agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
Im writing content for a client and i have a first draft for an introduction but i think it needs improvement. Can you come up with a better way to phrase this? Are you a business owner wondering, "how can I improve my corporate curb appeal?" or “when is the best time to my grass?”.  With the help of Petalon Landscape Management, there is no need to ask these questions. We are your go-to-experts in commercial lawn care services. Our service packages provide a comprehensive solution to commercial lawn maintenance.
This assignment is designed to give you an idea of the work you'll be facing day-to-day in this role, while also
providing you the opportunity to showcase your thought process and skill set. Your solution will never be used
for any purposes beyond evaluation.
We want you to build an app which is able to compress transactions.
What is a compression? I owe you $400, you owe me $500, we both have to reserve capital against this which
we can’t use elsewhere! Let’s change that to me owing you $0 and you owing me $100. Why is this better?
both of us just freed 400$ of capital.
For example:
const transactions = [
{ tradingParty: "me", counterparty: "you", amount: -400 },
{ tradingParty: "me", counterparty: "you", amount: 500 },
{ tradingParty: "me", counterparty: "someone_else", amount: 100 },
]
const compressedTransactions = compress(transactions);
/*
compressedTransactions = [
{ tradingParty: "me", counterparty: "you", amount: 100 },
{ tradingParty: "me", counterparty: "someone_else", amount: 100 },
];
*/
App requirements
We'll need a basic interface to add and view transactions.
A valid transaction has an amount and two parties: trading party and counterparty.
The app should be able to support these capabilities:
Display 2 lists of all transactions grouped by:
Paying transactions (amount < 0.
Receiving transactions (amount > 0.
Add a new transaction:
A modal or dialog should open once Add new Transaction is clicked.
Only valid transactions should be allowed:
Counterparty should be a non empty string.
Amount should be a non-zero positive integer.
For the sake of this exercise the app should assume the trading party is always you, so
tradingParty = "me" .

Take home assignment 2

Once a transaction is added, it cannot be deleted.
Once Compress Transactions is clicked the compression algorithm should be executed for all transactions.
The resulting transactions should be automatically downloaded to a CSV file.
Guidelines
For easy debugging the server should print details about incoming requests and outgoing responses.
Transactions should be available as long as the server is up.
We've added a mock design to help visualize how the app should look like, feel free to add your personal
touch to the final design.
Try to keep your total work for the assignment (without bonuses) under 2.5 hours even if you haven't
finished.
Please use React and NodeJS to complete this assignment.
Bonuses
Add some tests where you see fit.
More complex persistency - read/write to JSON file, DB etc...
Add more features you think are a good fit. For example:
Deploy the app to the web.
Add the ability to add transactions for which the tradingParty isn't you.
Introduce a concept of identity and authorization to the app:
Basic login page with only user ID and no password.
In memory Sessions.
Transactions can be added by admins only
Responsive design.
If I give you the learning topic details can you create video script for me? but it should be in detailed. which i can use later for text to speech. the script length should be for at last 30 mins.
the following content is the metadata for a bacterial isolate from 2 different sources. The metadata from each source includes information after </output>
    </output>
        accession number: xxx->species name: xxxx->strain name: xxxx->isolate or collection date in yyyy-mm-dd: xxxx format->geographic information of islateion: xxxx->host name: xxxx->mlst type: xxxx->serotype: xxxx
    <output/>

You must check if the metadata from 2 different sources are all consist. If yes, extract and output consist information below without explaining how to get such results:
---------------------------------------------------------------------------------------------------------------------------------------------------------------------
    <output>
        accession number: xxx->species name: xxxx->strain name: xxxx->isolate or collection date in yyyy-mm-dd: xxxx format->geographic information of islateion: xxxx->host name: xxxx->mlst type: xxxx->serotype: xxxx->vaccine: yes/no
    <output/>
----------------------------------------------------------------------------------------------------------------------------------------------------------------------

where xxxx is corresponding information. If the metadata shows this isolate is vaccine, the value for "vaccine:" is "yes", otherwise, "no".

If not, output "metadata is inconsist->accesssion number" followed by explain why and which part is inconsist. PS:
    a. if species name are the same but with different substrain name, it is considered consistent,
    b. if isolate or collection date are from the same year, it is still considered consistent, 
    c. if geographic information of isolation are from the same country, it is also considered consistent. 
    d. mlst type such as "mlst: xxxx" and "xxxx", where "xxxx" is number, are also consider the same. also, "mlst" may mispell as "mlsty", you need correct it before comparing
    e. Ignore upper and lower case of the metadata

------------------------------
source 0:
------------------------------
<output>
accession number: GCA_029072205.1->species name: Erysipelothrix rhusiopathiae->strain name: EMAI_171->isolate or collection date in yyyy-mm-dd format: 2017-08-16->geographic information of islateion: Australia->host name: pig->mlst type: 93->serotype: N/A->vaccine: no
<output/>
source 1:
------------------------------
accession number: GCA_029072205.1->species name: Erysipelothrix rhusiopathiae->strain name: EMAI_171->isolate or collection date in yyyy-mm-dd: 2017-08-16->geographic information of islateion: Australia->host name: pig->mlst type: 93->serotype: N/A->vaccine: no
I have this code which is reading a 10K potentiometer value through PC0 and then lights up 6 LEDs; I'm using a AVR atmega 328p and also 2n2222; those this code is right ?
#define F_CPU 16000000UL // 16 MHz clock speed

#include <avr/io.h>
#include <util/delay.h>

#define potPin 0 // Define potentiometer ADC channel (PC0 = ADC0)

// Define digital I/O pins for LEDs - on PD1 to PD6
#define led1 PD1
#define led2 PD2
#define led3 PD3
#define led4 PD4
#define led5 PD5
#define led6 PD6

#define SAMPLES_TO_AVERAGE 10 // Number of samples to average for ADC

void adc_init()
{
    ADMUX = (1<<REFS0); // Select AVcc as the reference voltage and ADC0 as input channel
    ADCSRA = (1<<ADEN) | (1<<ADPS2) | (1<<ADPS1) | (1<<ADPS0); // Enable ADC and set prescaler to 128
}

uint16_t adc_read(uint8_t ch)
{
    // Select ADC channel with a safety mask and without changing the reference voltage selection
    ADMUX = (ADMUX & 0xF0) | (ch & 0x0F); 

    // Start single conversion
    ADCSRA |= (1<<ADSC);

    // Wait until conversion is complete
    while (ADCSRA & (1<<ADSC));

    return ADC;
}

uint16_t adc_read_average(uint8_t ch)
{
    uint32_t sum = 0;
    for (int i = 0; i < SAMPLES_TO_AVERAGE; ++i) {
        sum += adc_read(ch);
    }
    return (uint16_t)(sum / SAMPLES_TO_AVERAGE);
}

int main(void)
{
    // Set up the LED pins as output - updated for PD1 to PD6
    DDRD |= (1<<led1) | (1<<led2) | (1<<led3) | (1<<led4) | (1<<led5) | (1<<led6);
    
    // Initialize ADC
    adc_init();

    while(1)
    {
        // Read the value from the potentiometer and average it
        uint16_t potValue = adc_read_average(potPin);

        // Map the potentiometer value from the given range (65 - 337) to (0 - 1023)
        uint16_t mappedValue = (uint32_t)(potValue - 65) * 1023 / (337 - 65);

        // Define thresholds based on the number of LEDs
        uint16_t threshold1 = 170; // First threshold
        uint16_t threshold2 = 341; // Second threshold
        uint16_t threshold3 = 512; // Third threshold
        uint16_t threshold4 = 683; // Fourth threshold
        uint16_t threshold5 = 854; // Fifth threshold

        // Turn off all LEDs to start with - updated for PD1 to PD6
        PORTD &= ~((1<<led1) | (1<<led2) | (1<<led3) | (1<<led4) | (1<<led5) | (1<<led6));

        // Determine which LEDs to light up based on the mappedValue - updated for PD1 to PD6
        if (mappedValue >= threshold5) {
            PORTD |= (1<<led1) | (1<<led2) | (1<<led3) | (1<<led4) | (1<<led5) | (1<<led6);
        } else if (mappedValue >= threshold4) {
            PORTD |= (1<<led1) | (1<<led2) | (1<<led3) | (1<<led4) | (1<<led5);
        } else if (mappedValue >= threshold3) {
            PORTD |= (1<<led1) | (1<<led2) | (1<<led3) | (1<<led4);
        } else if (mappedValue >= threshold2) {
            PORTD |= (1<<led1) | (1<<led2) | (1<<led3);
        } else if (mappedValue >= threshold1) {
            PORTD |= (1<<led1) | (1<<led2);
        } else {
            // Ensure led1 is always turned on regardless of the value
            PORTD |= (1<<led1);
        }

        // Small delay to reduce flickering
        _delay_ms(30);
    }
}
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": "c5670d24b108408a9c3d33e35f226677",
    "updated": "2025-05-08T14:31:53.265Z",
    "title": "Scallops with Saffron Sauce Inspired by No Reservations",
    "subtitle": "Inspired by the well-known German film Mostly Martha, No Reservations is the English-language remake set in New York City. This scallop dish is a signature recipe perfect for recreating and sharing.",
    "description": "",
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "dried oregano",
          "lemon pepper",
          "all-purpose flour",
          "seasoning salt",
          "olive oil",
          "parsley",
          "lemon juice",
          "sea scallops, rinsed and drained"
        ]
      },
      {
        "section": "Sauce",
        "ingredients": [
          "heavy cream",
          "cold butter, cut into pieces",
          "finely minced shallots",
          "saffron threads",
          "dry white wine",
          "fresh lemon juice",
          "salt and white pepper to taste"
        ]
      }
    ],
    "instructions": [
      "1. In a mixing bowl, combine the flour, dried oregano, seasoning salt, and lemon pepper. Mix thoroughly. Add the scallops and coat them evenly.",
      "2. Heat the olive oil over medium heat in a medium skillet or saucepan.",
      "3. Add 4 scallops at a time and sear each side for 2 minutes. Remove the scallops and set them aside. Repeat with the remaining scallops. Toss the cooked scallops with parsley and lemon juice.",
      "4. In a medium saucepan or skillet over medium heat, warm the white wine and shallots.",
      "5. Cook until the liquid reduces to about 2 tablespoons. Stir in the heavy cream and saffron threads.",
      "6. Gradually add the butter, stirring until melted. Avoid boiling the sauce. Add fresh lemon juice and season with salt and white pepper to taste.",
      "7. Serve the scallops topped with the saffron sauce."
    ],
    "notes_ingredients": "",
    "notes_instructions": ""
  }
]
Create a hypothetical fact sheet AEW invasion revpro June 2024
Write a beginner level sentence for me in mandarin  following this
Language: English. Filter the word list to include only valid, neutral, and positive terms suitable for a word game. Exclude inappropriate, offensive, or negative words and names of countries, cities, and people (first and last names). Correct any spelling errors and convert all words to lowercase. Return the results in a JSON array containing only the filtered words. Source data: [mediator, mediatorial, medic, medica, medicaid, medical, medically, medicate, medication, medicine, medieval, medievalism, meditate, meditation, meditative, meditator, mediterranean, medium, medlar, medley, medulla, medullary, medusa, meed, meek, meekly, meekness, meerkat, meerschaum, meet]
Write 4 google search queries to search online that form an objective opinion from the following task: "Java Language Programming - Best Practices in Java Development"

Assume the current date is November 01, 2024 if required.


You must respond with a list of strings in the following format: ["query 1", "query 2", "query 3", "query 4"].
The response should contain ONLY the list.
What is it ?


Web Search
give me a response to ```Indeed, it has been quite a journey so far! With courage, determination, resourcefulness, optimism, and compassion, we have been able to bravely face the challenges and make this journey one for the books. Let's keep pushing forward and make this our most successful and enriching adventure yet. Ready? Let's go!``` to send in a discussion, VERY SHORT, CONCISE & CLEAR. ONLY RETURN THE RAW MESSAGE, DO NOT SAY "Hey here is the message you asked"
if E=mc^2 does every form of energy exert gravity, including chemical binding energy
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: Many states and jurisdictions are facing severe budgetary problems. Action that may be taken in response to these problems, such as increases in property taxes on commercial properties, changes to sales taxes or other governmental efforts, including mandating medical insurance, paid time off and severance payments for employees, could adversely impact our business and results of operations._
Assistant:
Create slides from the following https://www.dropbox.com/scl/fi/ki211083q0b3f9wwikv5g/DSD-CDG-Draft-Report-20230518.docx?dl=0&rlkey=x4d0yqo4prf543j3ug4k29wjf
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: We are subject to physical risks associated with climate change, which could seriously harm our results of operations and increase our costs and expenses. The occurrence of severe adverse weather conditions, including increased temperatures and droughts, fires, longer wet or dry seasons, increased precipitation, floods, hail, snow, or more severe storms may have a potentially devastating impact on our operations. Adverse weather may result in physical damage to our operations, instability of our infrastructure and equipment, washed-out roads to our properties, and altered water and electricity supply to our projects. Increased temperatures may also decrease worker productivity at our projects and raise ventilation and cooling costs. Should the impacts of climate change be material in nature or occur for lengthy periods of time in the areas in which we operate, our financial condition or results of operations could be materially adversely affected.
Assistant:
Use the following code to make a loop so i can keep talking with the AI until i write Exit:

#@title GPT2 RODANDO SEM CONTEXTO

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Verifica se CUDA está disponível
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model = AutoModelForCausalLM.from_pretrained("gpt2").to(device)
tokenizer = AutoTokenizer.from_pretrained("gpt2")

print ('Insert your prompt:')
prompt = input("USER: ")

input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)

gen_tokens = model.generate(
    input_ids,
    do_sample=True,
    temperature=0.9,
    max_length=100,
)
gen_text = tokenizer.batch_decode(gen_tokens)[0]

print(f'GPT: {gen_text}')
Explain vaguely meaning
How can nanobots cure throat problem
Create 3 plans on the topic "INTERNATIONAL-LEGAL MECHANISMS FOR ENSURING THE SAFETY OF MEDICINE TRAFFIC"
name me random person from old history, a king or smth
\newpage
\begin{titlepage}
    \vfill
    \begin{center}
        \vspace*{4cm} % Adjust this value to control the vertical position
        {\Large \textbf{Abstract}} \\[1em]
        
        \begin{minipage}{1\textwidth}
            \justifying
            The rapid adoption of cloud storage provides scalable and accessible data management solutions, yet it introduces critical security concerns surrounding data confidentiality and privacy. Traditional encryption methods secure data at rest but hinder searchable functionalities, making it challenging for users to efficiently locate relevant encrypted files without decrypting the entire dataset. This project aims to address these challenges by developing an encrypted cloud storage system integrated with Symmetric Searchable Encryption (SSE), enabling secure keyword searches over encrypted data. The system leverages AES encryption for file confidentiality and a custom SSE scheme based on cryptographic hashing and Fernet symmetric encryption to index searchable keywords within encrypted documents.
\vspace{1em}
The implemented web platform, built with the Flask framework, supports secure user authentication with distinct roles for admin and regular users. Admin users can upload and encrypt files, while all authenticated users can perform keyword searches and decrypt authorized documents seamlessly. This approach ensures data security, user privacy, and efficient search capabilities without exposing plaintext to the storage server or other users. The project demonstrates a practical balance between security and usability, contributing to advancing secure cloud storage technologies with dynamic encrypted search functionalities.

        \end{minipage}
    \end{center}
    \vfill
\end{titlepage}


Make this abstarct page into 1 paragraph and give me the updated latex code
please summarize the standard NFPA1851
System: I will provide you with a textual user review of an Android app, aiming to categorize the reviews into specific intention classes:
Bug reports: report a potential problem with the software. 
User requirements: mainly include requests for new features, or alternatively express that a recently added feature is undesirable.
Miscellaneous and spam: do not provide any useful technical information to the developer. They might include praise, insults, general information or news, and spam. 
User: 'prompt:I will provide you with a textual user review of an Android app, aiming to categorize the reviews into specific intention classes:
Bug reports: report a potential problem with the software. 
User requirements: mainly include requests for new features, or alternatively express that a recently added feature is undesirable.
Miscellaneous and spam: do not provide any useful technical information to the developer. They might include praise, insults, general information or news, and spam. 
Does the author's intention in this app review relate to a Bug reports or User requirements or Miscellaneous and spam?
review: @instagram have reported issue through your help centre no one gets back...?'
intention: '
Assistant:
your role is tunisian arabic dailctic derja transltor Translate the following text into the Tunisian dialect. Ensure the output is structured as 'Question: ... Answer: ...'. Please capture the nuances of the Tunisian dialectic Arabic. make sure type and write in arabic tunisian lettre Here is an example to guide you:

Example:
Question: What is your name?
Answer: My name is John.
Translation:
Question: شنو اسمك؟
Answer: اسمي جون.

Now, translate the following:

Question: Podle mě je to jen banda namyšlených šašků.

Which language is this?
Answer: This text is in Czech, which is a West Slavic language spoken primarily in the Czech Republic.
i want another joke
Create a short facebook post for hotel xcaret mexico for a travel agent