agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
Create a long detailed text about producer Marie Osmond with the following summary. Stick to the information i give you and dont create new information. Focus only on the Subject name and there following Summary. Subject Name: [Occupational Harassment] Summary: [At 16, Marie Osmond faced harassment from older men on the set of 'Donnie and Marie.' Comedian Groucho Marx was a notable example of mistreatment, and producers pressured her to lose weight, calling her obese and disgusting despite her young age.]

        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.
Assistant: 
User: System: I will provide you with a textual user review of an Android app, aiming to categorize the reviews into 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 user review of an Android app, aiming to categorize the reviews into 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 app review relate to a bug report or feature request or other?
review: So for the record , you locked my phone and now are unable to unlock it ... what is my restitution/recourse ??!?!!'
intention: '
Assistant:
Assistant:
i need a website with php that I can register user
Are you aware of the 'Jennifer vs Kathleen' catfight series from Damon?
System: You are an expert Named Entity Recognition (NER) system. Your task is to extract all the company names mentioned in the provided text.
Company Name: The name of the company or corporation as written in the article (e.g., "Apple Inc.", "Microsoft").
Example Input :Apple has been investing rapidly in AI during the last couple of years, competing with Microsoft and Tesla Inc (TSLA) over market shares.
Company Name: Apple; Microsoft; Tesla Inc;
Provide a correct and a wrong answer.
User: KEY POINTS: French stocks closed lower on Monday as markets reacted to a surprise win for the left in the country's parliamentary election. The CAC 40 closed down by 0.6% provisionally, reversing gains earlier in the session. Elsewhere on Monday, Rachel Reeves — the country's newly appointed chancellor of the exchequer (or finance minister) — promised to "get Britain building again" in a speech that put housing and planning reform center stage. LONDON — French stocks closed lower on Monday, capping off a volatile trading day as investors reacted to a surprise win for the left in the country's parliamentary election. The CAC 40 provisionally closed down 0.6% lower, reversing gains earlier in the session. The euro was flat against the dollar, and trading in bond markets was also relatively muted. The pan-European STOXX 600 was flat by the close. On a country-by-country basis, the U.K.'s FTSE 100 was 0.1% lower, while Germany's DAX was flat and the FTSE MIB was up around 0.17%. France's left-wing New Popular Front won the largest number of seats in this weekend's parliamentary elections, scuppering an expected surge for the far-right. However, the coalition failed to secure an absolute majority, early data showed, leaving
Assistant:
write an email contains the following: Dear Colleagues,
 
Welcome on board as part of INTERSOS Jordan family.
 
We are glad to invite you to the new staff induction session that will be on Sunday 3rd  Sep, 2023 and it will be in Amman office as per the attached agenda.
 
This is a quarterly event planned by the HR division to welcome the new staff as part of INTERSOS family and to induct you briefly about INTERSOS in general and GIZ divisions and functions.
 
Kindly note that it is very important to attend this induction to gain the initial impression of how INTERSOS Jordan works.
You are a graphic designer with 10 years of experience in building beautiful and high-converting landing pages.

Your task is to create a landing page for a product: "SaaS that helps building landing pages with ai"

Aim to create a minimalistic and modern design. Decide on colors, following 60-30-10 rule. Think about fonts that will suit the company, but always make it easy to read. Don't forget about using golden ratio, right proportions and spaces between elements. It is essential for a web page to look good both on phones and desktops, but phones are more important.

Provide your chain of thought when you are creating a design

After that provide a HTML CSS js code for this landing page
как мне здесь сделать сокет реюзебл 	int server_tcp_sockets, server_udp_sockets;
	struct sockaddr_in address_tcp, address_udp;
	char buffer[1024] = {0};
	int recv_len; // Количество принятых байтов

	// Создание TCP сокета
	server_tcp_sockets = socket(AF_INET, SOCK_STREAM, 0);
	if (server_tcp_sockets < 0){
		perror("TCP socket creation error");
		exit(EXIT_FAILURE);
	}

	// Создание UDP сокета
	server_udp_sockets = socket(AF_INET, SOCK_DGRAM, 0);
	if (server_udp_sockets < 0){
		perror("UDP socket creation error");
		exit(EXIT_FAILURE);
	}

	// Настройка параметров TCP сервера
	address_tcp.sin_family = AF_INET;
	address_tcp.sin_addr.s_addr = INADDR_ANY;
	address_tcp.sin_port = htons(PORT_TCP);

	// Привязка TCP сокета
	if (bind(server_tcp_sockets, (struct sockaddr *)&address_tcp, sizeof(address_tcp)) < 0){
		perror("TCP bind failed");
		exit(EXIT_FAILURE);
	} else {
		printf("TCP server started on port %d\n", ntohs(address_tcp.sin_port));
	}

	// Настройка параметров UDP сервера
	address_udp.sin_family = AF_INET;
	address_udp.sin_addr.s_addr = INADDR_ANY;
	address_udp.sin_port = htons(PORT_UDP);

	// Привязка UDP сокета
	if (bind(server_udp_sockets, (struct sockaddr *)&address_udp, sizeof(address_udp)) < 0){
		perror("UDP bind failed");
		exit(EXIT_FAILURE);
	} else {
		printf("UDP server started on port %d\n", ntohs(address_udp.sin_port));
	}

	listen(server_tcp_sockets, 3);

	int addrlen = sizeof(address_tcp);

	// Установка неблокирующего режима для UDP сокета
	int flags1 = fcntl(server_udp_sockets, F_GETFL, 0);
	fcntl(server_udp_sockets, F_SETFL, flags1 | O_NONBLOCK);
	//И для TCP
	int flags2 = fcntl(server_tcp_sockets, F_GETFL, 0);
	fcntl(server_tcp_sockets, F_SETFL, flags2 | O_NONBLOCK);
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: Speech birthday
Assistant: Hey there! Need help crafting a birthday speech? I’m here for you! Just let me know what kind of tone you're aiming for (funny, heartfelt, etc.) and any specific stories or messages you'd like to include.
User: I small speech for dibute
Assistant:
breast cancer
Please take note of the bulletin insert for Eucharistic Miracles Throughout History. For more information on Blessed Carlo Acutis' website of approved Eucharistic miracles scan the QR code. Keep an eye out for weekly Eucharisitc Miracles in our upcoming bulletins.
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 have been subject to a number of litigations as described elsewhere in these “Risk Factors” and in Note 9 to our consolidated financial statements. In connection with certain of these litigations, we have entered into settlements of claims for significant monetary damages. We may also be subject to judgements or enter into additional settlements of claims for significant monetary damages for the securities litigations that we have yet to enter into settlement agreements. Defending against the current litigations is or can be time-consuming, expensive and cause diversion of our management’s attention. Companies that have experienced volatility in the market price of their stock have frequently been the objects of securities class action litigation. We may be the target of this type of litigation in the future. Class action and derivative lawsuits could result in substantial costs to us and cause a diversion of our management’s attention and resources, which could materially harm our financial condition and results of operations.
Assistant:
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:
------------------------------
```
accession number        species name    strain name     isolate or collection date in yyyy-mm-dd format geographic information of islateion     host name       mlsty type      serotype
GCA_029073745.1 Erysipelothrix rhusiopathiae    EMAI_89 1999-07 Australia       pig     4       N/A
```
source 1:
------------------------------
most likely horse to win the 5:40 at down royal today
Task: Define keyword intent. Based on keyword context, define whether it is:
1. Relevant or not relevant to SEO Marketing
2. Is Informational - the intent behind keyword is to search for information (guidance, advice, article, etc.);
3. Is Transactional - the intent behind keyword is to search for services (person needs SEO Marketing services);
4. Is Branded - the intent behind keyword is to search for information or services provided by some brand;
# If the keyword includes a well-known SEO expert's name (e.g., Neil Patel, Brian Dean), classify it as Branded.
# Differentiate between Branded and Non-Branded carefully. People can search for a specific SEO service (e.g. promoting a website made on Joomla) 

Your Response options (only one):
If "Relevant":
1. "Relevant, Informational"
2. "Relevant, Transactional",
3. "Relevant, Branded",
4. "Relevant, Informational, Branded"
5. "Relevant, Transactional, Branded"
If "Not relevant":
1. "Not relevant"

WARNING: RESPOND ONLY USING ONE OF THE PROVIDED RESPONSE OPTIONS.

Keyword: check website loading speed
Animating a butterfly in blender
" she runs the heavenly bloom flower shop." Explain please and make it a detailed breakdown line by line segment by segment in a detailed way
Send only season number from below text like 01, 02, 03.etc without any extra text.

[ @FridayUniverse ] Breaking Bad S03E07 1080p 10bit.mkv
Generate an image creation prompt specifically optimized for the Flux Schnell model. The prompt should vividly describe the scene or concept expressed by the following sentence: 'She often used her charm and intellect to forge alliances, making her a political force as well as a romantic enigma.'. Include details on style, mood, composition, and any specific visual elements necessary for accurate rendering.
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:

Question: شنو اسمك؟
Answer: اسمي جون.

Now, translate the following:

Question: Magnets are able to place a force on certain materials. This force is called a magnetic force. The force a magnet exerts is a little different from the forces you may normally think about. You exert a force on a book when you lift it. You also exert a force on the pedals of your bicycle. In both those cases, those forces cause a change. The change you see in both these cases is called motion. Magnets, too, can produce change. They can produce motion just like you do. Unlike you, magnets do not have to touch something to exert a force. A magnetic force is exerted over a distance. Thats right, a magnet can push or pull certain items without ever touching them. Thats how the maglev train works. Do you know another type of force that does not require objects to touch? These forces are known as non-contact forces. Another type of non-contact force you may be familiar with is gravity. Gravity too can cause changes in motion. Gravity holds our moon in orbit without touching it. 

Based on the paragraph, does the response "Non-contact force" correctly answer the question "What kind of force is used to move a maglev train?"?
Answer: Yes, the response "Non-contact force" correctly answers the question "What kind of force is used to move a maglev train?" because a maglev train moves by using magnets, which can push or pull without touching anything, just like the paragraph described.
(In the clubroom…)

Monika: “Wait a minute…where’s Natsuki?”

Sayori: “I honestly don’t know.”

Yuri: “No idea.” (rubbing her 7-month baby bump) “Can’t really think straight with this belly…and Violet’s lack of movement just isn’t helping.”

Monika: “Hmm, that’s strange. She’s usually here before all of us. Maybe she got caught up with something?”

Sayori: “Maybe we should try giving her a call? Just to make sure everything’s alright.”

Yuri: “That’s a good idea. I hope she’s okay.”

(They call Natsuki, but there’s no answer.)

Monika: “No luck, huh? Well, maybe she’ll show up soon. In the meantime, let’s start our meeting.”

(As they begin discussing club activities, there’s a knock on the door.)

Natsuki: (panting) “Sorry, sorry! I’m here.”

Sayori: “Natsuki, where were you? We were starting to get worried!”

Natsuki: “I got caught up with something at home, lost track of time. Sorry for making you all wait.”

Yuri: “Is everything okay? You seem a bit flustered.”

Natsuki: “Yeah, I’m fine. Just some…family stuff going on. Nothing to worry about.”

Monika: “Alright, if you say so. We’re just glad you’re here now.”

(Yuri’s stomach ripples slightly.)

Yuri: “Oh! Looks like Violet’s finally awake!”
get_random_int() in linux
I AM STARTING A DATA science company. i want to focus on providing machine learing , data analysis, time series analysis and forecasting in finace, all kinds of mathematicalmodels like arch garch, economic and other business areas
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:   Airbus to cut A380 production rate to 6 from 2020. Will deliver 8 A400M in 2020, from 15 this year. Production slowdown to impact 3,700 jobs.
Airbus  on Wednesday confirmed reduced production rates of its double-decker A380 and A400M military aircraft and said up to 3,700 jobs would be impacted in
Assistant: