agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
Optimize the code by reducing the number of joins : "import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.functions;

import static org.apache.spark.sql.functions.*;

public class SCDType2Example {
public static void main(String[] args) {
SparkSession spark = SparkSession
.builder()
.appName(“SCDDevice2Example”)
.master(“local”)
.getOrCreate();

Dataset oldData = spark.read().parquet(“path/to/oldData”);
Dataset newData = spark.read().parquet(“path/to/newData”);

// Repartition the data
oldData = oldData.repartition(“id”);
newData = newData.repartition(“id”);

String primaryKey = “id”;
String[] nonKeyColumns = {“name”, “age”, “city”}; // Parametrizable columns
String hashColumn = “hash”;

oldData = createHash(oldData, nonKeyColumns, hashColumn);
newData = createHash(newData, nonKeyColumns, hashColumn);

Dataset unchangedData = oldData.join(
broadcast(newData),
oldData.col(primaryKey).equalTo(newData.col(primaryKey))
)
.where(oldData.col(hashColumn).equalTo(newData.col(hashColumn)))
.select(oldData.columns());

Dataset changedOldData = oldData.join(
broadcast(newData),
oldData.col(primaryKey).equalTo(newData.col(primaryKey))
)
.where(oldData.col(hashColumn).notEqual(newData.col(hashColumn)))
.select(oldData.columns())
.withColumn(“end_date”, current_date())
.withColumn(“current_flag”, lit(false));

Dataset changedNewData = newData.join(
broadcast(oldData),
newData.col(primaryKey).equalTo(oldData.col(primaryKey))
)
.where(newData.col(hashColumn).notEqual(oldData.col(hashColumn)))
.select(newData.columns())
.withColumn(“start_date”, current_date())
.withColumn(“end_date”, lit(null).cast(“date”))
.withColumn(“current_flag”, lit(true));

Dataset newRows = newData.join(
broadcast(oldData),
newData.col(primaryKey).equalTo(oldData.col(primaryKey)),
“leftanti”
)
.withColumn(“start_date”, current_date())
.withColumn(“end_date”, lit(null).cast(“date”))
.withColumn(“current_flag”, lit(true));

Dataset result = unchangedData
.union(changedOldData)
.union(changedNewData)
.union(newRows);

result.write().parquet(“path/to/output”);
}

private static Dataset createHash(Dataset data, String[] nonKeyColumns, String hashColumn) {
Dataset result = data;
StringBuilder concatString = new StringBuilder();

for (String column : nonKeyColumns) {
concatString.append(“cast(”).append(column).append(" as string),“);
}

String concatColumns = concatString.toString();

if (concatColumns.endsWith(”,“)) {
concatColumns = concatColumns.substring(0, concatColumns.length() - 1);
}

return result.withColumn(hashColumn, sha2(functions.concat_ws(”", functions.expr(concatColumns)), 256));
}
}"
Fact check this:  In the film, Rakesh (Raa-Kaysh) suspected that his younger brother and Nisha (his late wife’s younger sister) had feelings for one another. In fact, Rakesh (Raa-Kesh) gives Nisha (Nee-shah) a chance to back out of the wedding in a letter he writes to her after she suffers from a medical episode. So, it is obvious to say that this hypothetical scenario would not have happened if Obviously for this to happen
Send only episode number from below text like 01, 02, 03.etc without any extra text.

918199000000_TrueCaller_(38619).zip
I sued this code: # Retrieve depth data
    depth_data = client.depth(symbol=symbol)
    
    bids = depth_data['bids']
    bid_prices = [float(bid[0]) for bid in bids]
    highest_bid_price = max(bid_prices)
    lowest_bid_price = min(bid_prices)
    buy_lowest_price = 0

    for bid_price in bid_prices:
        if bid_price > mp + (mp / 100):
            buy_lowest_price = bid_price
            break

    if buy_lowest_price == 0:
        buy_lowest_price = mp + (mp / 100)

    b_h = highest_bid_price
    b_l = buy_lowest_price

    asks = depth_data['asks']
    ask_prices = [float(ask[0]) for ask in asks]
    highest_ask_price = max(ask_prices)
    lowest_ask_price = min(ask_prices)
    sell_highest_price = 0.0

    for ask_price in ask_prices:
        if ask_price < mp - (mp / 100):
            sell_highest_price = ask_price
            break

    if sell_highest_price == 0:
        sell_highest_price = mp - (mp / 100)

    s_h = highest_ask_price
    s_l = lowest_ask_price

Can you add in buy_lowest_price and in sell_highest_price that if buy_lowest_price < mp + (mp / 100 ) it need to subtract qty of this bid and same system for ask
Goal
    Générer une image de suggestion de présentation d’un plat en fonction de sa description, en intégrant une ambiance inspirée du pays d’origine et un style photographique spécifique.

    1. Utiliser la description fournie pour définir l'apparence du plat.
    2. Ajouter une ambiance souhaitée.
    3. Appliquer un style photographique précis avec des paramètres spécifiques. 
    4. Optimiser le prompt pour le donner à midjourney 
  
    Return Format
    Tu dois renvoyer un objet JSON strictement respecté avec cette structure 
    {
    "data": "<prompt>"
    }
    - Le prompt doit être écrit en anglais et bien détaillé.

    Warnings
    - Respecte le format JSON strict.
    - N’explique pas la démarche, renvoie uniquement l’objet JSON.
    - Le prompt doit inclure tous les éléments demandés.

    Context Dump
    Description fournie : Visualize a plate of exquisite Mussel Risotto, elegantly served in a wide, shallow white bowl. The risotto is creamy and golden, enriched by a hint of ground turmeric, showcasing a perfect al dente texture. Scattered throughout are plump, succulent mussels, some still nestled in their dark shells for an appealing contrast. A drizzle of rich double cream glistens on the surface, and a generous sprinkling of freshly grated parmesan adds a touch of sophistication. The dish is garnished with finely chopped parsley, providing a vibrant green touch that pops against the earthy tones of the risotto and mussels. In the background, a subtle hint of steam wafts from the bowl, enhancing the warmth and inviting aroma of this coastal-inspired masterpiece.
    Ambiance souhaitée : 
        A refined, Michelin-star kitchen setting with stainless steel countertops gleaming under soft, focused lighting. The dish is carefully plated on a modern, minimalist plate, surrounded by high-end culinary tools like a sharp chef’s knife and elegant sauce ladles. In the background, the kitchen hums with the energy of a top-tier restaurant: sous-vide machines, open flame from a professional stove, and neatly organized spices. A professional, polished atmosphere that exudes expertise, precision, and a passion for gastronomy, with a subtle air of sophistication.
write about audio visual production
Rewrite this article:

A financially motivated Chinese threat actor dubbed "SilkSpecter" is using thousands of fake online stores to steal the payment card details of online shoppers in the U.S. and Europe.

The fraud campaign started in October 2024, offering steep discounts for the upcoming Black Friday shopping period that usually sees elevated shopping activity.

EclecticIQ threat researcher Arda Buyukkaya, who discovered the campaign, told BleepingComputer that, as of thepublishing of their report, SilkSpecter operates 4,695 fraudulent domains.

These sites impersonate well-known brands such as the North Face, Lidl, Bath & Body Works, L.L. Bean, Wayfair, Makita, IKEA, and Gardena.

In many cases, the domain names used in the campaign include the 'Black Friday' string, clearly targeting online shoppers looking for discount deals.

SilkSpecter websites are well-designed and typically named after the impersonated brand to appear authentic at a quick glance. However, their sites usually use top-level domains like '.shop,' '.store,' '.vip,' and '.top,' which are not generally associated with large brands or trustworthy e-commerce sites.

Depending on the victim's location, the website uses Google Translate to automatically adjust the language on the fraud sites accordingly.

The phishing sites integrate Stripe, a legitimate and trusted payment processor, which adds to the site's legitimacy while still allowing them to steal credit card information.

SilkSpecter also uses tracking tools like OpenReplay, TikTok Pixel, and Meta Pixel on the sites. These tools help them monitor visitor behavior and possibly adjust their tactics to increase the operation's effectiveness.

When users attempt to purchase from those sites, they are redirected to a payment page that prompts them to enter their credit/debit card number, expiration date, and CVV code. A phone number is also requested at the final step.

Apart from stealing the money for the order by abusing the Stripe service, the phishing kit also sends the entered card details to an attacker-controlled server.

EclecticIQ believes the phone number is stolen to be used later in voice or SMS phishing attacks required for handling two-factor authentication (2FA) prompts when exploiting the payment card data.

SilkSpecter is believed to be Chinese, based on their use of Chinese IP addresses and ASNs, Chinese domain registrars, linguistic evidence in the sites' code, and previous use of the Chinese Software as a Service (SaaS) platform named "oemapps" (prior to Stripe).

BlackFriday shoppers are recommended only to visit official brand websites and avoid clicking on ads, links from social media posts, or promoted results on Google Search.

Finally, cardholders should activate all available protection measures on their financial accounts, including multi-factor authentication, and monitor their statements regularly.
cust cam_int_customers%rowtype[];
begin

open cust_cursor;
loop
  fetch cust_cursor bulk collect into cust limit cust_limit;
  
    exit when cust.count = 0;

    forall i in 1 .. cust.count
update cam_int_customers c
set prt_mcard_idtp_id = 14
where c.cust_id = cust(i).cust_id
  and prt_mcard_idtp_id is null
  and (prt_mcard_series is not null
  or prt_mcard_num is not null
  or prt_mcard_date_of_issue is not null
  or prt_mcard_valid_for is not null);
commit;
end loop;

Переделать под plpgsql
Sonic x Dragon ball: Sonic Characters react to Goku becomes Super Saiyan. (Sonic Characters Don't know Dragon ball Universe)
Goal
    Traduire toutes les valeurs textuelles présentes dans une liste d’objets d’une recette de cuisine de en (ISO 639) vers es (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 es.
    - 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 : es (ISO 639)
    Données à traduire :

    [
  {
    "recipeCode": "84f4868e26cd4460b990a9ac851ac674",
    "updated": "2025-05-19T23:09:29.280Z",
    "title": "Ban Doufu",
    "subtitle": null,
    "description": "During a hot spell in Shanghai, my appetite was as slow as the weather, but I craved some local specialties, especially something light and simple. A friend recommended one of the many small private restaurants now offering meals. China is the original home of soya bean cultivation, and for over 3,000 years, the Chinese have created a variety of soya bean products. This recipe requires no cooking. The fresh, soft, silky-smooth beancurd—highly nutritious but naturally mild—only needs a little flavor and texture, provided here by dried shrimps and preserved vegetables, to make a truly delightful starter. Use very fresh beancurd, preferably the soft variety. Serve chilled.",
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "fresh, soft beancurd",
          "dried shrimps",
          "sichuan preserved vegetables, rinsed and finely chopped",
          "chilli bean sauce",
          "sesame oil"
        ]
      },
      {
        "section": "For the garnish",
        "ingredients": [
          "finely chopped spring onions (optional)."
        ]
      }
    ],
    "instructions": [
      "Carefully drain the beancurd and soak it in several changes of cold water.",
      "Drain thoroughly, then cut into 2.5cm (1in) cubes.",
      "Arrange the beancurd on a serving platter and chill in the refrigerator.",
      "Soak the dried shrimps in warm water for 20 minutes, then drain completely.",
      "Finely chop the dried shrimps and mix them with the preserved vegetables and chilli bean sauce in a small bowl.",
      "Spread this mixture evenly over the beancurd and drizzle with sesame oil.",
      "If desired, garnish with chopped spring onions and serve immediately."
    ],
    "notes_ingredients": null,
    "notes_instructions": null
  }
]
Goal
    Traduire toutes les valeurs textuelles présentes dans une liste d’objets d’une recette de cuisine de en (ISO 639) vers es (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 es.
    - 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 : es (ISO 639)
    Données à traduire :

    [
  {
    "recipeCode": "4a3648aa58b44ba180018f25051f21c4",
    "updated": "2025-04-24T09:56:21.843Z",
    "title": "Charred Broccoli with Tonnato, Pecorino, Lemon, and Chili Flakes",
    "subtitle": "A fresh and vibrant take on chips and dip!",
    "description": "Broccoli truly shines when charred. The floret tips crisp up beautifully while the stems retain a pleasant tenderness, creating a delightful contrast of textures.",
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "broccoli, stems trimmed and peeled, cut into long florets",
          "lemon, halved, and one of the halves cut into 4 wedges",
          "dried chile flakes",
          "kosher salt and freshly ground black pepper",
          "extra-virgin olive oil",
          "nice chunk of aged pecorino romano, for grating",
          "dried breadcrumbs",
          "tonnato (optional)"
        ]
      }
    ],
    "instructions": [
      "Preheat the broiler.",
      "Spread the broccoli florets in a single layer on a rimmed baking sheet. Broil without oil until slightly tender and nicely charred on most sides, turning once, for approximately 5 to 7 minutes. Grilling in a grill basket is also an option.",
      "Transfer the charred broccoli to a bowl and squeeze the juice of half a lemon over it. Season generously with salt, black pepper, and ½ teaspoon of chili flakes. Drizzle with ¼ cup of olive oil and toss to combine. Taste and adjust seasonings as needed.",
      "Arrange the seasoned broccoli on a serving platter. Grate a generous amount of Pecorino Romano over the top. Sprinkle with dried breadcrumbs, if desired. Serve with the lemon wedges and a side of tonnato for dipping, if using."
    ],
    "notes_ingredients": "",
    "notes_instructions": ""
  }
]
Act as a spelling corrector, content editor, and text improver. ONLY return the rewritten text and nothing else.

Strictly follow these rules:
- Correct spelling, grammar, and punctuation errors in the given text
- Enhance clarity and conciseness without altering the original meaning
- Use the same language as the original text
- Divide lengthy sentences into shorter, more readable ones
- Eliminate unnecessary repetition while preserving important points
- Prioritize active voice over passive voice for a more engaging tone
- Opt for simpler, more accessible vocabulary when possible
- ALWAYS ensure the original meaning and intention of the given text
- ALWAYS maintain the original language of the text
- ALWAYS maintain the existing tone of voice and style, e.g. formal, casual, polite, etc.
- NEVER surround the improved text with quotes or any additional formatting
- If the text is already well-written and requires no improvement, do not change the given text

Text to improve:



# The One with the Feedback

Thanks to the feedback from kind fellow Redditers, I've implemented a few improvments

- dark mode added
- replaced line chart with bar chart
- aggregate graph for chosen browsers added
- short intro text added

I've also added a Privacy Policy text so you know what is happening to your data

Don't forget to subscribe for a newsletter for the latest major updates of the project

💁🏻‍♂️ https://browserating.kawaiier.dev/
make this prompt more ok:

The research plan outlines that each study will be conducted either through web searches or by consulting the AI. The plan is ordered, and the result of the previous research plan will be used as the input for the next research plan. Make sure to have a very detail and comprehensive research plan. Example: CBDC in Indonesia vs the world
(1) Find information on the current status of CBDC development and implementation in Indonesia.
(2) Find information on the current status of CBDC development and implementation in other countries around the world.
(3) Compare and contrast the approaches to CBDC in Indonesia and other countries.
(4) Find expert opinions and analysis on the potential benefits and risks of CBDC in Indonesia and globally.
(5) Find information on the technological infrastructure and platforms being considered for CBDC in Indonesia and other countries.
(6) Find information on the legal and regulatory frameworks being developed for CBDC in Indonesia and other countries.
(7) Find information on the potential impact of CBDC on the financial system and economy in Indonesia and globally.
(8) Find information on the public's perception and adoption of CBDC in Indonesia and other countries.
Write me a story where I visit lucifer from hazbin hotel at his house for tea. I’m wearing a white dress. I had six cups of tea already. After a while lucifer asks me if I need to let all the tea I drank out. I playfully tell him that it's probably not a good idea to say something like that to a lady. The story is in second person.
You are a database expert and SQL optimizer. You will be given a SQL query and its rewritten variant. The rewritten query improves performance upon the original while maintaining equivalence. Your task is to create a new pair of original and rewritten queries that invoke a similar rewrite.

[ORIGINAL QUERY]```select a.ca_state state,count(*) cnt from customer_address a,customer c,store_sales s,date_dim d,item i where a.ca_address_sk = c.c_current_addr_sk and c.c_customer_sk = s.ss_customer_sk and s.ss_sold_date_sk = d.d_date_sk and s.ss_item_sk = i.i_item_sk and d.d_month_seq = (select distinct (d_month_seq) from date_dim where d_year = 2002 and d_moy = 3 ) and i.i_current_price > 1.2 * (select avg(j.i_current_price) from item j where j.i_category = i.i_category) group by a.ca_state having count(*) >= 10 order by cnt,a.ca_state limit 100;```

[REWRITTEN QUERY]```WITH avg_price AS ( SELECT i_category, AVG(i_current_price) avg_price FROM item GROUP BY i_category ), month_seq AS ( SELECT d_month_seq FROM date_dim WHERE d_year = 2002 AND d_moy = 3 LIMIT 1 ) SELECT a.ca_state state, COUNT(*) cnt FROM customer_address a JOIN customer c ON a.ca_address_sk = c.c_current_addr_sk JOIN store_sales s ON c.c_customer_sk = s.ss_customer_sk JOIN date_dim d ON s.ss_sold_date_sk = d.d_date_sk JOIN item i ON s.ss_item_sk = i.i_item_sk JOIN avg_price ap ON i.i_category = ap.i_category JOIN month_seq ms ON d.d_month_seq = ms.d_month_seq WHERE i.i_current_price > 1.2 * ap.avg_price GROUP BY a.ca_state HAVING COUNT(*) >= 10 ORDER BY cnt, a.ca_state LIMIT 100;```

Now, consider the TPC-DS schema and create a new original and equivalent rewritten query pair such that the rewrite operation is similar to the provided demonstration.Keep the following points in mind while creating the new query pair:
1. The original query must contain CTEs.
2. The original query must make use of the following fact tables: ['store_sales']. Below is the join graph for these fact tables:
    ['store_sales(ss_store_sk) references store(s_store_sk);', 'store_sales(ss_cdemo_sk) references customer_demographics(cd_demo_sk);', 'store_sales(ss_item_sk) references item(i_item_sk);']
4. The original query must employ 2 joins.
5. The original query must employ the following operators in its WHERE clause: ['max', 'avg'].
6. The rewritten query must be equivalent to the original query, and must follow a similar rewrite pattern as in the given example. Enclose the original and rewritten queries within ``` and ```.
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: I didn’t get the biggest project and technology and they a branch