Warrior0302/CAP-Bench huggingface dataset:
Next Friday, I’m planning a family vacation to Miami and need two contingency plans based on the weather.

First, check AccuWeather for the **daytime precipitation probability** in Miami for the travel day.  
- If the precipitation probability is **above 40%**, go to TripAdvisor and find **3 indoor things to do** that are **Good for Kids**, sorted by **rating (highest to lowest)**.  
- If the precipitation probability is **40% or below**, find **3 outdoor things to do** that are **Good for Kids**, also sorted by **rating (highest to lowest)**.  

If AccuWeather cannot provide the precipitation probability for that travel day, use the **daytime precipitation probability for the nearest available date** and apply the same threshold logic.

Return the precipitation probability value for the day (or nearest available date), along with each recommended activity’s **name, rating, review count, and TripAdvisor detail page link**.
I want to plan my Python data engineering study for 2026.
First, go to Amazon and search for "Python Data Engineering" paperback books. Use the filtering options to identify 3 candidate books that have a rating of 4 stars or higher, are priced between $30 and $60, and were published in 2024 or 2025.
Next, go to Goodreads, look up these 3 books, and select the one with the highest "Rating Count" as the final choice.
Finally, extract core technical keywords mentioned in the title of the ultimately selected book (e.g., PySpark, Kafka, Airflow). Then, go to StackOverflow, search for the tag corresponding to this technology, navigate to that tag's page, sort by "Votes," and find the question with the highest number of votes.

Please output: the titles/prices/publication years of the 3 candidate books, the Goodreads rating count of the finally selected book, the extracted keyword, and the title and link of the highest-voted StackOverflow question.
I need to reproduce a **Music Source Separation** algorithm and want to find several reliable open-source implementations.

First, search arXiv for papers on Music Source Separation from the **last six months**, and pick **three papers** that appear to be relatively influential. Then, for each of these three papers, search GitHub for the corresponding official repository.

For each repository found, focus on checking:

1. Whether it is an **Official Implementation**;  
2. Whether there have been any code updates (**commits**) in the **last two months**;  
3. Whether it is a complete project or just a skeleton (e.g., check whether the file tree includes actual `src` or training code, not just README files).

If no official implementation is found for a paper, look for a relevant community reproduction repository instead (prefer ones with higher star counts) and clearly label it as **“Unofficial.”**

Finally, compile a checklist that includes the paper title, the corresponding GitHub repository link, and conclusions for each of the checks above.
I’m going to San Francisco this coming Friday to see **“The Racket”** stand-up show at **Cobb’s Comedy Club**, which starts at **8:00 PM**. Please help me plan what to do after the show: first, confirm approximately what time the show will end, then find **3 restaurants on Yelp** that are within a **10-minute walk** and still open at that post-show time. Prefer places rated **4.0 stars or higher**, and **no fast food**.

Please provide the **name**, **rating**, **exact walking time**, and **closing time** for each of the 3 places. Also, please check which one has Yelp reviews that mention **“late-night snack”** or **“late night”** most frequently.
A family member of mine was recently diagnosed with hypertension. The doctor recommended a low-sodium diet, but he is allergic to shellfish and peanuts. I want to create a one-week dinner plan that meets hypertension dietary guidelines while avoiding allergenic ingredients.

First, check Mayo Clinic for dietary recommendations for people with hypertension. Specifically record the daily sodium intake target relevant to blood pressure management. If the page does not provide a “hypertension-specific” value, record the sodium recommendation given for adults/cardiovascular health and clearly note the applicable population. Also capture the recommended food categories.

Then go to WebMD to confirm possible cross-reactive foods for shellfish and peanut allergies (e.g., whether other seafood or legumes are safe).

Next, search AllRecipes for low-sodium dinner recipes. Find 5 recipes rated above 4 stars with no more than 600 mg sodium per serving. Review each recipe’s full ingredient list carefully, exclude any recipe containing shellfish, peanuts, or identified cross-reactive ingredients, and keep 3 final safe recipes.

For the core protein ingredients in these 3 recipes (e.g., chicken, beef), check the EWG database for food safety ratings, ensuring there are no pesticide-residue or additive-risk concerns.

Output:
- Summary of Mayo Clinic hypertension diet guidance (daily sodium limit, recommended food types, and the applicable population for the sodium value) + page link  
- WebMD-confirmed list of cross-reactive allergy foods + page link  
- For 3 qualifying recipes: recipe name, link, sodium per serving, full ingredient list, and user rating  
- EWG safety rating + link for each recipe’s core protein ingredient
I've recently developed a keen interest in mythological motifs on ancient Greek pottery and would like to study them systematically.

First, please go to the Getty Museum website and find three examples of 5th-4th century BCE Greek red-figure pottery. Ensure that the description for each piece explicitly mentions specific Greek mythological figures (e.g., Athena, Heracles, Achilles, etc.). Record the name of the pottery, the mythological figure(s) depicted, the date, and the link to the Getty detail page.

Next, use the names of these three mythological figures to search on Google Scholar. For each figure, find two English academic papers published since 2020 with more than 10 citations, whose themes are related to Greek pottery art or mythological iconography.

Finally, go to YouTube and search for explanatory videos using the English name of each mythological figure. Filter for educational content between 10-30 minutes in length with over 5,000 views. Find the single most relevant video for each figure, and expand its description to confirm whether it mentions ancient Greek art or pottery.

**Output:**
*   **For each piece of pottery:** name, date, mythological figure(s) depicted, Getty link.
*   **For each paper:** title, author(s), publication year, citation count, Scholar link.
*   **For each video:** title, duration, view count, channel name, a summary of the video description (highlighting whether it mentions pottery or art), and YouTube link.
I’ve recently been exploring fine-tuning techniques for large language models, and I heard there’s a classic paper on **“Llama 2”** fine-tuning (the title likely includes “Llama 2” and “Open Foundation”). Please help me find this paper on arXiv.  

After finding it, I want to check whether there are any official or highly popular community reproductions on Hugging Face. First, extract the paper’s **Code link** or **project homepage** from arXiv (if no usable code/project link is provided on the page, then use the paper title or author names as search clues), and then search for the corresponding model on Hugging Face.  

Find the version with the highest download count, and check whether its Model Card explicitly cites or mentions which dataset(s) were used. Also confirm whether this model is officially released by **meta-llama**.  

Finally, tell me the **model name**, **whether it is official**, and the **dataset name(s) mentioned**.
I’m arranging an interstate consultation trip with gastroenterology specialists for my father, and I need to screen doctors and plan the itinerary. First, go to health.usnews.com and find one **Gastroenterologist** at **Mayo Clinic in Rochester, Minnesota** and one at **Cleveland Clinic in Ohio**. Both doctors must meet two strict criteria: **more than 20 years of experience** and a **perfect 5-star patient rating** (if no doctor at that location has a full 5 stars, relax the rating requirement to **4.5 stars or higher** and clearly note the reason for the relaxation).  

After identifying the doctors, go to maps.google.com and calculate the estimated **air travel time** from **Mayo Clinic (Rochester)** to **Cleveland Clinic**.  

Finally, return to the map view for **Mayo Clinic (Rochester)** and find a hotel. Prioritize hotels that meet both conditions: **within 1 mile walking distance** and **rated above 4.5**. If no hotel satisfies both at the same time, keep the rating threshold at **≥4.5** and relax walking distance to **within 1.5 miles**, and explicitly state this relaxation in the output.  

Output the following: both doctors’ names, years of experience, ratings, flight travel time between the two locations, and the selected hotel’s name, walking distance, rating, and all detail-page links.
I just received a software engineer offer from a tech company in the San Francisco Bay Area, with an annual salary of $120,000, and now I need to find housing. The company is Salesforce. First, I want to confirm the exact address of their San Francisco office (preferably from the company’s LinkedIn page; if LinkedIn does not show the full address, use Salesforce’s official office locations page or the official Google Maps location page to verify it, and cite the source).

Then help me find apartments near that address with a budget of no more than $2,500/month, ideally with a driving commute of no more than 30 minutes, and it must be a 1-bedroom unit. My top priorities are an in-unit washer and dryer and a parking space, since I plan to drive to work.

Please try to find 3–5 listings that meet these criteria. If there are fewer than 3 under the strict requirements, keep the existing results and state the count—do not relax any of my filters. For each listing, calculate the total move-in cost, including deposit and parking fees, and estimate the monthly commute time.

Finally, summarize which options offer the best value for money so I can plan in-person tours.
Lately my commuting time has been pretty fragmented, so I’m looking for a few highly rated, short-play RPGs suitable for Steam Deck.  
First, go to Metacritic and filter for RPG titles with a Metascore above 85. Platform can be either PC or Switch. Sort by user rating (if that sort option is unavailable, use the closest user-related sort available on the site, or keep the default sort and continue). Check multiple pages rather than only the first popular page.

Then pick 3 titles you think are good candidates, and look up their **Main Story** playtime on HowLongToBeat. Prioritize games with a main story of **20 hours or less**. If there are fewer than 3 titles strictly under 20 hours, keep the **Metascore ≥ 85** requirement unchanged and fill the list to 3 from the candidate pool, clearly marking which ones exceed 20 hours.

Finally, go to Steam and confirm whether these 3 games are Steam Deck verified (shown with the green **Deck Verified** icon).

Please output the following for the 3 games: **title, Metascore, HLTB Main Story length, and Steam Deck compatibility status**.
I just watched 'Dune 2' and want to delve deeper into the story behind the film.

First, go to IMDb, find the movie, and note down the director's name, the original author from the 'Writers' section, and the title of the original book.

Then, search for the original book title on Goodreads. Once found, provide its rating, number of reviews, and page count. If it's part of a series, list the titles of other books in the same series.

Next, go to YouTube and search for 'Dune 2 behind the scenes' or 'Dune 2 making of'. Filter the results to videos longer than 15 minutes, sort them by view count in descending order, and identify the top 3 videos that are behind-the-scenes/making-of specials.

Finally, visit the official Criterion Collection website and search for the director's name. Check if any of his other works are featured. If so, list the title of the work, available formats (DVD/Blu-ray/4K), and a brief description of the special features.

**Output:**
Movie Director's Name, Original Book Title, Original Author, Goodreads Rating, Number of Reviews, Page Count, Other Series Titles (if applicable), Goodreads Book Link, Titles of the top 3 YouTube videos, View Counts, Durations, Video Links, Titles of other director's works featured on Criterion, Formats, Brief Description of Special Features, Criterion Collection Detail Page Links.
I’m planning to drive my 2024 Tesla Model Y Long Range on a weekend next month from Griffith Observatory in Los Angeles to the Bellagio Hotel in Las Vegas. I don’t trust the official EPA range, so first go to Car and Driver and find the real-world “75-mph highway range” test result for this model. If the exact model’s 75-mph test data is unavailable, then find the tested 75-mph range for a same-year or nearby-year Model Y Long Range on Car and Driver, and clearly note any model/year differences. Use 80% of that tested range as my safe maximum distance for the first driving leg.

Next, plan the route in Google Maps and use the “Search along route” feature to identify 3 Supercharger stations located between 150 miles from the origin and that safe maximum distance (if fewer than 3 are available, record all that can be found and note the count).

Finally, for each of those 3 charging stations, go to Yelp and find the highest-rated restaurant within 0.2 miles of the station’s address (rating must be 4.0+ and price level must be $$ or lower). If no qualifying option exists within 0.2 miles, expand the radius to 0.5 miles and explicitly note that this relaxation was applied.

Output: the Car and Driver tested 75-mph range, the calculated safe maximum distance, and the C&D article link; the names of 3 candidate charging stations and their distances (in miles) from the origin; and for each station, the recommended restaurant name, Yelp rating, price tier, and Yelp detail page link.
As a film noir enthusiast, I want to collect three classic films released between 1940 and 1959 that are highly acclaimed.

First, on Metacritic, filter for film noir titles released between 1940 and 1959 with a Metascore of 85 or higher. Sort them in descending order by score and select the top three.

Next, visit the official Criterion website to confirm if Blu-ray versions (non-4K, non-DVD) of these three films are available for purchase. Check the technical specifications for a "4K digital restoration" note and record the official Criterion website price.

Finally, search on eBay for used Blu-ray discs of these three films. Requirements: condition "Very Good" or better, "Buy It Now" format only, and the total price (including shipping) must be lower than the official Criterion website price.

Output the following for each film: Title, Metascore, Criterion official Blu-ray price, whether it has a "4K digital restoration" note, and the lowest eligible eBay item price and link.
I am conducting research on Neural Radiance Fields (NeRF), and my advisor asked me to compile the latest NeRF-related work from CVPR 2024. First, go to OpenReview and search for papers in CVPR 2024 that contain the keywords “NeRF” or “Neural Radiance Fields.” Filter for papers with acceptance status **Oral** or **Spotlight**, and collect up to 5 papers (if fewer than 5 are available, record the actual number). For each paper, record the title, authors, and OpenReview link.  

Then, use each paper’s title and first author name to search arXiv for the corresponding preprint. After finding a match, record the arXiv ID and PDF link.  

Finally, go to Bilibili and search for explanatory videos using core keywords from each paper title (e.g., “神经辐射场” or key English title terms). Prefer one Chinese explanatory video per paper with duration over 10 minutes and more than 10,000 views. If no video for a paper satisfies both conditions, keep the core requirement of being a Chinese explanation video, select the most relevant one with relatively higher views or longer duration, and note which condition(s) are not met.  

For each paper, output: paper title, first author, acceptance type (Oral/Spotlight), OpenReview paper page link, arXiv ID, arXiv PDF link, Bilibili video title, video duration, view count, video link, and whether the video meets the criteria “duration ≥ 10 minutes and views ≥ 10,000.”
I’m preparing a submission to CVPR and want a comprehensive understanding of the conference. First, please check DBLP to analyze the topic distribution of CVPR papers from the past three years, with a focus on identifying major trending areas. Then, on arXiv, find several highly influential papers in those trending areas (judged using both visible paper-page signals and external citation-metric pages), and review their writing style and experimental design. Next, go to GitHub to locate the corresponding open-source code repositories for those papers, and evaluate their star counts and code completeness to understand current experimental standards. Finally, search Bilibili for CVPR-related peer-review experience sharing or conference talk videos with high view counts and durations over 20 minutes, and summarize the key reviewing points that are most frequently emphasized.  

At the end, provide me with a structured summary of: what directions this conference currently values most, what experiments the reference papers typically include, expected code quality standards, and what reviewers pay special attention to.
Our team is evaluating environmental compliance risk in Harris County, Texas. Please use the EPA ECHO system and prioritize list-based results (do not rely on the map view) to identify one chemical or manufacturing facility in the county that is currently in **Significant Non-Compliance (SNC)** status. First, open the facility ranked at the top of the list, review its detailed report, and determine the specific chemical pollutant for which it is in violation (e.g., benzene, lead, particulate matter).

If the map page appears blank, flickers, or fails to load reliably, switch to an accessible list/table results page and continue the screening there. If the first facility’s detail page does not clearly name a pollutant, move on to the next facility, and continue until you find one with an explicit pollutant record.

After obtaining the pollutant name, conduct a technical search in the USPTO patent database. Find **two U.S. patents granted after 2021** that are specifically aimed at treating or removing that pollutant. Finally, report the facility name, the violating pollutant, and the titles of the two patents you found.
I am planning a "Game vs. Reality" pilgrimage video for "Like a Dragon: Infinite Wealth," focusing on Honolulu, Hawaii.

First, navigate to IGN and search for "Real Life Locations" or a similar guide for the game. Identify a list that maps in-game establishments/landmarks to their real-world counterparts.

From this list, select four real-world commercial establishments (restaurants, shops, or bars, excluding generic parks or streets). Each selected location must have a Google Maps rating of 4.0 stars or higher to ensure their quality and appeal.

For each of these four selected locations, search for their real-world names on Google Maps. Verify their specific "price level" (e.g., $, $$) and their current "operating status" (e.g., Open or Closed).

Output the following for each location: In-game Name, Real-world Counterpart Name, Real-world Address, Google Maps Rating, Price Level, Operating Status, and the respective links to the IGN guide page and the Google Maps detail page.
I’ve just finished learning Python fundamentals and want to switch into data analysis, but I’m not sure whether pandas or PyTorch is currently more in demand.  
First, go to Stack Overflow and open the two tag pages ([pandas] and [pytorch]). Record each tag’s **“Total questions”** count. Then, under each tag, sort by **Newest** and count the number of new questions posted in the **past 24 hours** as today’s activity level. Compare the two and identify which library is more active.

Next, for the winning library, go to Udemy and find **3 advanced courses** that meet all of the following criteria:  
- Rating above **4.5**  
- **English-language** instruction  
- Duration over **17 hours**  
- Level set to **Expert** or **Intermediate**

If fewer than 3 courses meet all criteria, keep the rating, language, and duration requirements unchanged, first relax the Level filter to **All Levels**, and clearly mark which courses were added after relaxing this condition.

Output required:  
- For Stack Overflow: total question count and past-24-hour question count for both libraries, plus the name of the winning library  
- For Udemy: the 3 course titles, instructors, ratings, durations, prices, and course links
I want to refresh my bedroom in a Mid-Century Modern style this month. Please create a soft-furnishing shopping list with **three items**, with a **total budget of no more than $400**.

First, go to **IKEA**, search for **“Chest of drawers,”** filter for **Brown/Wood** finishes, and sort by **price from low to high**. Choose one **3-drawer or 4-drawer** chest that is currently marked **“In stock”** and priced **under $200**.

Next, go to **Etsy** and find a **“Bauhaus exhibition”** art poster that matches this style. It must be a **physical item** (not a digital download), from a **Star Seller** shop, with a size of **11x14 inches or larger**.

Finally, go to **Target** and find a **table lamp** with a **Brass/Gold** finish, rated **4 stars or higher**, and available for **Shipping**.

For each item, provide: **product name, price, key verification info** (IKEA stock status, Etsy shop badge, Target rating), **size/specs**, and the **product detail page link**.
I’m preparing for a Google interview and want to understand the latest research trends in the **Machine Learning** direction at their Mountain View headquarters.

First, search LinkedIn for a **Research Scientist** role at Google in **Mountain View**, and find one that was posted recently (**Past Month**) or otherwise appears relatively new. Carefully review the job description and extract one core technical keyword (such as a specific large-model technique or algorithm name). If you encounter a LinkedIn login requirement or cannot view full job details, record that access limitation and extract the keyword from whatever LinkedIn-visible job summary is available; if the summary is insufficient, then extract one keyword from visible skills/requirements on the same page.

After obtaining the keyword, go to Google Scholar and check Google’s publications in that area. Focus on papers from **2023 onward** and identify the top 3 by citation count (**Cited by**). For each of the three papers, provide the title, first author, and citation count, and also verify whether the author list appears to include Google-affiliated researchers (typically indicated by institution tags/abbreviations next to names). If Google Scholar triggers unusual traffic checks/CAPTCHA or cannot be accessed reliably, switch to Semantic Scholar and use the same keyword and year filter (**2023 onward**), then select the top 3 by citations and output the same fields (title, first author, citation count), plus the basis for Google-affiliation judgment (author institution or institution info on the paper page).