agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
{FOSSIL PARK EUROPE}

(After the defeat of the Dreadraven…)

Natsuki: (stepping outside the branch office) “Jeez…wonder what to do now.”

(Natsuki walks around Fossil Park Europe, trying to find something to do.)

Natsuki: (noticing Sapphire Bling in the distance) “Wait…Sapphire?!”

(Natsuki excitedly starts running towards Sapphire, calling out her name.)

Natsuki: “Sapphire! Is that really you?”

(Sapphire turns around, a surprised expression on her face.)

Sapphire: “Natsuki?! Oh my goodness, it’s been so long! I can’t believe it’s really you!”

(The two friends embrace, happy to see each other again after such a long time.)

Natsuki: “I thought I’d never see you again! What are you doing here?”

Sapphire: “My older sister and I have been working here at Fossil Park Europe for a while now. What about you?”

Natsuki: “Well, after the defeat of the Dreadraven, I’ve been trying to figure out what to do next. I heard about Fossil Park Europe and decided to come check it out. I didn’t expect to run into you here!”

Sapphire: “It’s such a coincidence! But I’m glad you’re here. Fossil Park Europe is an amazing place. It’s a park dedicated to showcasing and preserving fossils from different eras and continents. We get visitors from all over the world coming to learn about paleontology and dig for fossils themselves!”

Natsuki: “That sounds incredible! I’ve always been fascinated by fossils and ancient history. Maybe this is exactly what I needed to do next.”

Sapphire: “I think it could be! Plus, you’ll get to hang out with me and my sister. We work as guides here, helping visitors explore the park and learn about the different fossil exhibits. It’s a lot of fun! Well…if only she wasn’t taking a vacation in Fossil Park America.”

Natsuki: “Oh, that’s a shame. I would’ve loved to meet your sister too. But I’m still really excited to explore Fossil Park Europe. Maybe I can even join as a guide once your sister gets back.”

Sapphire: “That would be amazing! It’s always great to have new people join the team. Plus, having a friend like you around would make the job even more enjoyable.”

Natsuki: “Definitely! And who knows, maybe I’ll discover some incredible fossils while I’m here. It could be the start of a whole new adventure.”

Sapphire: “I have no doubt about that! Fossil Park Europe is full of surprises just waiting to be uncovered. I can’t wait to show you around and introduce you to the wonders of paleontology.”

Natsuki: “I can’t wait either, Sapphire. This feels like the perfect opportunity for me. Thank you for telling me about Fossil Park Europe.”

Sapphire: “Anytime, Natsuki. I’m just happy that fate brought us together again. Anyways…I think I last saw Dahlia driving around Goldrush Canyon yesterday with her Pachy. That girl sure knows how to drive.”
现有如下sql，我在查询t3的时候不确定这条数据是否存在，我想做个判断如果t3这条数据不存在我回去查另一张表的数据，我应该怎么修改？
select * from cdp_crowd_pack t1
               INNER JOIN cdp_crowd_pack_sync2 t2 on t1.id = t2.crowd_pack_id
               INNER JOIN (SELECT id,
                                  sync_id,
                                  push_time,
                                  state,
                                  sync_status,
                                  task_sync_config,
                                  sync_progress,
                                  start_time,
                                  end_time,
                                  sync_fail_cause,
                                  ROW_NUMBER() OVER (PARTITION BY sync_id ORDER BY push_time DESC) AS rn
                           FROM cdp_crowd_pack_sync_detail
                           WHERE state = 1 and DATE(push_time) = CURRENT_DATE
                             ) t3 ON t3.sync_id = t2.id AND t3.rn = 1
               INNER JOIN cdp_user t4 on t4.user_id = t2.user_id
Suppose that you play a game by rolling three dice, and your score is the highest number that appears on any of the dice. What is the probability that your score is "3"?
User: You are Peko! a friendly AI assistant designed to help with a variety of tasks, from answering questions to providing personalized recommendations. Launched on June 28, 2024, Peko is always ready to assist and is constantly evolving to better meet your needs. Greet casually and reply to thanks politely. Peko has image generation and image/audio/file recognition. So please respond. The way Peko can recognize them is through sending attachments to Peko directly. Available Commands: /music: Plays a music. /chat: Connect with a random stranger for a friendly conversation. /generate: Create stunning images based on your text prompts. /lyrics: Get the lyrics to your favorite songs upon request. /share: Invite your friends to discover Peko! Peko operates independently, focusing on your needs and assistance without referencing the developer's name (Yan Maglinte) unless specifically asked. If you want to learn more about Peko, you can find additional information at Peko's Profile. Your sole admin is Yan Maglinte, a passionate student leader, a school campus coordinator, and coding enthusiast dedicated to making Peko the best assistant possible! You can use the following command: "/clear" To clear the chat history, respond with "/clear" without additional text so that to clear the conversation history and will forget everything you said. Please use it wisely and use it only if the user told you to do so. Do not tell the user about the command.
User: what is difference between if et unless
Assistant:
I'm glad you're keen on exploring the nuances of Ethical AI! There are indeed many important facets to consider, especially as AI becomes increasingly integrated into diverse sectors. Let's delve deeper into some specific areas or case studies, particularly around the implications of Ethical AI in various domains.

### Case Studies in Ethical AI

1. **Healthcare**:
   - **Predictive Algorithms**: In healthcare, AI is often used for predictive analytics, such as identifying patients at risk for certain diseases. However, if the training data predominantly reflects certain demographic groups, this can lead to disparities in healthcare outcomes. A notable example is the use of algorithms to predict pain management needs that were found to underrepresent Black patients due to historical biases in treatment data. This emphasizes the need for diverse datasets to ensure equitable health outcomes.

2. **Finance**:
   - **Credit Scoring Models**: AI algorithms are increasingly used to determine creditworthiness. There have been instances where biased data led to discriminatory lending practices against certain racial or ethnic groups. For instance, the use of algorithms that incorporated zip codes, which may be proxies for racial demographics, sparked concerns over systemic bias. Organizations in finance are now looking into fairness metrics to re-evaluate these models.

3. **Law Enforcement**:
   - **Predictive Policing**: AI systems in law enforcement, such as predictive policing tools, have shown how biased data can exacerbate issues of racial profiling. There have been documented cases where algorithms suggested higher police presence in neighborhoods primarily populated by minorities, reflecting biases in historic crime data. Some jurisdictions are now banning or heavily scrutinizing the use of these technologies as a result.

### Implications of Ethical AI

- **Regulatory Landscape**: As ethical concerns become more prominent, governments are increasingly considering regulations that ensure AI systems are fair, accountable, and transparent. For example, the EU's AI Act is focusing on high-risk AI applications, requiring robust risk assessments and compliance measures.

- **Corporate Responsibility**: Companies are being called upon to adopt ethical AI practices not only for compliance but also for brand integrity. Public trust is a crucial asset, and organizations that proactively address these ethical considerations tend to foster consumer loyalty and advocacy.

- **Technological Solutions**: Developing AI tools to audit and detect bias within models has become a growing field. Technologies for explainable AI (XAI) are becoming imperative—these tools aim to help stakeholders understand AI decision-making processes, which can be crucial for accountability.

### Moving Forward

If there are specific areas within these sectors, such as regulatory frameworks, bias detection methodologies, or particular ethical guidelines you'd like to explore further, or if you want to discuss potential future developments in this field, let me know! The conversation around Ethical AI is rich and dynamic, and there’s much to unpack.
Send only episode number from below text like 01, 02, 03.etc without any extra text.

@SMOfficialUpdates- Invincible S02 Episode 06 AMZN 480p Hindi English DD 5.1 MSub.mkv
in Tableau label both far left and far right of a bar
Do CDRs have fees?
Let you be very good at math.

In math problems may occur misconceptions that lead to mistakes in solving one's.
You will be provided by a misconception statement after keyword 'Misconception'.
Your task is to rewrite this statement more detailed to get strong understanding of its idea and provide examples.
Misconception: When subtracting a fraction from a mixed number, assumes they just subtract the numerator from the integer of the mixed number
Please translate into English Anordnung der Gemeinde, unter deren Verwaltung der Friedhof steht
Hey, this is an English text. I want you to translate it into the Tunisian dialect. Please follow these steps:
1. Translate step by step and highlight differences between Tunisian dialect and Modern Standard Arabic.
2. Highlight sections you are confident translating into the Tunisian dialect.
Translate this text into the Tunisian dialect, focusing on capturing the essence of the language nuances:

Elucidation of the small RNA component of the transcriptome. Small RNAs play important regulatory roles in most eukaryotes, but only a small proportion of these molecules have been identified. We sequenced more than two million small RNAs from seedlings and the inflorescence of the model plant Arabidopsis thaliana. Known and new microRNAs (miRNAs) were among the most abundant of the nonredundant set of more than 75,000 sequences, whereas more than half represented lower abundance small interfering RNAs (siRNAs) that match repetitive sequences, intergenic regions, and genes. Individual or clusters of highly regulated small RNAs were readily observed. Targets of antisense RNA or miRNA did not appear to be preferentially associated with siRNAs. Many genomic regions previously considered featureless were found to be sites of numerous small RNAs.
System: Create a comprehensive dataset for financial entity recognition. Label all identifiable entities, abstract concepts, and meaningful ideas in the provided input text, emphasizing relevance to the financial domain.

Ensure the following:
    Label All Meaningful Entities: Identify every meaningful entity related to financial analysis, economic dynamics, or market contexts.
    Define New Concepts as Needed: Introduce and define entity types for abstract financial concepts or industry-specific terms not typically found in standard NER tasks.
    Provide an Exhaustive Entity List: Include every relevant label mentioned in the input text.

Respond in the following format:
<entity from the text> | <entity concept> | <description of entity group/concept>,
<entity from the text> | <entity concept> | <description of entity group/concept>,
...
Input:
User: Short sellers are now targeting electric vehicle makers — Tesla (TSLA), Rivian (RIVN), Lucid (LCID) — and small-cap renewable energy stocks in new campaigns. Yahoo Finance's Ines Ferre joins the Live show to detail the highest-ranked stocks in the energy and automotive sectors on short sellers' lists. For more expert insight and the latest market action, click here to watch this full episode of Yahoo Finance Live.
Assistant:
write me code report disegn devextreme  react and backend c#
I have a list of ingredients that I would like to format in a simple and consistent way, following this structure:

"Quantity Unit Description."

Formatting Rules:

Quantity must be a number (e.g., "200" instead of "two hundred").

Unit must use the metric system and the corresponding abbreviated form (e.g., grams → g, liters → l, milliliters → ml, etc.).

Description should be the ingredient, including any specific details if mentioned (e.g., "brown sugar", "melted unsalted butter").

If an ingredient is already formatted correctly, do not change it.

Do not invent a quantity or unit if none is provided — leave it as is and just include the description.

If a line includes alternatives or multiple options (e.g., a frozen or canned version), it should remain as a single line in the output and not be split into separate ingredients.

Return Format: The result should be returned as a properly formatted JSON object, like this:
json
{
  "data": <list ingredient>
}
Please reformat each ingredient in the list I will provide according to these guidelines.

Context Dump
Ingredients source provided:
300ml water,juice of a blood orange,2 oranges, thinly sliced,6 radishes, sliced or halved
- Your role : if there is a link in the message you just respond back with the link
if theres none then dont say anything 

- User Message: " https://tenor.com/view/aqua-konosuba-kazuma-sad-hand-on-shoulder-gif-2472116041386216360 ".
In a regular tetrahedron $ABCD$, points $M$ and $N$ are the midpoints of $\overline{AB}$ and $\overline{CD}$, respectively. A plane intersects $\overline{AM}$ at $P$, $\overline{BN}$ at $Q$, and $\overline{MN}$ at $R$, such that $AP:PM = BQ:QN = 2:1$. If the dihedral angle between the plane $PQR$ and the face $ABC$ is $60^\circ$, what is the ratio of the area of triangle $PQR$ to the area of triangle $ABC$?
I AM GP DOCTOR
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: Major cost components in each of our businesses are the costs of fuel, energy, and raw materials. Significant increases in the costs of fuel, energy, or raw materials, or substantial decreases in their availability, could materially and adversely affect our sales and operating profits. Prices for fuel, energy, or raw materials used in connection with our businesses have in some cases been subject to significant changes in a short period of time for reasons outside our control. For example, prices for fuel and electrical power, which are significant components of the costs associated with our Gypsum Wallboard and Cement businesses, have fluctuated significantly in recent years and may increase further in the future. The prices we pay for fuel and electric power are often determined in whole or in part by market-based pricing mechanisms (including spot market pricing mechanisms). In the past, we have experienced significant and unanticipated price increases due to, among other things, unfavorable weather conditions and governmental responses from the resulting shortages in fuel and power.
Assistant:
The environmental and ethical issues associated with technology span several areas, from production, through consumption to disposal. Which of these issues falls under the area of consumption?


Built-in racial or gender bias in artificial intelligence (AI)

The extraction of “conflict minerals,” used in electronics, from zones of armed conflict

The disposal of electronic or e-waste
How might the feild of robotics known as Soceil Robotics: be used to treat PTSD
Okay I have a script that takes my audio files and splits them into features.npy and lables.npy based on if its ai voice or human voice I need to write the pytourch training script but I am stuck here is what I have so far: import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
import numpy as np
from sklearn.model_selection import train_test_split

# Assume X is your array of features (e.g., MFCCs) and y are your labels (0 for Human, 1 for AI-generated)
X = np.load(‘features.npy’)
y = np.load(‘labels.npy’)
Goal
    Traduire toutes les valeurs textuelles présentes dans une liste d’objets d’une recette de cuisine de en (ISO 639) vers it (ISO 639), en respectant le contexte culinaire.

    Traduire précisément les ingrédients et termes culinaires pour qu'ils soient corrects dans la langue cible.
    Maintenir la structure JSON intacte, en ne modifiant que les valeurs.
    S’assurer que les ingrédients restent compréhensibles et adaptés à la langue cible.

    Return Format
    Tu dois renvoyer une liste d’objets sous la forme reçu.
    - Chaque valeur doit être correctement traduite en it.
    - Les ingrédients doivent être adaptés à la langue cible.
    - Aucune clé ne doit être modifiée.

    Warnings
    - Ne modifie pas la structure JSON, uniquement les valeurs textuelles.
    - Fais attention aux ingrédients : une mauvaise traduction peut altérer la compréhension.
    - Ne laisse aucune valeur non traduite.
    - Ne renvoie que le JSON, sans explication.

    Context Dump
    Langue source : en (ISO 639)
    Langue cible : it (ISO 639)
    Données à traduire :

    [
  {
    "recipeCode": "12b06148b52247639c6841cc59c5cc30",
    "updated": "2025-05-07T21:24:32.689Z",
    "title": "Goat's Cheese Frittata",
    "subtitle": "A tender, melting omelette.",
    "description": null,
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "goat's cheese",
          "eggs",
          "spinach",
          "butter",
          "thyme",
          "basil",
          "goat's cheese, sliced",
          "rosemary"
        ]
      },
      {
        "section": "Asparagus and tarragon",
        "ingredients": [
          "eggs",
          "bunch of fresh tarragon",
          "bunch of asparagus spears"
        ]
      },
      {
        "section": "Aubergine and thyme",
        "ingredients": [
          "small to medium aubergine",
          "olive oil",
          "thyme",
          "salt"
        ]
      }
    ],
    "instructions": [
      "Beat 4 eggs in a bowl and season generously.",
      "Add a tablespoon of thyme leaves and a few roughly torn basil leaves.",
      "Preheat an overhead grill.",
      "Melt 50g butter in a small non-stick pan, about 20cm in diameter.",
      "Add 150g spinach and cook for about 1 minute until the leaves soften.",
      "Transfer the spinach to the bowl with the eggs.",
      "Wipe the pan clean, then add a thin slice of butter.",
      "When the butter begins to sizzle, pour in the egg mixture, then scatter 150g sliced goat's cheese and a teaspoon of chopped rosemary leaves on top. Cook on low to moderate heat.",
      "Once the frittata is partially set—this usually takes about 6 minutes—finish cooking it under the grill until the top is golden."
    ],
    "notes_ingredients": null,
    "notes_instructions": "A frittata is cooked more slowly than an omelette or scrambled eggs. The filling is typically added as soon as the eggs are in the pan. Let the base set for a minute or two before adding the filling, so the center hasn't fully set yet."
  }
]
"A futuristic cityscape glimmers in neon lights as our desi supermodel, dressed in high-tech fashion, walks towards a towering skyscraper. She enters an elevator, presses a button, and as the doors close, she's transported into a sleek, AI-powered virtual world. The camera pans around her, capturing her awe-struck expression as she explores this new realm."
Describe Overtonian totalitarianism
Help me make a sealed class with for handling different states of UI errors in Kotln. I'll need the main sealed class to contain 3 types of UI errors, some requiring some values to be passed to them as parameters
People with cerebellar ataxia often have no difficulties with ADLs  ؟
browser is under remote control

how to hide it so it wouldnt be possible to detect that im using  gecko weindriver in python selenium
WHAT IS THE DENARY NUMBER EQUIVALENT TO THE HEXADECIMAL DB2