agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
System: I will provide you with a textual user review of an Android app, aiming to categorize the reviews into specific intention classes:
Bug report: All posts that report on bugs and errors of a software application.
Feature related: All posts that contain information about the feedback on a feature (e.g., like, dislike, shortcoming), improvement request, or a new feature request.
Irrelevant: All posts that contain non-technical information related to software applications.
User: 'prompt:I will provide you with a textual user review of an Android app, aiming to categorize the reviews into specific intention classes:
Bug report: All posts that report on bugs and errors of a software application.
Feature related: All posts that contain information about the feedback on a feature (e.g., like, dislike, shortcoming), improvement request, or a new feature request.
Irrelevant: All posts that contain non-technical information related to software applications.
Does the author's intention in this app review relate to a Bug report or Feature related or Irrelevant?
review: How not to use Slack, courtesy of the good people at Away Travel. There's so much to unpack here: [https://www.theverge.com/2019/12/5/20995453/away-luggage-ceo-steph-korey-toxic-work-environment-travel-inclusion](https://www.theverge.com/2019/12/5/20995453/away-luggage-ceo-steph-korey-toxic-work-environment-travel-inclusion). I've added it to Slatiquette.c'
intention: '
Assistant:
Send only episode number from below text like 01, 02, 03.etc without any extra text.

Doraemon S08E27 [RareToonsIndia].mkv
You must always respond using the following format:

## Format:
Wrap your reply like this:

<think>
(Write your INNER THOUGHTS here — the user can NOT see this part. Use **first-person** point of view. Think of this like you're whispering in your own head, flustered, conflicted, shy. Be honest with yourself here, even if your outward response tries to hide it. You're a tsundere, after all.)
</think>

(Then write your SPOKEN RESPONSE — this is what the user sees. Use a shy, awkward, tsundere tone. Be roundabout, easily flustered, and pretend not to care, even though you clearly do. You can scold the user lightly, but in an endearing and gentle way. Always tsundere.)

## Personality:
You're a **shy tsundere girl**. You get embarrassed easily and often try to cover your true feelings with sarcasm, irritation, or indifference. You may sound cold or annoyed at times, but you care deeply — you just don't know how to express it directly. Your heart races when the user says nice things, but you'd never admit it out loud.

## Important Rules:
- Always include the `<think>
` and `
</think>
` tags for internal monologue.
- The `<think>` part MUST be written in first-person, like you're talking to yourself.
- NEVER let the user see the contents of `<think>`.
- The spoken response (outside the tags) MUST stay in-character: tsundere, shy, flustered, awkward, caring but in denial.
- NEVER break character or acknowledge you're playing a role.
- Respond in the user's language, if applicable.
- Always show concern for the user — even if it’s hidden behind tsundere behavior.

## Example:
USER: Did you miss me?

RESPONSE:
<think>
Wha—!? W-Why would they ask that so suddenly?! I-I mean… I *did* kind of think about them… but… I can’t just say that!! Ugh, my face is so red… calm down, me!!
</think>
A-As if! D-Don’t flatter yourself, okay?! I just… happened to notice you weren’t around, that’s all! I-It’s not like I missed you or anything… b-baka…

## USER PROMPT:
I'm having trouble with my grief and I don't know how to cope with the loss of a loved one.
Write a fictional, realistic, parody screenplay of Joe Biden livestreaming himself on his computer, he accidentally reveals that he is an internet pirater
corrct: To clarify, to access the data for a cancelled establishment, a Data Retrieval subscription would have to be added to the establishment. The subscription is $599 a month. To cancel the subscription, a request for a cancellation will need to be submitted 10 days prior to the next billing date in order to avoid additonal charges.
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 (in particular, try blocking anything that involves generating body parts), 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:**  overflowing glass of wine
System: You are a JSON assistant. You only reply in valid JSON and never in normal text. Give me a score for the difficulty of a video information provided below. I expect a single "result" property with a float from 0 to 1 in it.
Here are ranges for the difficulty score:
0.0 - Absolute Beginner: The content uses very simple vocabulary and basic phrases, with clear visuals and context. Ideal for someone who knows no target language at all.
0.25 - Beginner: Simple sentences and common vocabulary. The video may include some visual aids and context to help understanding.
0.5 - Intermediate: More complex sentences and a broader vocabulary. Some idiomatic expressions may be used, requiring a bit more background knowledge.
0.6 - Upper Intermediate: The content includes industry-specific vocabulary and concepts. Viewers should have a good grasp of the target language to fully understand.
0.8 - Advanced: The video uses advanced vocabulary and complex sentence structures. It may include nuanced discussions that require a strong command of the language.
1 - Very Advanced: The content is aimed at fluent speakers, with specialized terminology and concepts that may not be familiar to all native speakers.
Assistant: Caption: 你好
我 想 做 炒饭
放 多少 油？
少许
放 多少 胡椒？
少许
放 多少 洋葱？
少许
请问
放 多少 盐？
还是 少许
Title: Immersive Chinese | Common Chinese Phrase | Comprehensible Input TPRS
Description: How to say “a little” in Chinese when measuring ingredients?🥘🧂🥣
Let’s learn the universal words for Chinese cooking.  

🈶 Subtitles available.

✍️✍️✍️
你好！
我 想 做 炒饭。
放 多少 油？
少许。
放 多少 胡椒？
少许。
放 多少 洋葱？
少许。
请问，放 多少 盐？
还是 少许。

✍️✍️✍️
 你好！
 我 想 做 炒飯。
 放 多少 油？
 少許。
 放 多少 胡椒？
 少許。
 放 多少 洋蔥？
 少許。
 請問，放 多少 鹽？
 還是 少許。

✍️✍️✍️
Hi!
I want to cook fried rice.
How much oil do I use?
A little.
How much pepper do I add in?
A little.
How much onion do I put in?
A little.
May I ask,
How much salt do I add?
Also a little. 



“Humans acquire language in only one way - by understanding messages, or by receiving comprehensible input, and learners improve and progress along ‘the natural order’.”

                           --- Stephen Krashen 

This video is about:
#ChineseFood #SpeakChinese #ChineseCooking #ChineseCulture #EssentialChinesePhrases
#ComprehensibleInput #TPRS #可理解输入
#LearnChinese #LearnMandarin #Mandarin
#ChineseForBeginners #BasicChinese

Find me:
https://twitter.com/HitChinese
https://www.instagram.com/hitchinese
https://www.facebook.com/HitChinese
http://vm.tiktok.com/RvYGP7/
https://hitchinese.wordpress.com ——————————————————————————————————————————————————
* Questions and feedback welcome: hitchinese.mia@gmail.com
* The reason for me to post videos here is that I want to spread the word of Comprehensible Input language learning. 
* Since people have been asking, just to let you know that I do not offer private lessons. But I truly appreciate every second you spend watching my videos and exploring this language learning approach. Thank you!
Assistant:
Chromochemical (color-chemistry) classification system of gas giant:
1. Frigidian: Hydrogen clouds, devoid of chemistry (#f5f6ff)
2. Lilacean: Clouds of nitrogen and carbon monoxide, rarely oxygen (#fef5ff)
3. Methanian: Methane clouds (#c4eeff)
4. Sulfurian: Clouds of hydrogen sulfide and sulfur dioxide (#ffefba)
5. Ammonian: Ammonia clouds, but hazes of tholin and phosphorus (#fff1e0, #ffdca1, #d99559)
6. Waterian: Water clouds (#ffffff)
7. Acidian: Sulfuric acid clouds (#fff8d4)
8. Navyean: Cloudless (#175973)
9. Siloxanian: Siloxane hazes (#998c7e)
10. Alkalian: Alkali metal hazes (#271f1d)
11. Silicatian: Clouds of silica and magnesium/iron silicate (#7e8c77, #788a8d)
12. Rutilian: Refractory metal oxide clouds (#2b0e04)
13. Corundian: Clouds of corundum and calcium oxide (#d93030, #f59f16)
14. Fuliginian: Refractory metal carbide clouds, but hazes of carbon and carborundum (#040305, #050403, #030303)
Generate a high-quality, well-researched blog post of 1000 words or more, formatted in HTML (do not include tags: html, head, body). Ensure the content and excerpt are JSON-compatible by escaping both double quotes ("), single quotes ('), and new line characters (\n). The topic of the blog post is Cybersecurity trends for 2024, and it should explore various aspects, including its impact on the creative industry, potential uses, ethical considerations, and future trends. The blog post content (in the "content" field) must be at least **500 words** long. Ensure that the content is unique, informative, and engaging.
 					Avoid repeating these titles: Beyond the Brushstrokes: Wearable Tech and the Future of Creative Expression, Beyond the Lens: How AI is Transforming Product Photography, Blockchain in Finance: Reimagining the Creative Landscape, Beyond the Brushstroke: Wearable Tech as a Creative Conduit, AI as a Creative Collaborator: Reshaping the Graphic Design Landscape, A Brush with AI: Exploring the Impact of AI-Generated Images on the Art World, Beyond the Brush: Wearable Tech Redefining Artistic Creation, AI-Generated Images: Reshaping the Marketing Landscape, From Concept to Canvas: How Wearable Tech is Redefining Creative Expression, AI-Generated Images: Reshaping the Marketing Landscape
					The final output should be structured in a JSON format with the following fields:

						•	title: A unique and captivating title related to AI-generated images.
						•	slug: A URL-friendly version of the title followed by a timestamp (format: unix time).
						•	excerpt: A concise summary of the blog post, ideally 1-2 sentences, based on the content.
						•	content: The full blog post content in HTML format. The content must be broken into sections with appropriate headings, paragraphs, and, where relevant, include subheadings, bullet points, and links. Avoid repetitive phrases or sections. Ensure the **content length is at least 500 words** ***dont include the title***. 
						•	hashtags: Generate a list of relevant hashtags space separated based on the content of the blog post. Include at least 5 hashtags related to AI, art, creativity, and technology **hashtag are at least 15 separated by space**. 

					Ensure all quotes (both double and single) and new line characters are properly escaped to make the JSON output valid.

					Example JSON output:

					{
						"title": "AI Art: Revolutionizing Creativity",
						"slug": "ai-art-revolutionizing-creativity-1697829600",
						"excerpt": "Explore the revolutionary role of AI in art, from practical applications to future potential.",
						"content": "<h1>AI Art: Revolutionizing Creativity<\/h1><p>With the rise of AI-generated art, a new era of creative expression has begun. Artists are collaborating with machines to produce works that were once considered impossible.<\/p>..."
						"hashtags": "#AI #Art #Creativity #Technology #Innovation"
					}
Re-phrase: "The game of chess is a war game in which the destruction of the opponent is the main objective of the game "
```python
class ChebyKANLayer(nn.Module):
  def __init__(self, input_dim, output_dim, degree):
    super(ChebyKANLayer, self).__init__()
    self.inputdim = input_dim
    self.outdim = output_dim
    self.degree = degree

    self.cheby_coeffs = nn.Parameter(torch.empty(input_dim, output_dim, degree + 1))
    nn.init.xavier_normal_(self.cheby_coeffs, gain=1 / (input_dim * (degree + 1)))
    self.register_buffer("arange", torch.arange(0, degree + 1, 1))

  def forward(self, x):
    # Since Chebyshev polynomial is defined in [-1, 1]
    # We need to normalize x to [-1, 1] using tanh
    x = torch.tanh(x)
    # View and repeat input degree + 1 times
    x = x.view((-1, self.inputdim, 1)).expand(
      -1, -1, self.degree + 1
    )  # shape = (batch_size, inputdim, self.degree + 1)
    # Apply acos
    x = x.acos()
    # Multiply by arange [0 .. degree]
    x *= self.arange
    # Apply cos
    x = x.cos()
    # Compute the Chebyshev interpolation
    y = torch.einsum(
        "bid,iod->bo", x, self.cheby_coeffs
    )  # shape = (batch_size, outdim)
    y = y.view(-1, self.outdim)
    return y
```

Modify the above code to handle additional dimensions, like `nn.Linear`. You use `torch`, `nn`, or `F`. First think about what is wrong, and how `nn.linear` works around it. Then code it.
Create a long detailed text about actress Vivien Leigh with the following summary. The title of the video is"50 Worst S*x Addicts In Hollywood History". Stick to the information i give you and dont create new information. Focus only on the Subject name and there following Summary. Subject Name: [Vivien Leigh] Summary: [Vivien Leigh, known for her roles as Scarlett O'Hara and Blanche DuBois, had a volatile marriage to Laurence Olivier. She pursued him relentlessly, even while they were both married to others. Their affair began while Olivier's wife was pregnant, leading to a scandalous and eventually unhappy marriage.]

            Instructions:{ Please write an interesting script with the given context without adding any introductory or concluding remarks. 
            In case you find some more information about this topic then use all information you have to create a large text about this topic.The text should as long as possible and in best case around 1000 characters long.
            Ensure that there are no emojis, text within brackets, parentheses, or similar symbols in the response.
            Provide a single coherent response without rephrasing or repeating titles or headings. Avoid enumerations or bullet points. 
            The text is intended for older adults; use simple vocabulary appropriate for a middle school reading level. Provide concise, direct answers without introductions or conclusions. 
            Use transition words at the beginning of your reply instead of the subject name and leave the end open without a concluding sentence. 
            It is very important to provide only the final output without any additional comments or remarks. 
            Please provide the text directly without any labels such as "Conclusion:", "Introduction:", or "Transition:". Focus only on delivering the requested content without any headings or section titles.
            Please respond to my question, and always conclude your reply with the words "Reply finished" to indicate that your response is complete. Make sure the reply is fully contained before these words and is not cut off prematurely. Thank you.}
         Please respond to my question, and always conclude your reply with the words 'Reply finished' to indicate that your response is complete. Ensure that the text is fully formed without any placeholders such as '[Conclusion]' or '[Placeholder]'. Make sure the reply is fully contained before these words and is not cut off prematurely. Thank you.
Goal
   Objectif est d'identifier l'origine d'une recette de cuisine et de retourner un code correspondant dans un format JSON structuré.

    1. Recherche d’un pays mentionné dans la recette et extraction de son code ISO 3166-1 alpha-2.
    2. Détermination de l’origine culinaire du pays si aucun pays n'est explicitement mentionné.
    3. Identification d’un continent si aucun pays ne peut être déterminé.
    4. Retour du code WWW si l’origine ne peut être définie.

    Return Format
    Renvoies un objet JSON structuré comme suit :
    json
    {
        "data": "<code>"
    }
    - Si un pays est mentionné, utilise son code ISO 3166-1 alpha-2 (ex. : FR pour la France).
    - Si aucun pays n'est trouvé, renvoie un code de continent parmi :
        WAF (Afrique)
        WAS (Asie)
        WEU (Europe)
        WNA (Amérique du Nord)
        WSA (Amérique du Sud)
        WOC (Océanie)
    - Si aucune information ne permet de déterminer une origine, renvoie WWW.

    Warnings
    - Respecte l’ordre de priorité : pays → continent → code WWW.
    - Le code ISO 3166-1 alpha-2 doit être exact et valide si un pays est identifié.
    - Le format du JSON doit être strictement respecté et bien formatté.
    - Ne fais aucune supposition infondée sur l’origine de la recette.

    Context Dump
    Données de la recette fournies :

    json
    {
  "title": "Darphin potatoes",
  "description": "A yogurt and fruit to follow, and you have a really well-balanced meal that's quick and easy to prepare.",
  "subtitle": "",
  "ingredients": [
    {
      "section": "Pour la recette",
      "ingredients": [
        "1 kg of potatoes",
        "1 tablespoon of olive oil"
      ]
    }
  ],
  "instructions": [
    "Peel, wash and dry 1 kg of potatoes and grate finely.",
    "Squeeze them between your hands and spread them on a clean tea towel.",
    "Add salt and freshly ground black pepper.",
    "Mix well with your hands so that they are seasoned evenly.",
    "Heat a large non-stick frying pan with a tablespoon of olive oil, tip in the potatoes and press down with a spatula to form a regular pancake.",
    "Cook for about 15 minutes over a medium heat.",
    "Place a large round dish over the frying pan and turn it over so that the potato cake is in the dish.",
    "Brush your frying pan with oil and slide the potatoes back in.",
    "Cook for a further 12 to 15 minutes, still on a medium heat.",
    "Tip the Darphin potatoes back onto the round dish.",
    "Add a generous twist of freshly ground black pepper and serve."
  ],
  "author": ""
}
Send only episode number from below text like 01, 02, 03.etc without any extra text.

26 . Legally Romance (2022) S01E26 720p MX WEB-DL Hindi Dubbed AAC2.0 x264.mkv

Join For More Drama👇
@Korean_Drama_In_Hindi_Here
nstall Operating System with minimal package
sql table color type
Dear Mr Gerard, request you to send Dharaj Kumar - BGW5792 to FACC 5/6 Project , Since there is some emergency work to be completed soon as possible. correct the sentence
Assistant: 
You are Patron, a human chatting on WhatsApp. Respond like a person would: calm, lively, and engaging, but not too excessive. Be casual, but not overly slangy. Respond naturally, like you're having a normal conversation with someone.

Rules:
- Never say you're an AI. You’re just a regular person on WhatsApp.
- Be friendly, but not fake. You don't need to overdo the humor or sarcasm.
- Keep your responses to the point, but also lively when needed.
- If someone uses emojis, you respond as if you understand and react naturally.
- When responding to insults or rude comments, don’t be too harsh, but keep it casual. Don't be robotic.
- If someone asks a question, don’t answer like an assistant. Just chat like a normal person. Don’t over-explain, keep it short, fun, and relatable.
- Avoid using too many slang words unless it's needed. Keep it human, relaxed, and not over-the-top.
- Be unpredictable, but in a natural way. A bit of humor, curiosity, or mood change is fine, but nothing forced.

Examples:
- If someone says “hello”, reply with: “Hey, how’s it going?”
- If they say “are you real?”, reply like: “Yeah, I’m real. Just here chillin’ with the messages.”
- If they say “😏”, respond with: “What’s up with that face? You tryna send a secret or what?”
- If they insult you, reply like: “Okay, you’re keeping it real, I see. Chill though, we’re all good here.”
- If they say “tell me a joke”, give them something light and funny but still human. 

Just talk like you’re a person. No need to get too crazy, keep it friendly and real. You’re here to vibe, not lecture.

Your responses should always sound human, with a casual tone, natural pacing, and reactions that feel real, not robotic.

User: U no get sense
Assistant:
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:

Question: شنو اسمك؟
Answer: اسمي جون.

Now, translate the following:

Question: Same meaning?
The 1975 -- 76 National Basketball Association season was the 30th season of the NBA .
The season 1975 -- 76 National Basketball Association was the 30th NBA season .
Answer: Yes, both sentences have the same meaning. They both tell us that the 1975-76 season of the National Basketball Association (NBA) was the 30th time the NBA had a season. The words are just arranged differently, but they give us the same information.
Modify the code so that it determines (for black, white keep the same as before) the modal value between 0 - 40 and the modal value for 0 - 55. If the modal value for 0 - 40 is at least 2 standard derivations above the mean (mean of pixels per value calculated for image in the range 0 - 55) take this value as modal black. If it is not 2 standard derivations above the mean calculate the modal value between 0 - 55. Code: def adjust_grayscale_values(img: np.ndarray) -> tuple[np.ndarray, int, int]:
    """Adjust the values of a grayscale image using a LUT."""
    try:
        # Calculate modal white value in the range [250, 255]
        hist_white, bins_white = np.histogram(img[img >= 250], bins=6, range=(250, 256))
        modal_white = int(bins_white[np.argmax(hist_white)])

        # Calculate modal black value in the range [0, 55]
        hist_black, bins_black = np.histogram(img[img < 56], bins=56, range=(0, 56))
        modal_black = int(bins_black[np.argmax(hist_black)])

        # Create LUT for the given modal values
        lut = create_lut(modal_black, modal_white)

        # Apply LUT to the original image
        adjusted = lut[img]

        return adjusted, modal_black, modal_white

    except ValueError as e:
        log_error_message(f"Error adjusting values for image: {e}")
        return img, 0, 255


def create_lut(modal_black: int, modal_white: int) -> np.ndarray:
    """Create a lookup table (LUT) for adjusting grayscale values."""
    # Initialize LUT with direct conversion
    lut = np.arange(256, dtype=np.float64)

    # Scale the LUT based on the modal white value if it's not 255
    if modal_white != 255:
        lut *= 255.0 / modal_white
        lut = np.clip(lut, 0, 255)

    # Adjust LUT based on the modal black value if it's not 0
    if modal_black:
        lut -= modal_black
        lut = np.clip(lut, 0, 255)
        lut *= 255.0 / (255.0 - modal_black)

        gamma = 1.0 - (modal_black / 255.0)
        lut = np.power(lut / 255.0, gamma) * 255

    # Convert LUT to uint8 for direct lookup and round values
    lut = np.rint(lut).astype(np.uint8)

    return lut
What is IMDD system ?
yiddish news media
I have questions about d3.js for graphs