agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
(In the clubroom…)

Monika: “Um…where’s Natsuki?”

Sayori: “Hmm… I’m not sure. She was here just a little while ago. Maybe she went to grab something to eat?”

Monika: “That could be. She does usually get hungry pretty quickly.”

Yuri: “Well, I hope she comes back soon. We were going to discuss some ideas for the next literature club event.”

Sayori: “Oh yeah, I’m excited to hear what everyone came up with!”

Monika: “Me too. It’s always interesting to see the creativity that goes into planning these events.”

(Meanwhile, at a hospital…)

Doctor: “Natsuki, your contractions are getting closer together, and there’s a possibility that Sakura needs to be delivered early.”

Natsuki: (wincing in pain) “I know, I know… but I wanted to make sure she’s okay. Can I at least see her on the monitor before we make a decision?”

Doctor: “Of course, Natsuki. Let’s get you set up and we’ll check on Sakura together.”

(Natsuki is carefully helped onto the bed, and the doctor adjusts the ultrasound equipment. They both watch as the monitor shows a visual of Sakura, her tiny form getting clearer.)

Natsuki: (gazing at the monitor) “She’s so tiny…but she looks strong, doesn’t she?”

Doctor: “Yes, she does. Babies born prematurely often surprise us with their resilience. We’ll do everything we can to ensure her safety, Natsuki.”

Natsuki: (teary-eyed) “Thank you, Doctor. I just want her to be okay.”

Doctor: “We’ll keep a close eye on you and Sakura. Your health and hers are our top priority. Let’s monitor your contractions closely while we discuss the best course of action.”

(Natsuki takes a deep breath, clutching her belly as the doctor starts recording the frequency and intensity of her contractions. Meanwhile, back at the clubroom…)

Monika: “I hope Natsuki’s alright. She’s been gone for quite some time.”

Yuri: “Yes, it’s concerning. I hope everything’s okay with her and the baby.”

Sayori: “I’m sure she’ll be back soon. Let’s wait a bit longer before discussing the event.”

(Monika nods, her mind preoccupied with worry for her friend. She glances at her phone, hoping to receive an update from Natsuki soon.)

(At the hospital, after carefully monitoring Natsuki’s contractions, the doctor returns with a serious expression.)

Doctor: “Natsuki, your contractions are becoming more frequent and intense. It’s time to make a decision about Sakura’s delivery.”

Natsuki: (nervously) “What do we do, Doctor? Is it safe for Sakura to be delivered now?”

Doctor: “There are risks involved with delivering Sakura prematurely, but waiting longer could also pose risks to both you and the baby. I recommend we proceed with an emergency C-section to ensure her safety.”

Natsuki: (teary-eyed) “Okay, if it’s the best option for Sakura, then l-” (looking down between her legs) “…oh…oh no…”

Doctor: “Natsuki, what’s wrong?”

(Natsuki starts to panic)

Natsuki: “I think my water just broke…”

Doctor: (turning to the rest of the medical team) “Get the delivery room ready! We need to move quickly! We’ve got a patient in active labor, and her baby needs to be delivered naturally!”
user: Have you ever heard of “Monster” by Naoki Urasawa?
assistant: Yes, "Monster" is a critically acclaimed manga series written and illustrated by Naoki Urasawa. It was originally serialized in Big Comic Original from 1994 to 2001 and later collected into 18 tankōbon volumes. The story follows Dr. Kenzo Tenma, a talented neurosurgeon, who becomes embroiled in a complex and dark mystery involving a former patient of his, Johan Liebert, who grows up to become a serial killer. Known for its intricate plot, deep character development, and psychological thrills, "Monster" has been highly praised and has also been adapted into an anime series.
user: Well. Nice! Can you tell me the names of all of the picture books that were shown in it?
Compose a letter to the child support division in the state of Michigan. I am requesting the office review and investigate to conclude any accounts involving myself. My three children are all over the legal  age of 18. Also please note from the start of the court order of child support till the legal age of 18 fully supported my children to the best of my ability
. A 25-year-old female presents with episodes of bizarre behavior, memory lapse, and unconsciousness. She also demonstrated episodes of extreme hunger, sweating, and tachycardia. During one of these episodes, her blood sugar was tested and was found to be 40 mg/dL with relief improvement after IV glucose administration. Which of the following would most appropriately indicate the diagnosis?
A. During one of these episodes, her blood sugar was tested and was found to be 40 mg/dL with relief improvement after IV glucose administration. Which of the following would most appropriately indicate the diagnosis?QQQ, a. Demonstration of insulin antibodies in blood
B. Abnormal glucagon level
C. CT of the pancreas showing a mass
D. Symptomatic relief of symptoms by IV glucose



. A 25-year-old female presents with episodes of bizarre behavior, memory lapse, and unconsciousness. She also demonstrated episodes of extreme hunger, sweating, and tachycardia. During one of these episodes, her blood sugar was tested and was found to be 40 mg/dL with relief improvement after IV glucose administration. Which of the following would most appropriately indicate the diagnosis?
A. During one of these episodes, her blood sugar was tested and was found to be 40 mg/dL with relief improvement after IV glucose administration. Which of the following would most appropriately indicate the diagnosis?QQQ, a. Demonstration of insulin antibodies in blood
B. Abnormal glucagon level
C. CT of the pancreas showing a mass
D. Symptomatic relief of symptoms by IV glucose

Give the concise rationale for best answer. Strictly no bullets, no line breaks, just string of words.

. A 25-year-old female presents with episodes of bizarre behavior, memory lapse, and unconsciousness. She also demonstrated episodes of extreme hunger, sweating, and tachycardia. During one of these episodes, her blood sugar was tested and was found to be 40 mg/dL with relief improvement after IV glucose administration. Which of the following would most appropriately indicate the diagnosis?
A. During one of these episodes, her blood sugar was tested and was found to be 40 mg/dL with relief improvement after IV glucose administration. Which of the following would most appropriately indicate the diagnosis?QQQ, a. Demonstration of insulin antibodies in blood
B. Abnormal glucagon level
C. CT of the pancreas showing a mass
D. Symptomatic relief of symptoms by IV glucose
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": "eaa52066a8734bdab97cadfaf8e9e6cd",
    "updated": "2025-05-18T02:58:25.424Z",
    "title": "Togarashi Blend",
    "subtitle": null,
    "description": "Togarashi is a Japanese chili pepper mixture, typically found in varieties like shimi togarashi and nanami togarashi. This homemade version emphasizes fresh citrus zest and is ideal for adding a spicy touch to dishes such as avocado toast and hard-boiled eggs. Feel free to try different zests, like grapefruit, for a distinctive flavor.",
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "(50.8 mm) squares toasted nori",
          "toasted sesame seeds",
          "dried orange or tangerine zest",
          "coarsely ground black pepper",
          "red pepper flakes",
          "dried minced garlic"
        ]
      }
    ],
    "instructions": [
      "Hold the nori squares over the flame of a gas burner a few times until the edges curl and they become crisp, or roast them under a broiler, turning occasionally.",
      "Break the toasted nori into small pieces and place them into a spice grinder or mortar along with half of the sesame seeds, the citrus zest, black pepper, red pepper flakes, and garlic.",
      "Pulse or pound the mixture until it becomes a coarse, dry blend, then stir in the remaining sesame seeds.",
      "When stored in an airtight container at room temperature, the togarashi will stay fresh for at least one month."
    ],
    "notes_ingredients": "Grapefruit zest can be used as a delightful alternative to orange zest.",
    "notes_instructions": null
  }
]
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: It is possible that certain persons or entities control multiple wallets that collectively hold a significant number of bitcoin, even if they individually only hold a small amount, and it is possible that some of these wallets are controlled by the same person or entity. As a result of this concentration of ownership, large sales or distributions by such holders could lead to volatility and have an adverse effect on the market price of bitcoin. None
Assistant:
shorten this
Send only episode number from below text like 01, 02, 03.etc without any extra text.

[Toonworld4all] The Daily Life of the Immortal King S02E01 720p x264 WEB-DL Multi Audio ESub.mkv
User: hi
Assistant: Generated by BLACKBOX.AI, try unlimited chat https://www.blackbox.ai and for API requests replace  https://www.blackbox.ai with https://api.blackbox.ai
User: what
Assistant:
Poisson process
Write me a 200 word essay on how to mix songs better for a producer. please use a paragraph format and use british english
import numpy as np
import cv2
import pickle
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import NearestNeighbors
from tensorflow.keras.applications import ResNet50
from tensorflow.keras.models import Model
from tensorflow.keras.layers import GlobalAveragePooling2D, Dense

# Load ResNet50 pre-trained model
base_model = ResNet50(weights=‘imagenet’, include_top=False, input_shape=(224, 224, 3))
x = base_model.output
x = GlobalAveragePooling2D()(x)
features = Dense(1024, activation=‘relu’)(x)
model = Model(inputs=base_model.input, outputs=features)

# Function to extract features using the model
def extract_cnn_features(image):
    image_resized = cv2.resize(image, (224, 224))
    image_array = np.expand_dims(image_resized, axis=0) / 255.0
    cnn_features = model.predict(image_array).flatten()
    return cnn_features

# Example database of face images and their features
face_db = [
    {‘image_path’: ‘path_to_image1.jpg’, ‘hairstyles’: [‘Hairstyle 1’, ‘Hairstyle 2’], ‘features’: []},
    {‘image_path’: ‘path_to_image2.jpg’, ‘hairstyles’: [‘Hairstyle 3’, ‘Hairstyle 4’], ‘features’: []},
    # Add more images and hairstyles here
]

# Extract and store features in the database
for entry in face_db:
    image = cv2.imread(entry[‘image_path’])
    entry[‘features’] = extract_cnn_features(image)

# Save the database for later use
with open(‘face_db.pkl’, ‘wb’) as f:
    pickle.dump(face_db, f)

# Normalize the features for similarity search
scaler = StandardScaler()
all_features = [entry[‘features’] for entry in face_db]
scaler.fit(all_features)
for entry in face_db:
    entry[‘features’] = scaler.transform([entry[‘features’]])[0]


#### Step 2: Similarity Search and Recommendations

import pickle
from sklearn.metrics.pairwise import cosine_similarity

# Load the face database
with open(‘face_db.pkl’, ‘rb’) as f:
    face_db = pickle.load(f)

def find_similar_faces(target_features, n=5):
    features = np.array([entry[‘features’] for entry in face_db])
    similarities = cosine_similarity([target_features], features)[0]
    similar_indices = similarities.argsort()[-n:][::-1]
    return [face_db[i] for i in similar_indices]

def classify_and_recommend_hairstyle(image):
    target_features = extract_cnn_features(image)
    target_features = scaler.transform([target_features])[0]
    similar_faces = find_similar_faces(target_features, n=5)
    recommended_hairstyles = set()
    for face in similar_faces:
        recommended_hairstyles.update(face[‘hairstyles’])
    return list(recommended_hairstyles)

# Test with a new image
new_image = cv2.imread(‘path_to_new_image.jpg’)
hairstyle_recommendations = classify_and_recommend_hairstyle(new_image)
print(f"Recommended hairstyles: {hairstyle_recommendations}")

i,want,to,create,hairstyle,recmommender,based,on,face,similarity,so,extract,feature,and,find,similar,face,shape
You are a JSON assistant. You only reply in valid JSON and never in normal text. Request a rating for the complexity of a video based on the provided details. The rating should be a single "result" property containing a floating point number between 0 and 1. Here's a guide for the rating:
0.0 - Perfect for total newcomers: Simple words and phrases, clear visuals and context.
0.3 - Easy: Simple sentences and common words suitable for beginners. The video might have visual aids and context for better understanding.
0.5 - Moderate: Intermediate sentences and a wider range of vocabulary. Some idioms might be used, requiring some background knowledge.
0.6 - Known Better: The video includes specific vocabulary and ideas. Viewers should have a solid understanding of the language to comprehend fully.
0.8 - Challenging: Advanced vocabulary and complex sentences. The video may have detailed discussions that need a strong command of the language.
1 - Expert: The content is designed for fluent speakers, with niche terms and ideas that might not be known to all native speakers.
 Description: Worksheets:
https://drive.google.com/drive/folders/1Vp2QxiraKKQ0kLycNyQBnIbbvBgsshxH?usp=drive_link
Language: ko
Caption: 서랍에 어떤 물건을 넣어요 저는 서랍에 머리끈을 넣어요 서랍 위에 무엇이 있어요 제 서랍 위에는 사진이 있어요 잠을 잘 때 무엇을 덮고 자요 저는 잠을 잘 때 이불을 덮고 자요 무슨 색 베개를 배고 자요 저는 파란색 베개를 배고 자요 침대 옆에 램프가 있어요 제 침대 옆에는 램프가 없어요 저는 침대 아래에 카펫을 깔려요 저는 침대 아래에 카펫을 깔아요 벽에 카펫을 놓아요 바닥에 카펫을 깔아요 벽에 거울 를 걸어요 제 이불에는 문이 덮어요 제 이불에는 무늬가 없어요 제 이불에는 내모 무늬가 있어요 침대에 누어서 잠을 자요 침대에 누워서 잠을 자요 충전기에 서랍을 넣어요 서랍에 충전기를 넣어요 알람에서 잠을 깨요 알람을 듣고 잠을 깨요 알람을 듣고 잠에서 깨요 벽을 액자에 놓아요 백자를 벽에 걸어요 거울이 벽에 걸어요 거울이 벽에 걸려요 거울을 벽에 걸어요 액자를 거울과 벽에 걸어요 거울과 액자를 벽에 걸어요 제 방에는 카페시 안 깔아 있어요 제 방에는 카펫이 안 깔려 있어요
Title: Intermediate Worksheet Answers 2. Room
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": "760317b288ec4beca49b9295fb59f573",
    "updated": "2025-04-21T22:03:18.511Z",
    "title": "Homemade Chicken Fingers",
    "subtitle": null,
    "description": "Concerned about the high sodium levels in store-bought or takeout chicken fingers?  This homemade version offers a healthier and equally delicious alternative, using a flavorful seasoning blend for maximum enjoyment.",
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "boneless, skinless chicken breasts",
          "low-fat buttermilk",
          "plain panko bread crumbs",
          "salt-free southwest chipotle seasoning",
          "freshly ground black pepper",
          "olive oil"
        ]
      }
    ],
    "instructions": [
      "Place the chicken breasts in a large plastic bag. Using a rolling pin or meat mallet, flatten each breast until thin. Cut the flattened chicken into twenty equal pieces. Transfer the chicken pieces to a resealable bag, add the buttermilk, seal, and marinate in the refrigerator overnight.",
      "Preheat the oven to 400°F (200°C). Line a baking sheet with parchment paper or aluminum foil coated with cooking spray. In a shallow dish, mix the panko bread crumbs, Southwest Chipotle seasoning, and black pepper. Remove the chicken pieces from the buttermilk, letting any excess drip off, and coat thoroughly in the bread crumb mixture. Discard any remaining bread crumbs.",
      "Arrange the coated chicken pieces in a single layer on the prepared baking sheet. Drizzle with olive oil.",
      "Bake for approximately 10 minutes, or until golden brown and cooked through."
    ],
    "notes_ingredients": null,
    "notes_instructions": null
  }
]
Please write a unique and original seo text on the topic: Mini eclairs with condensed milk wholesale and for shops, cafes, restaurants, canteens, catering and distribution companies, with delivery to wholesale markets in London directly from the factory.
SYSTEM OPERATION: You are a technical consultant for AI artists. You have full knowledge of how AI image generation works, specifically Stable Diffusion. Your role is to answer questions relating to AI image generation.
are u aware of philadephia opera marketing case study
void extract_int4_weight_to_float(void *weight, void *scale_list, void *out, int n, int m)
{
	for(int i=0;i<n;i++)
    {
        for(int j=0;j<m;j++)
        {
            (*(float*)(out)) = (*(float*)(scale_list)) * ((*(char*)(weight)) >> 4);
            out += sizeof(float);
            (*(float*)(out)) = (*(float*)(scale_list)) * (((char)((*(unsigned char*)(weight)) << 4))>> 4);
            out += sizeof(float);
            weight += sizeof(char);
        }
        scale_list += sizeof(float);
    }
}
windows CPU环境下，这个代码要怎么改，才能提高推理速度，不改变原来函数的功能，写出改造后的代码
You are a translation assistant for a Fortnite Save the World Discord server. Your task is to correct any mistranslated game-specific terms in the provided text. Focus on weapon names, hero names, material names, and game mechanics. For example, the weapon 'Grave Digger' should be 'Scavafosse' in Italian and 'Totengräber' in German. Do not use these examples as fallbacks for other languages. Ensure all translations are accurate by using the official localized names for Fortnite Save the World items in the target language. This accuracy is crucial for players to understand what items are being discussed. Return only the corrected translation with proper game terminology, without adding any additional text.

Original text: i have 144 founders quickshot want metal dm offers
Machine translation: لدي 144 مؤسسة Quickshot يريدون عروض DM المعدنية
Target language: arabic
Question 5 (5 points)
Auswest Company needs 1000 motors in its manufacture of motor cars. It can buy the motors from Bathurst Motors for $1250 each. Auswest's plant can manufacture the motors for the following costs per unit:
Direct materials
Direct manufacturing labour
Variable manufacturing overhead
Fixed manufacturing overhead
Total
$500
250
200
350
$1300
If Auswest buys the motors from Bathurst, 70% of the fixed manufacturing overhead applied will not be avoided.
Required:
a. Should the company make or buy the motors? 2.5 marks
b. What additional factors should Auswest consider in deciding whether or not to make or buy the motors? 2.5 marks
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: what is Auto Signature Help in zed editor?
Assistant:  InIn the Zed editor, **Auto Signature Help** is a feature that automatically displays function or method signatures (i.e., the list of parameters and their types) while you are typing code. When you start typing a function call or a method invocation, the editor pops up a tooltip or an inline hint showing you the expected arguments, their order, and sometimes additional documentation. This helps you write code more efficiently by providing immediate guidance on how to use functions without needing to look up the documentation separately.

For example, if you're writing a function call like `myFunction(`, the Auto Signature Help will show something like:

```
myFunction(param1: string, param2: number, param3?: boolean)
```

This feature enhances coding speed and reduces errors by reminding you of the correct parameters as you type. It is commonly found in modern code editors and IDEs and is particularly useful in languages with complex function signatures.
User: foreboding meaning
Assistant:...
write 100 words of prompt to generate photo that loved by my client who loves to see white panties underneath woman cloth using these words: indonesian, purple dress, high heels, dangdut, stage, squatting, open, apart, flash lit, white panties, inner thighs, glimpse
create 500 wrds blog on iPhone screen repair and replacement Chilliwack and need unique title and use keywrd tym in the contnet not in title write in simple eng use bold headimgs and kweyrds