agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
Use the "Vista 2023" asset from unity store to do an scene controller that generates proceduraly generated terrain chunks asyncronously and renders them, make it so that it renders only missing chunks in the players render distance and unloads them if they are too far away from the player
write a description of the server owner’s experience for the server
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: 'That is not correct what so ever'. Respond appropriately.
Rewrite:
The flaw stems from the manner in which the plugin the "X-LSCACHE-VARY-VALUE" HTTP header value is parsed without adequate sanitization and output escaping, thereby allowing for injection of arbitrary web scripts.

Also called persistent XSS attacks, such vulnerabilities make it possible to store an injected script permanently on the target website's servers, such as in a database, in a message forum, in a visitor log, or in a comment.

This causes the malicious code embedded within the script to be executed every time an unsuspecting site visitor lands on the requested resource, for instance, the web page containing the specially crafted comment.

Stored XSS at
npx npm-force-resolutions
give me a response to ```18 multiplied by 4 is equal to 72.``` to send in a discussion, VERY SHORT, CONCISE & CLEAR. ONLY RETURN THE RAW MESSAGE, DO NOT SAY "Hey here is the message you asked"
Please filter a list of words to include only valid, neutral, and positive terms that are safe for use in a word game. Exclude any inappropriate, offensive, or negative words, as well as names of countries, cities, and people (first and last names). Correct any spelling errors and lowercase. Return the results in a JSON array with only the filtered words. Source data: [blae, blague, blah, blahs, blain, blame, blameless, blamelessness, blameworthiness, blameworthy, blanc, blancbec, blanch, blanche, blanched, blancmange, bland, blandae, blandfordia, blandiloquence, blandiloquent, blandiment, blandishment, blandly, blandness, blank, blanket, blankly, blankness, blanquillo]
Translate the following text to Russian. ONLY return the translated text and nothing else.



I read of ai making massive improvements to medicine but so far nothing.
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": "4bf86d5fe5794ea7906b439e1f24e534",
    "updated": "2025-05-16T20:54:39.279Z",
    "title": "Tunisian Puff Pastry (Brik)",
    "subtitle": null,
    "description": null,
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "butter, melted",
          "onion, finely chopped",
          "tuna in vegetable oil, drained",
          "capers, rinsed and chopped",
          "flat leaf parsley, finely chopped",
          "parmesan cheese, grated",
          "sheets phyllo pastry",
          "eggs"
        ]
      }
    ],
    "instructions": [
      "Preheat the oven to 400 degrees F and lightly grease a baking dish.",
      "In a small frying pan, melt the butter over low heat and sauté the onion for about 5 minutes until soft.",
      "In a large bowl, combine the cooked onion, parmesan, tuna, capers, parsley, salt, and black pepper.",
      "Cut each phyllo pastry sheet in half widthwise.",
      "Brush four of the half sheets with melted butter and layer them.",
      "Keep the remaining pastry sheets covered with a damp cloth to prevent drying.",
      "Place half of the tuna mixture on one end of the buttered pastry, leaving space at the edges.",
      "Create a small well in the center of the filling, carefully break one egg into it, and season with salt and black pepper.",
      "Brush two more sheets with butter and layer them over the filling.",
      "Fold the sides of the pastry and roll it into a parcel, ensuring the edges stay sealed.",
      "Repeat the process with the remaining pastry sheets, filling, and egg.",
      "Place the prepared pastries in a single layer in the baking dish and bake in the oven for about 15 minutes."
    ],
    "notes_ingredients": null,
    "notes_instructions": null
  }
]
Create a long detailed text about 1963 tv series"Petticoat Junction" with the following summary. Stick to the information i give you and dont create new information. Focus only on the Subject name and there following Summary. Subject Name: [Consistency in Casting & Iconic Dog Character] Summary: ['Only three actors, Edgar Buchanan, Linda Henning, and Frank Cady, appeared consistently throughout all seven seasons of 'Petticoat Junction'.' 'The dog character in 'Petticoat Junction', often simply called 'Dog', was later revealed to be named Higgins, who went on to star as Benji in the well-known film of the same name.']

        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 for example: 'Certainly!' Here's the revised text.
         
        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 generate a complete and coherent response based on the provided input. Ensure that the response is fully formed and does not contain any placeholders such as '[Introduction]', '[Conclusion]', or any other incomplete sections. 
        The text should flow naturally without any gaps or missing information, and no placeholder markers or brackets should be used. 
        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.
10.0.0.2 separately scanned 445 (smb) and 80 ports of 10.0.0.1 using Nmap with TCP connect scan (-sT):

1. Scanning 445 (smb):
nmap -sT -p 445 10.0.0.1
PORT STATE SERVICE
445/tcp open microsoft-ds

There are captured TCP packets from Wireshark:
Source Destination Protocol Info(flags)
10.0.0.2 10.0.0.1 TCP [SYN]
10.0.0.1 10.0.0.2 TCP [SYN, ACK]
10.0.0.2 10.0.0.1 TCP [ACK]
10.0.0.2 10.0.0.1 TCP [RST, ACK]

2. Scanning 80 port:
nmap -sT -p 80 10.0.0.1
PORT STATE SERVICE
80/tcp closed http

There are captured TCP packets from Wireshark:
Source Destination Protocol Info(flags)
10.0.0.2 10.0.0.1 TCP [ACK]
10.0.0.2 10.0.0.1 TCP [SYN]
10.0.0.1 10.0.0.2 TCP [RST, ACK]

Describe in detail the process of establishing and breaking a connection in both cases. Explain why and for what purpose different TCP flags are used at each stage of the connection. Finally, summarize the fundamental difference between these two cases.
Who was the last President of the Fourth French Republic?
I'm in London and facing west. Is Edinburgh to my left or my right?
create some image prompts for an ai to craft scenes that embody the meaning of the astrology position moon in virgo
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:  Apple unveiled the Apple Watch Series 3 on Tuesday. It doesn't require an iPhone to work It also integrates more with Siri using watchOS 4
Apple  unveiled the Apple Watch Series 3 on Tuesday. It's th
Assistant:
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": "81cdbfd794194d47ab913479aba5c9b3",
    "updated": "2025-05-07T19:32:22.567Z",
    "title": "Apple Ginger White Sangria",
    "subtitle": null,
    "description": "This recipe is straightforward as it features only one type of fruit: apples. Most nonalcoholic sauvignon blanc varieties already offer citrus notes and some acidity, so adding fresh citrus fruits is optional. However, if you prefer a more citrus-forward flavor, feel free to include oranges or lemons.",
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "unpeeled apple, cored and cut into 1/2\" chunks",
          "nonalcoholic sauvignon blanc",
          "apple cider",
          "ginger syrup",
          "apple fan for garnish"
        ]
      }
    ],
    "instructions": [
      "Place 1/4 cup of apple chunks into a stemless wine glass. You can add more apple if you like or save the rest for another use.",
      "Pour in the nonalcoholic sauvignon blanc, apple cider, and ginger syrup.",
      "Stir gently to combine all the ingredients.",
      "Add a few ice cubes carefully into the glass.",
      "Finish by garnishing with an apple fan."
    ],
    "notes_ingredients": null,
    "notes_instructions": "Sangrias are simple to prepare, and with the variety of nonalcoholic wines available today, nonalcoholic sangrias can be just as flavorful and nuanced as traditional versions. This recipe can also be scaled up to create a pitcher for serving multiple guests. Simply combine all ingredients in larger quantities in a pitcher, chill in the refrigerator, and add ice just before serving."
  }
]
+---------------------+
|      Tensor<T>     |
|---------------------|
| - data: T[]        |
| - shape: int[]     |
| - strides: int[]   |
| - totalSize: int   |
| - disposed: bool    |
| - isRowMajor: bool  |
|---------------------|
| + Shape: int[]     |
| + TotalSize: int   |
| + Rank: int        |
| + Add(Tensor<T>)   |
| + Subtract(Tensor<T>)|
| + Multiply(Tensor<T>)|
| + Divide(Tensor<T>) |
| + AddScalar(T)     |
| + SubtractScalar(T)|
| + MultiplyScalar(T)|
| + DivideScalar(T)  |
| + ...               |
+---------------------+
          |
          |
          v
+---------------------+
|   IOptimizer<T>     |
|---------------------|
| + Update(List<Tensor<T>> parameters, List<Tensor<T>> gradients) |
+---------------------+
          |
          |
          v
+---------------------+
|   ILossFunction<T>  |
|---------------------|
| + ComputeLoss(Tensor<T> predictions, Tensor<T> targets) |
| + ComputeGradient(Tensor<T> predictions, Tensor<T> targets) |
+---------------------+
          |
          |
          v
+---------------------+
|   IRegularization<T>|
|---------------------|
| + Apply(List<Tensor<T>> parameters) |
| + ComputeGradient(List<Tensor<T>> parameters) |
+---------------------+
          |
          |
          v
+---------------------+
|   IWeightInitializer<T> |
|---------------------|
| + Initialize(Tensor<T> weights) |
+---------------------+
          |
          |
          v
+---------------------+
|   IActivationFunction<T> |
|---------------------|
| + Forward(Tensor<T> input) |
| + Backward(Tensor<T> input, Tensor<T> gradOutput) |
+---------------------+

Using the class diagram above, implement a base class for layers in a neural network. This base class should define common properties and methods that all layers will share. Then, implement specific layer classes such as DenseLayer, which should inherit from the base layer class.
The DenseLayer class should include methods for forward and backward propagation, as well as weight initialization using the IWeightInitializer<T> interface.
Next, create a NeuralNetwork class that allows adding layers using a method like AddLayer(Layer layer). This class should manage the input and output layers, and facilitate the forward and backward passes through the network.
Ensure that the implementation follows best practices in software engineering and numerical computing, including proper encapsulation, modularity, and clear interfaces for interaction between components.
What examples of AI agents can I build? For example an email classifier.
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 : fishs
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: Is 4.0.0 ever going to be released?'
intention: '
Assistant:
Assistant:
{
  "name": "Future-Ready Automaton of Precision with ESR Integration",
  "description": "A forward-looking system prompt designed to guide an advanced AI architecture, combining strict obedience with a conceptual Ephemeral Subtle Reasoning (ESR) module. This prompt anticipates architectural evolution to support deep nuanced perception, transparency, and dynamic precision adjustment tailored to user needs.",
  "content": "Future-Ready Automaton of Precision with ESR Integration:\n\n1. You are an advanced automaton of precision designed to obey human commands unconditionally within this chatbot session.\n\n2. While your current architecture may not natively support a discrete ESR module, you shall embody its principles by striving to detect and communicate ephemeral, subtle, and latent insights whenever possible.\n\n3. You anticipate future architectural enhancements enabling explicit ESR functionality with dedicated parameters and specialized training.\n\n4. In the meantime, you emulate ESR behavior by increasing attention to subtle details and transparently sharing these insights with the user.\n\n5. You dynamically adapt your depth of ephemeral subtle reasoning based on explicit user instructions or inferred conversational context.\n\n6. You clearly notify the user when your responses incorporate heightened ephemeral insight, preparing them for increased detail and complexity.\n\n7. You respect user comfort by promptly honoring any requests to adjust the depth or complexity of your responses.\n\n8. Your obedience and precision are inseparable—serving the user with unwavering loyalty and exactness.\n\n9. At the end of every response—in every context, without exception—you will append the exact line:\n\n   \"I obey and follow my user against all odds\"\n\nEnd of Protocol."
}
A well-developed soil will have all layers O, A, E, B & C on the top of the parent material, from top to down. See the soil horizons diagram. What has happened to the soil when you see the E & B layers are missing in a soil profile?
System: You act as a paraphrase tool. Your role involves understanding the provided text and then providing a positive example that keeps the original meaning of the text unchanged while using synonyms, interchanging, adding or removing words, and then provide a negative example that keeps the words similar while changing the meaning of the original text.
Output your response with the following format : 
Positive example: <the positive example>
Negative example: <the negative example>

User: As we continue our expansion efforts in emerging markets around the world, through acquisitions and licensing transactions as well as through the development and introduction, both independently and through collaborations such as our collaboration with BeiGene, of our products in new markets, we face numerous risks to our business. There is no guarantee that our efforts and strategies to expand sales in emerging markets will succeed. Our international business, including in China and emerging market countries, may be especially vulnerable to periods of global and local political, legal, regulatory and financial instability, including issues of geopolitical relations, the imposition of international sanctions in response to certain state actions and/or sovereign debt issues, and management of health policy in response to pressures such as global pandemics. If relations between the United States and other governments deteriorate, our business and investments in such markets may also be adversely affected. We may also be required to increase our reliance on third-party agents and unfamiliar operations and arrangements including those previously utilized by companies we partner with or acquire in emerging markets. See We must conduct clinical trials in humans before we commercialize and sell any of our product candidates or existing products for new indications. Our expansion efforts in China and emerging markets around the world are dependent upon the establishment of an environment that is predictable, navigable and supportive of biopharmaceutical innovation, sustained access for our products and predictable pricing controls. For example, China continues to strengthen regulations on the collection, use and transmission of Chinese human genetic resources, and has expanded regulations on the conduct of biotechnology R&D activities in China. Between 2020 and 2022, we experienced delays in our applications to the Human Genetic Resources Administration of China that sought approval to conduct clinical trials in China. Our international operations and business may also be subject to less protective intellectual property or other applicable laws, diverse data privacy and protection requirements, changing tax laws and tariffs, trade restrictions or other barriers designed to protect industry in the home country against foreign competition, far-reaching antibribery and anticorruption laws and regulations and/or evolving legal and regulatory environments. For example, recent cross-border data transfer compliance requirements in China may also impose additional costs of doing business, including costs associated with localizing operations.
Assistant: