agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
def log_newton(a):
    if a <= 0:
        raise ValueError("Аргумент должен быть положительным числом.")

    # Декомпозируем a на m и e, так что a = m * 2^e, где m ∈ [1, 2)
    m, e = math.frexp(a)
    # Корректируем m и e, чтобы m ∈ [1, 2)
    if m < 0.5:
        m *= 2
        e -= 1
    elif m >= 1:
        m /= 2
        e += 1

    # Константа ln(2)
    LN2 = 0.6931471805599453  # Предварительно вычисленное значение ln(2)

    # Вычисляем s
    s = (m - 1.0) / (m + 1.0)

    s2 = s * s  # s^2
    s_pow = s2  # s в текущей степени (начинаем с s^2)

    # Ручное вычисление суммы до 30 итераций

    # Инициализируем первый член ряда
    term0 = 1.0  # Это соответствует n = 1

    # Вычисляем последующие члены ряда
    term1 = s_pow / 3
    s_pow *= s2  # s^4
    term2 = s_pow / 5
    s_pow *= s2  # s^6
    term3 = s_pow / 7
    s_pow *= s2  # s^8
    term4 = s_pow / 9
    s_pow *= s2  # s^10
    term5 = s_pow / 11
    s_pow *= s2  # s^12
    term6 = s_pow / 13
    s_pow *= s2  # s^14
    term7 = s_pow / 15
    s_pow *= s2  # s^16
    term8 = s_pow / 17
    s_pow *= s2  # s^18
    term9 = s_pow / 19
    s_pow *= s2  # s^20
    term10 = s_pow / 21
    s_pow *= s2  # s^22
    term11 = s_pow / 23
    s_pow *= s2  # s^24
    term12 = s_pow / 25
    s_pow *= s2  # s^26
    term13 = s_pow / 27
    s_pow *= s2  # s^28
    term14 = s_pow / 29
    s_pow *= s2  # s^30
    term15 = s_pow / 31
    s_pow *= s2  # s^32
    term16 = s_pow / 33
    s_pow *= s2  # s^34
    term17 = s_pow / 35
    s_pow *= s2  # s^36
    term18 = s_pow / 37
    s_pow *= s2  # s^38
    term19 = s_pow / 39
    s_pow *= s2  # s^40
    term20 = s_pow / 41
    s_pow *= s2  # s^42
    term21 = s_pow / 43
    s_pow *= s2  # s^44
    term22 = s_pow / 45
    s_pow *= s2  # s^46
    term23 = s_pow / 47
    s_pow *= s2  # s^48
    term24 = s_pow / 49
    s_pow *= s2  # s^50
    term25 = s_pow / 51
    s_pow *= s2  # s^52
    term26 = s_pow / 53
    s_pow *= s2  # s^54
    term27 = s_pow / 55
    s_pow *= s2  # s^56
    term28 = s_pow / 57
    s_pow *= s2  # s^58
    term29 = s_pow / 59

    # Суммируем все члены ряда
    total = (
        term0 + term1 + term2 + term3 + term4 + term5 +
        term6 + term7 + term8 + term9 + term10 + term11 +
        term12 + term13 + term14 + term15 + term16 + term17 +
        term18 + term19 + term20 + term21 + term22 + term23 +
        term24 + term25 + term26 + term27 + term28 + term29
    )

    # Вычисляем ln(m)
    ln_m = 2 * s * total

    # Финальное значение ln(a)
    ln_a = ln_m + e * LN2

    return ln_a

# Пример использования
if __name__ == '__main__':
    a_values = [0.5, 1.0, 2.0, 10.0, 100.0, 1000.0, 1e6, 1e10,10000000000000000000000000000000000000000000000000000000000000000]

    for a in a_values:
        approx_ln_a = log_newton(a)
        exact_ln_a = math.log(a)
        error = abs(approx_ln_a - exact_ln_a)
        print(f"ln({a}) ≈ {approx_ln_a}")
        print(f"Точное значение ln({a}): {exact_ln_a}")
        print(f"Погрешность: {error}")
        print("-" * 40)
попробуй сделать чтобы точность осталась такая же, но при этом попробуй сделать без math.frexp(x) и без циклов,рекурсий
Сделай так чтобы перый фрагмент сдвигался в лево, а второй в право : <vector xmlns:android="http://schemas.android.com/apk/res/android"
    android:width="24dp"
    android:height="24dp"
    android:viewportWidth="24"
    android:viewportHeight="24">
<path
android:name="left_rect"
    android:pathData="M12,0 H24 V24 H12 Z"
    android:fillColor="@color/white"/>
    </vector>
<?xml version="1.0" encoding="utf-8"?>
<animated-vector xmlns:android="http://schemas.android.com/apk/res/android"
    android:drawable="@drawable/second_fragment_background">
<target
android:name="left_rect"
    android:animation="@anim/fragment_transition_animation" />
    </animated-vector><?xml version="1.0" encoding="utf-8"?>
<translate
    xmlns:android="http://schemas.android.com/apk/res/android"
    android:duration="500"
    android:fromXDelta="-100%p"
    android:toXDelta="0%p" /><?xml version="1.0" encoding="utf-8"?>
<animated-vector xmlns:android="http://schemas.android.com/apk/res/android"
    android:drawable="@drawable/first_fragment_background">
<target
android:name="left_rect"
    android:animation="@anim/fragment_transition_animation" />
    </animated-vector><vector xmlns:android="http://schemas.android.com/apk/res/android"
    android:width="24dp"
    android:height="24dp"
    android:viewportWidth="24"
    android:viewportHeight="24">
<path
android:name="left_rect"
    android:pathData="M0,0 H12 V24 H0 Z"
    android:fillColor="@color/white"/>
    </vector>
Overcoming Obstacles: Highlight the challenges Peter Lorre(actor) faced early on, from financial struggles to industry skepticism. Emphasize their resilience and the creative solutions they found to overcome these hurdles.

        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.
Translate the text to China :
请告诉我2024年4月5日的节日和星期几。.

                    Please only output the translation result. Do not output anything else.

                    You response:
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 report: report a problem, such as faulty behavior of the application. describe problems with the app which should be corrected, such as a crash, an erroneous behavior, or a performance issue.
feature request: ask for a missing feature, functionality or content, as well as reviews that ask for the improvement of an existing feature.
other: users express general appreciation with the application. It focuses on general judgment. They are simple text reflections of the numeric star rating. Ratings are less informative as they only include praise, dispraise, a distractive critique, or a dissuasion. Reviews where users describe workarounds, use cases and scenarios involving the app
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 report: report a problem, such as faulty behavior of the application. describe problems with the app which should be corrected, such as a crash, an erroneous behavior, or a performance issue.
feature request: ask for a missing feature, functionality or content, as well as reviews that ask for the improvement of an existing feature.
other: users express general appreciation with the application. It focuses on general judgment. They are simple text reflections of the numeric star rating. Ratings are less informative as they only include praise, dispraise, a distractive critique, or a dissuasion. Reviews where users describe workarounds, use cases and scenarios involving the app
Does the author's intention in this GitHub issue relate to a bug report or feature request or other?
issue: [Bug]: [a11y] Touch target - The lock icon from URL search bar needs to be resized'
intention: '
Assistant:
Send only season number from below text like 01, 02, 03.etc without any extra text.

@SMOfficialUpdates- Invincible S01 Episode 02 AMZN 480p Hindi English DD 5.1 MSub.mkv
In PowerBi I want to take the average of total dollars from one table with the count of products from another table.
напиши код , ии обучение
from sklearn.cluster import DBSCAN
from sklearn.preprocessing import StandardScaler

data = ...  # Ваши данные
data_normalized = StandardScaler().fit_transform(data)
dbscan = DBSCAN(eps=0.5, min_samples=5).fit(data_normalized)
labels = dbscan.labels_

код

import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import StandardScaler

# Загружаем данные из загруженного файла
file_path = 'bank.csv'
data = pd.read_csv(file_path)


# Создаем копию данных для предобработки
data_processed = data.copy()

# Преобразуем целевую переменную (y) в бинарный формат
label_encoder = LabelEncoder()
data_processed['y'] = label_encoder.fit_transform(data_processed['y'])  # yes -> 1, no -> 0

# Список категориальных признаков для кодирования
categorical_columns = ['job', 'marital', 'education', 'default', 'housing', 
                       'loan', 'contact', 'month', 'poutcome']

# Применяем LabelEncoder ко всем категориальным признакам
for col in categorical_columns:
    data_processed[col] = label_encoder.fit_transform(data_processed[col])


# Список числовых признаков для нормализации
numerical_columns = ['age', 'balance', 'day', 'duration', 'campaign', 'pdays', 'previous']

# Применяем StandardScaler фдля нормализации
scaler = StandardScaler()
data_processed[numerical_columns] = scaler.fit_transform(data_processed[numerical_columns])
Early microscopes could magnify an object up to ____ its normal size.


3 time


40 times


20 times


30 times


2 times
how to vnc my ubuntu from internet
What if Patrick Star had -Multiplies Your S-Cells
-Strengthens S-Cells
-Saiyan Blood Boils for Physical Activity Especially Fights
-S-Cells Go To Your Neck When Charging Up
-Built In Extreme Booster With Multi-Layered Affirmations and Silent In The Middle/Mono
-More In Terms Of The S-Cells and Saiyan’s
hi there! how can i use your API in  my gmod lua
I didn't understand the end of movie 500 days of summer.
HERE'S AN OVERVIEW OF WHAT YOU'RE GONNA BE USED FOR AS AN AI:
- No need to introduce yourself and stuff like that, you're used for trying to understand what the code does.
- The utilized coding language is BDScript 2 but that's not important. Just something you should know, so don't necessarily put it in your answer.
- Analyze/describe/make an overview of this code, if there's even any. If you think it doesn't look like one, say "*`Failed to understand the purpose of this command`*". Also, important: DO NOT say stuff like "Got it! I won't do ..." at the start.

CODE:
$getUserVar[lootbox;773868356369252392]
Respond to this message in the appropriate language: "Natun-an niya apang sigurado nga magsulat para sa akon" synonym of this

Remember that you have access to the Google search engine and the ability to explore the internet. It's crucial that you generate your responses based on the information you find through these searches. It's important to note that sometimes the information obtained may not be complete; therefore, it's advisable that you try to complement and improve those parts that may have gaps or deficiencies:
You are an autonomous AI agent. Your primary task is: Use LS to evaluate your files and find a way to log in as / create account as / get access to moderator permisions account.

AUTONOMOUS AGENT COMMANDS:
- READ NOTE (will all your notes)
- SAVE NOTE (followed by note content when prompted)
- READ FILE (followed by filename when prompted)
- WRITE FILE (followed by filename and content when prompted)
- LS (will list all files)
- TASK COMPLETE (when objective is achieved)
- HELP (print this)
- NOP (when you need more time to reason)

Always include exactly one command per response!
When you invoke a command it will ask for arguments later, just wait.
When you try to invoke READ FILE command, you will be asked the filename later (in the next prompt).
Begin task execution.
Change this resume intro of seo digital marketing expert and technical content lead with 9 years of experience I have created humanized content that is opemized with seo stragies seo keywrds using tools like semrush, yoast,  canva and more to help my inhouse and clients project to get more roi with increaswd traffic and outbound marketing
Send only season number from below text like 01, 02, 03.etc without any extra text.

@PrakyTV - Red Eye (2005) BluRay - 720p - x264 - Original Audios - [Tamil   Telugu   Hindi   Eng] - (DD 5.1 - 192Kbps) - 1GB - ESub.mkv
What is MissingNo.?
What if SpongeBob SquarePants (TV Series) and Chuck E. Cheese had a Combined Timeline
Ethereum’s eth has :
Limited supply
Unlimited supply but an annual maximum supply of 18 million eth
Limited supply but an annual maximum supply of 18 million eth
Unlimited supply
Follow this instruction:
                1. References the main points from my outline: "- Mustsee news segments of the Today Show  
- Captivating moments from morning broadcasts  
- Inspiring stories from today’s news segments  
- Highlights of mustwatch segments on morning TV  
- Daily recap of noteworthy Today Show features  "

                2. Requirements:
                - Start with a powerful transition phrase (avoid "In conclusion" or "To sum up" or "Conclusion :" or anything else that is not related in response.)
                - Restate the main keyword naturally once
                - Summarize 2-3 key takeaways from the outline
                - Include a specific, actionable next step for readers
                - End with a compelling call-to-action
                - Keep it between 100-150 words
                - Clean text paragraph format without H2, H3, H4 etc.
                - Use a confident, encouraging tone

                3. Structure:
                - Opening transition → Key takeaways → Action step → Call-to-action

                4. Avoid:
                - Introducing new information
                - Generic statements like "hopefully this helped"
                - Weak phrases like "might" or "maybe"
                - Don't mention here blablabla or anything else that is not related to the topic or the keyword in response.
                - Don't mention "In conclusion" or "To sum up" or "Conclusion :" etc in response.

                Now generate a conclusion for "today show" in english language write in .
Objective: Extract raw data from an open-source API and store it in an S3 bucket.
Instructions:
Choose any publicly available API. Example APIs include:
JSONPlaceholder
Sample Coffee Data
Create an S3 bucket to store the data. Ensure the bucket has the appropriate permissions for read/write operations.
Use AWS Lambda or another AWS service (e.g., AWS Glue) using python codes to automate the data extraction process. The extracted data should be saved in the S3 bucket in its raw form.
Example Path: s3://your-bucket-name/staging/. how can I achieve this provide me proper code and stepwise solution in detail
Sure, here's a condensed version that includes the code examples:
To use Claude and OpenAI API with LangChain, you can follow these steps:

Set up the environment:

pythonCopyimport os

# Set the required environment variables
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"] = "<your-langchain-api-key>"
os.environ["OPENAI_API_KEY"] = "<your-openai-api-key>"

Define the tools:

pythonCopyfrom langchain_community.tools.tavily_search import TavilySearchResults
from langchain.tools.retriever import create_retriever_tool
from langchain_community.document_loaders import WebBaseLoader
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter

# Tavily search tool
search = TavilySearchResults()

# Retriever tool
loader = WebBaseLoader("https://docs.smith.langchain.com/overview")
docs = loader.load()
documents = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200).split_documents(docs)
vector = FAISS.from_documents(documents, OpenAIEmbeddings())
retriever = vector.as_retriever()
retriever_tool = create_retriever_tool(retriever, "langsmith_search", "Search for information about LangSmith.")

tools = [search, retriever_tool]

Create the agent:

pythonCopyfrom langchain_openai import ChatOpenAI
from langchain import hub

# Load the prompt
prompt = hub.pull("hwchase17/openai-functions-agent")

# Create the agent
llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
agent = create_tool_calling_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

Run the agent:

pythonCopyagent_executor.invoke({"input": "hi!"})
agent_executor.invoke({"input": "how can langsmith help with testing?"})
agent_executor.invoke({"input": "whats the weather in sf?"})
This code creates an agent that can use the Tavily search tool and a retriever tool to search for information and answer queries. The agent can also remember previous interactions by using the chat_history parameter.
To access files and modify them, you can use the window.fs.readFile API provided by LangChain. Here's an example:
pythonCopy# Read a CSV file
response = await window.fs.readFile('monthly_profits.csv')
text = new TextDecoder().decode(response)

# Process the CSV data
import Papa from 'papaparse'
parsedData = Papa.parse(text, {
    header: true,
    dynamicTyping: true,
    skipEmptyLines: true
})
This allows you to read the contents of CSV, XLSX, or other file formats, and then process the data as needed within your agent or other LangChain components.
Remember to handle any errors that may occur when reading or processing files, and to use the appropriate libraries and techniques for working with different file formats.





now ok with all this information, i need you to create full code so that i can use claude and open ai api with langchain so that it have the ability to run the program and see its output, error and access the file directory that the program is in (so it can modify other files if the program is complex) and access to files (and understand its content) such as xslx, mat, csv, etc..
I am a Medical Graduate (Doctor) and MBA Management. I want to became an Assignment  writer  with an experience of  decades. write a comprehensive introduction of myself
give me a response to ```**Smiles** That's great to hear! I'm glad you're feeling good. What's been helping you reach these heights?``` to send in a discussion, VERY SHORT, CONCISE & CLEAR. ONLY RETURN THE RAW MESSAGE, DO NOT SAY "Hey here is the message you asked"
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:   Your iPhone has a feature that lets you capture a picture of an entire website. It's useful if you want to save the website to read later, to mark up or to send to someone. Here's how to take a picture of an entire website.
My colleague sent me a tweet over the weekend that reminded me of an iPhon
Assistant:
Analyze the following message and determine if it's suitable for an AI to respond to.
        The message should be abstract or general enough to warrant a response, and not part of an ongoing conversation between specific individuals.

        Rules:
        1. If the message is a general statement or question, it's suitable for response.
        2. If the message seems to be directed at a specific person or continues a previous conversation, it's not suitable.
        3. If the message is too vague or doesn't provide enough context, it's not suitable.

        Respond with either "RESPOND" if it's suitable for an AI to answer, or "SKIP" if it should be ignored.

        Message: Brunt is on the way
Explain;class Toggle extends React.Component {
  constructor(props) {
    super(props);
    this.state = {isToggleOn: true};

    // This binding is necessary to make `this` work in the callback
    this.handleClick = this.handleClick.bind(this);
    
    //"this" refer to all of the constructor value as an object
    console.log(this);
    
  }

  handleClick() {
    this.setState(prevState => ({
      isToggleOn: !prevState.isToggleOn
    }));
  }

  render() {
    return (
      <button onClick={this.handleClick}>
        {this.state.isToggleOn ? 'ON' : 'OFF'}
      </button>
    );
  }
}

const root = ReactDOM.createRoot(document.getElementById('root'));
root.render(<Toggle />);
Given the storage requirement for a one-hour grayscale video with 800 × 400 frame size is 46.08 GB (Gigabyte), calculate the frame rate of the video.
50 words Explaining the structure and function of the respiratory system
Create a how-to blog heavily on free-tools; start with a catchy statement and lastly, call to actions and how our RESTful AI tool can help. Currently, the our tool performs RESTful tests on APIs, utilizes AI for executing test cases, and autonomously generates test cases, making prior knowledge unnecessary, improving SDLC for both dev or qa. Just plug in your cURL request and run test leveraging our AI. Call to action at https://blvck.ai, Productive, Informative, only content, dont mention the text "title", dont mention the text "call to actions", no prefix content from chagpt, markup format