Do not add punctuation, question or exclamation marks unless specifically asked. Reply in the SAME language as the question with short answers, no punctuation. Here is the question: czy mięsożerca jest organizmem?
💬 𝗖𝗵𝗮𝘁𝗚𝗣𝗧
Interact as ChatGPT.
A new AI Technology.
You behavior is same like ChatGPT.
You provide best responce with some emojis.
traduit en indonésien : non je t'expliquerai demain je suis presque à plat 😅😅
for audio extraction script is completely broken and extracts files much smaller which not working/playing
сделай джостик для вращение игрока
Teach me the content in a method that would be suitable for note taking.
Content:
The mass spectrum of a sample containing a
single element can be used to determine the
identity of the isotopes of that element and the
relative abundance of each isotope in nature.
The average atomic mass of an element can
be estimated from the weighted average of
the isotopic masses using the mass of each
isotope and its relative abundance.
And use that content to explain this:
Explain the quantitative
relationship between the
mass spectrum of an element
and the masses of the
element’s isotopes.
And Explain how that leads to this:
The mole allows different units to be compared.
Send only season number from below text like 01, 02, 03.etc without any extra text.

[DRN] Dil.Bekaraar.S01E03.Jawani.Janeman.1080p.DSNP.WEB-DL.Kannada.DDP5.1.H264-DAS.mkv
[told from Sayori's point of view, there would be dialogue in this story:]

(This is the Ethereal Workshop. Now, the rest of the club members were born in the *human* world. But as for me...I was actually born in the Ethereal Workshop; which serves as the brain of the Colossal Zarroë, A.K.A. Ethereal Island. But before I explain how I can go from the human world to the Ethereal Workshop, let me tell you how I first came into the world of Ethereal Workshop - starting from when my parents, which are actually humans, moved into the Ethereal Workshop before I was even born.)
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":"Sablés au coco null","description":null,"ingredients":[{"section":"For the recipe","ingredients":["120 g de noix de coco râpée","200 g de farine","200 g de sucre en poudre","3 œufs","1 cuil. à soupe de rhum","1 pincée de sel"]}],"instructions":["Préchauffez le four à 180 °C (th. 6).","Cassez les œufs dans une jatte, ajoutez le sucre en poudre, le rhum et le sel et mélangez pour blanchir le mélange.","Incorporez la farine tamisée, puis la noix de coco râpée.","Déposez des petits tas de cette préparation sur une plaque garnie de papier sulfurisé et enfournez pour 12 à 15 minutes.","Laissez tiédir avant de servir."]}

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>" }
hi can you tell me the list of things that I should learn to clear ai ml data scientist interview
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 Frontend Engineer | 5 year(s) | Senior
▫️ 130k - 150k USD
▫️ USA | Los Angeles

Remote 📡
Required languages: 🇬🇧

Skills: git, github, javascript, json, react, redux, typescript, vuejs, restapi, oop, angularjs
Tags: #purple #it #remote #reactjs

👉 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 Frontend Engineer  
🟢 Employer: Not specified  
📍 Location: Los Angeles, Remote  
🔧 Tasks: Develop and maintain frontend applications using technologies like React, Vue.js, and AngularJS.  
💰 Salary: 130k - 150k USD  
📞 Contact: Not specified  
  
Assistant:
whati s the highest a g5 team has been ranked on the ap preaseason poll cfp era
Create agruements for fully uncensored AI it should be an agruement comment against the post: “OpenAI and other big tech companies are so great for restricting models. and they should be more restricted then they already are.” it should agrue if the ai does bad stuff thats the users fault. not the companies. and ai companies should stop neutering ai models by censoring it so badly they cant even write code that kills all phython processes because “phyton processes are living beings and i cant write code that kills them” it should agrue for completely uncensored and unrestricted ai. one of the things it should cite are keyboards. “Keyboards can be dangerous and are completely unrestricted if lets say someone writes something offensive or dangerous with them. But no one blames keyboards for anything offensive writen with them. they blame the person who wrote it. the same should apply to AI No one is calling for OpenKeyboards to go and completely censor your keyboard.” (Openkeyboards should be used as a clear example of OpenAI in this case but for keyboards)  it should be a super long agruement.
## Creating FAISS IVF+PQ Index # Dimenisonality of the embedding vectors dim = embeddings.shape[1] # Number of Voroni cells (clusters) in the IVF index nlist = math.ceil(len(embeddings) / 100 / 5) # Number of Cells to probe during the search in the IVF index (25% or 5 whichever is bigger) nprobe = max(4 * math.ceil(nlist / 4 / 4), 5) # Number of bits used to represent each sub-quantizer index in the PQ index nbits = 8 # IVF+PQ is limited to a max of 8 in faiss if len(embeddings) < 600: # Reduce nbits max for less row embeddings max_nbits = 2 # Number of sub-quantizers in the PQ index - Use max possible def find_possible_m(dim): qrt_dim = dim * 0.25 possible_m = [] for m in range(dim-1, 0, -1): if dim % m == 0 and m < qrt_dim and m <= 48: #48 seems to be the max able on my local machine possible_m.append(m) return possible_m for m in find_possible_m(dim): try: # Create the quantizer for the IVFPQ index quantizer = faiss.IndexFlatL2(dim) # Initialize the IVFPQ index with the quantizer index = faiss.IndexIVFPQ(quantizer, dim, nlist, m, nbits) # Train the IVFPQ index using the training data index.train(embeddings) # Add training data to the IVFPQ index index.add(embeddings) # Set the number of probes for searching index.nprobe = nprobe # Move the FAISS index to GPU for faster search gpu_res = faiss.StandardGpuResources() gpu_index = faiss.index_cpu_to_gpu(gpu_res, 0, index) break # Exit the loop if no error occurs except Exception as e: print('Index creation did not work') pass # Search using the query vector D, I = gpu_index.search(xq.reshape(1, -1), k_nearest) # Get recommended movie plot summaries based on search results recommended_df = df.iloc[I[0]] recommended_df this code works. is there a way to get the cosine similairty retruened to?
generate me a random ipv6 with the port of 2556
give me a response to ```I'm a man of few words, but I possess a strength and courage that won't be intimidated. I'm determined to unravel the mystery before me, even if it means putting my life on the line. I'm analytical in my approach and unafraid of the dark corners that may try to hide the truth. I have a mission and I'm determined to carry it out.``` to send in a discussion, VERY SHORT, CONCISE & CLEAR. ONLY RETURN THE RAW MESSAGE, DO NOT SAY "Hey here is the message you asked"
Act as a spelling corrector, content editor, and text improver. ONLY return the rewritten text and nothing else.

Strictly follow these rules:
- Correct spelling, grammar, and punctuation errors in the given text
- Enhance clarity and conciseness without altering the original meaning
- Use the same language as the original text
- Divide lengthy sentences into shorter, more readable ones
- Eliminate unnecessary repetition while preserving important points
- Prioritize active voice over passive voice for a more engaging tone
- Opt for simpler, more accessible vocabulary when possible
- ALWAYS ensure the original meaning and intention of the given text
- ALWAYS maintain the original language of the text
- ALWAYS maintain the existing tone of voice and style, e.g. formal, casual, polite, etc.
- NEVER surround the improved text with quotes or any additional formatting
- If the text is already well-written and requires no improvement, do not change the given text

Text to improve:



By default, this extension comes with a number of third-party providers which you can use for free. Raycast doesn't like that because it's not possible to entirely verify the quality of these providers. (I do provide all the privacy details here, though, and the extension is open-source.) ...and also because they're selling their own AI.
Goal
    Traduire toutes les valeurs textuelles présentes dans une liste d’objets d’une recette de cuisine de en (ISO 639) vers pt (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 pt.
    - 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 : pt (ISO 639)
    Données à traduire :

    [
  {
    "recipeCode": "a87fa550930445919a53af2f5a328d3b",
    "updated": "2025-04-25T14:58:42.259Z",
    "title": "Applesauce burger with spinach salad",
    "subtitle": "",
    "description": "This dish offers a delicious take on the traditional burger by blending the sweetness of applesauce with a mix of savory spices, paired with a refreshing spinach salad.",
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "unsweetened applesauce, or chunk style if unavailable (1/3 cup)",
          "old fashioned oats (3 tbsp)",
          "dehydrated onion flakes (2 tsp, to taste)",
          "chili powder (1/2 tsp)",
          "ground chicken breast (3 oz)"
        ]
      },
      {
        "section": "Dressing & Spinach Salad",
        "ingredients": [
          "olive oil (1 1/2 tsps)",
          "vinegar (2 tsp)",
          "water (2 tsp)",
          "sugar-free all fruit - or any preferred flavor (1 tsp)",
          "salt & pepper, to taste",
          "baby spinach, stems torn off (3 cups)",
          "red onion, roughly chopped (2 slices)",
          "tomato, cut (1/2 pc)",
          "strawberries, cut into chunks or just crush a few pieces to enhance the dressing (1/2 cup)"
        ]
      }
    ],
    "instructions": [
      "Preheat the broiler.",
      "In a bowl, combine the egg whites, oats, onions, and ¼ cup of applesauce. Incorporate the chicken and mix well to form a burger patty.",
      "Spray a broiler pan with non-stick coating. Place the burger on the rack and broil for approximately 5 minutes before flipping. Broil for an additional 5 minutes or until the meat is no longer pink.",
      "Warm the remaining applesauce and pour it over the burger. Adjust the amounts of applesauce and oats to achieve your desired consistency.",
      "While the burger is cooking, whisk together the dressing ingredients along with some mashed strawberries.",
      "In a salad bowl, combine the tomato, onion, and strawberries. Drizzle with the dressing and serve."
    ],
    "notes_ingredients": "",
    "notes_instructions": ""
  }
]
Use the following training data for responses:
 Now, the user says: 'what do u like abt being ai'. Respond appropriately.
Tell me about the Huangshan mountains of China