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Database table size impacts performance in more ways than one: a) B-tree depth. Using 8k pages and a 16b uuid: 1 level = ~370 rows 2 levels = ~138k rows 3 levels = ~50m rows 4 levels = ~20b rows The lookup cost on a table with 100k rows...

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Ben Dicken

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211,974 次观看 • 3 个月前 •via X (Twitter)

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SQL has levels to it: - level 1 SELECT, FROM, WHERE, GROUP BY, HAVING, LIMIT Master these basic keywords and you’ll be well on your way to mastering SQL. - level 2 Mastering JOINs: Most common JOINs: INNER and LEFT Less common JOINs: FULL OUTER Joins you should avoid almost always: RIGHT and CROSS JOIN Mastering common table expressions (CTEs). The WITH keyword defines a CTE which you can imagine as a “variable” that you can query later. Using variables like this you can master algorithm techniques like recursion, breadth first search and more! CTEs also make your SQL much more readable and make your coworkers hate you less compared to nested sub queries. - level 3 Mastering window functions Window functions have 3 pieces: The function (i.e. SUM, RANK, AVG) The over clause to start the window The window definition which has 3 pieces: - how to split the window up with PARTITION BY - how to order the window with ORDER BY - how to restrict the window size with ROWS clause (useful for rolling monthly averages) Understand RANK vs DENSE_RANK vs ROW_NUMBER, I have been asked this in interviews a million times. - level 4 You understand table scans, b-tree indexes, and partitioning schemes to increase performance. Doing something like COUNT(CASE WHEN) is much better than doing multiple queries with a UNION ALL. UNION ALL is terrible for all sorts of reasons that I don’t want to get into in this post. B-trees indexes allow for efficient scanning of data in the WHERE clause. Use explain plans to understand if an index is actually being used or not! Partitioning is similar to indexes except it’s a “poor mans” index. It just keeps data in specific folders and skips the folders that don’t include the data I question. What else did I miss for mastering SQL?
1:25

SQL has levels to it: - level 1 SELECT, FROM, WHERE, GROUP BY, HAVING, LIMIT Master these basic keywords and you’ll be well on your way to mastering SQL. - level 2 Mastering JOINs: Most common JOINs: INNER and LEFT Less common JOINs: FULL OUTER Joins you should avoid almost always: RIGHT and CROSS JOIN Mastering common table expressions (CTEs). The WITH keyword defines a CTE which you can imagine as a “variable” that you can query later. Using variables like this you can master algorithm techniques like recursion, breadth first search and more! CTEs also make your SQL much more readable and make your coworkers hate you less compared to nested sub queries. - level 3 Mastering window functions Window functions have 3 pieces: The function (i.e. SUM, RANK, AVG) The over clause to start the window The window definition which has 3 pieces: - how to split the window up with PARTITION BY - how to order the window with ORDER BY - how to restrict the window size with ROWS clause (useful for rolling monthly averages) Understand RANK vs DENSE_RANK vs ROW_NUMBER, I have been asked this in interviews a million times. - level 4 You understand table scans, b-tree indexes, and partitioning schemes to increase performance. Doing something like COUNT(CASE WHEN) is much better than doing multiple queries with a UNION ALL. UNION ALL is terrible for all sorts of reasons that I don’t want to get into in this post. B-trees indexes allow for efficient scanning of data in the WHERE clause. Use explain plans to understand if an index is actually being used or not! Partitioning is similar to indexes except it’s a “poor mans” index. It just keeps data in specific folders and skips the folders that don’t include the data I question. What else did I miss for mastering SQL?

Zach Wilson

34,010 次观看 • 2 个月前

SQL has levels to it: - level 1 SELECT, FROM, WHERE, GROUP BY, HAVING, LIMIT Master these basic keywords and you’ll be well on your way to mastering SQL. - level 2 Mastering JOINs: Most common JOINs: INNER and LEFT Less common JOINs: FULL OUTER Joins you should avoid almost always: RIGHT and CROSS JOIN Mastering common table expressions (CTEs). The WITH keyword defines a CTE which you can imagine as a “variable” that you can query later. Using variables like this you can master algorithm techniques like recursion, breadth first search and more! CTEs also make your SQL much more readable and make your coworkers hate you less compared to nested sub queries. - level 3 Mastering window functions Window functions have 3 pieces: The function (i.e. SUM, RANK, AVG) The over clause to start the window The window definition which has 3 pieces: - how to split the window up with PARTITION BY - how to order the window with ORDER BY - how to restrict the window size with ROWS clause (useful for rolling monthly averages) Understand RANK vs DENSE_RANK vs ROW_NUMBER, I have been asked this in interviews a million times. - level 4 You understand table scans, b-tree indexes, and partitioning schemes to increase performance. Doing something like COUNT(CASE WHEN) is much better than doing multiple queries with a UNION ALL. UNION ALL is terrible for all sorts of reasons that I don’t want to get into in this post. B-trees indexes allow for efficient scanning of data in the WHERE clause. Use explain plans to understand if an index is actually being used or not! Partitioning is similar to indexes except it’s a “poor mans” index. It just keeps data in specific folders and skips the folders that don’t include the data I question. What else did I miss for mastering SQL?
1:25

SQL has levels to it: - level 1 SELECT, FROM, WHERE, GROUP BY, HAVING, LIMIT Master these basic keywords and you’ll be well on your way to mastering SQL. - level 2 Mastering JOINs: Most common JOINs: INNER and LEFT Less common JOINs: FULL OUTER Joins you should avoid almost always: RIGHT and CROSS JOIN Mastering common table expressions (CTEs). The WITH keyword defines a CTE which you can imagine as a “variable” that you can query later. Using variables like this you can master algorithm techniques like recursion, breadth first search and more! CTEs also make your SQL much more readable and make your coworkers hate you less compared to nested sub queries. - level 3 Mastering window functions Window functions have 3 pieces: The function (i.e. SUM, RANK, AVG) The over clause to start the window The window definition which has 3 pieces: - how to split the window up with PARTITION BY - how to order the window with ORDER BY - how to restrict the window size with ROWS clause (useful for rolling monthly averages) Understand RANK vs DENSE_RANK vs ROW_NUMBER, I have been asked this in interviews a million times. - level 4 You understand table scans, b-tree indexes, and partitioning schemes to increase performance. Doing something like COUNT(CASE WHEN) is much better than doing multiple queries with a UNION ALL. UNION ALL is terrible for all sorts of reasons that I don’t want to get into in this post. B-trees indexes allow for efficient scanning of data in the WHERE clause. Use explain plans to understand if an index is actually being used or not! Partitioning is similar to indexes except it’s a “poor mans” index. It just keeps data in specific folders and skips the folders that don’t include the data I question. What else did I miss for mastering SQL?

Zach Wilson

79,578 次观看 • 1 年前

most people stick to just 1 or 2 tables they are familiar with when writing their SQL queries in we built the ability to search for tables, preview tables and easily sample rows from a table so you can find the ones you need + even discover new tables data discovery is a big problem even in small companies. most data doesn't get analyzed because most of company doesn't know it exists
0:27

most people stick to just 1 or 2 tables they are familiar with when writing their SQL queries in we built the ability to search for tables, preview tables and easily sample rows from a table so you can find the ones you need + even discover new tables data discovery is a big problem even in small companies. most data doesn't get analyzed because most of company doesn't know it exists

rahul

11,550 次观看 • 11 个月前

This is, by far, one of the best uses of modern AI. If you don't use embeddings when querying your database, you are definitely leaving a lot on the table. In this video, I'll show you how to run semantic searches using OpenAI and PostgreSQL. It's all thanks to Pgai, an open-source PostgreSQL extension: Here's what will happen: 1. We'll create a simple table with news articles 2. We'll generate embeddings for those articles 3. We'll run queries on top of those embeddings For this video, I generated the embeddings using a simple query, but pgai Vectorizer would do the same automatically as new information makes it into the database. This is awesome! If you have a PostgreSQL database with data you are searching over, you should start experimenting with semantic searches immediately. For most use cases, a combination of full-text search + semantic search is the best approach. If you don't have a PostgreSQL database around, you can try free for 30 days using Timescale: Thanks to the Timescale (now TigerData) team for partnering with me on this post!
19:19

This is, by far, one of the best uses of modern AI. If you don't use embeddings when querying your database, you are definitely leaving a lot on the table. In this video, I'll show you how to run semantic searches using OpenAI and PostgreSQL. It's all thanks to Pgai, an open-source PostgreSQL extension: Here's what will happen: 1. We'll create a simple table with news articles 2. We'll generate embeddings for those articles 3. We'll run queries on top of those embeddings For this video, I generated the embeddings using a simple query, but pgai Vectorizer would do the same automatically as new information makes it into the database. This is awesome! If you have a PostgreSQL database with data you are searching over, you should start experimenting with semantic searches immediately. For most use cases, a combination of full-text search + semantic search is the best approach. If you don't have a PostgreSQL database around, you can try free for 30 days using Timescale: Thanks to the Timescale (now TigerData) team for partnering with me on this post!

Santiago

109,517 次观看 • 1 年前

Pemdas figured out how to edit the Character Select Screen. The edits go as follows: - Now sorted as 5 rows of characters instead of 3 - Faster scroll and next page speed - Removed the randomized voicelines when you select a character (instead plays sequentially) - Added animations to make it more clear and expressive who you have selected, the slot will pulse when hovered over, and pop-up when selected. - Added multiple rows for costume slots to allow for more visible costumes to be selected. - Also added the Xenoverse 1 Villainous slots back since the bigger more fluid roster allows for more fluff slots like them. User interface Placements are not final. We hope to implement this in a future update.
0:38

Pemdas figured out how to edit the Character Select Screen. The edits go as follows: - Now sorted as 5 rows of characters instead of 3 - Faster scroll and next page speed - Removed the randomized voicelines when you select a character (instead plays sequentially) - Added animations to make it more clear and expressive who you have selected, the slot will pulse when hovered over, and pop-up when selected. - Added multiple rows for costume slots to allow for more visible costumes to be selected. - Also added the Xenoverse 1 Villainous slots back since the bigger more fluid roster allows for more fluff slots like them. User interface Placements are not final. We hope to implement this in a future update.

⭐️Revamp XV2 Mod⭐️

63,248 次观看 • 6 个月前

4 consecutive 20" corn rows and 4 off Create "alley crop" corn behind my Uncle David's cattle lot. The idea is to grow serious pasture for the cattle and preserve 90% -94% corn yields like many 60" corn growers are realizing (twin rows are better) This will also allow for in season manure applications. Either slinging chicken or turkey compost into the air or side shoot swine solution at the ankles of the corn. Corn in 2026 will be planted in the space between which has the opportunity to prime with the soil health attributes of a much more significant and diverse cover crop / forage mix. This ground now becomes useful for multiple purposes on the farm from grazing, to calving, with the ability to apply manure when all other fields are locked up
1:36

4 consecutive 20" corn rows and 4 off Create "alley crop" corn behind my Uncle David's cattle lot. The idea is to grow serious pasture for the cattle and preserve 90% -94% corn yields like many 60" corn growers are realizing (twin rows are better) This will also allow for in season manure applications. Either slinging chicken or turkey compost into the air or side shoot swine solution at the ankles of the corn. Corn in 2026 will be planted in the space between which has the opportunity to prime with the soil health attributes of a much more significant and diverse cover crop / forage mix. This ground now becomes useful for multiple purposes on the farm from grazing, to calving, with the ability to apply manure when all other fields are locked up

Jason Mauck

16,286 次观看 • 1 年前

You can now access AI directly from your database! Here is a step-by-step demo that uses GPT-4 to classify customer reviews from a MySQL dataset. And I'm only writing SQL instructions! You have to see it! The model acts as another table in the database. I can query it and join it with other tables. It's awesome! This is all thanks to mindshub. Here is their GitHub repository: They are sponsoring this post. Watch the video.
6:49

You can now access AI directly from your database! Here is a step-by-step demo that uses GPT-4 to classify customer reviews from a MySQL dataset. And I'm only writing SQL instructions! You have to see it! The model acts as another table in the database. I can query it and join it with other tables. It's awesome! This is all thanks to mindshub. Here is their GitHub repository: They are sponsoring this post. Watch the video.

Santiago

209,454 次观看 • 2 年前

An underrated issue with document parsing for RAG / agent use cases is dealing with multi-page tables - sometimes a big table spills over into multiple pages. This breaks chunking algorithms that generally operate at the page-level or smaller, and causes LLMs to lose the full view of the data. With LlamaParse Continuous Mode (in beta), you can now parse a document with multi-page tables and join them into a single table! This means you can now: 💡 Do contiguous chunking for RAG use cases OR 💡 Parse the table for text-to-SQL Check out our blog post highlighting this feature. Huge shoutout to Pierre-Loic Doulcet and Sacha Bron : Signup here: It's in beta, let us know your feedback!
2:22

An underrated issue with document parsing for RAG / agent use cases is dealing with multi-page tables - sometimes a big table spills over into multiple pages. This breaks chunking algorithms that generally operate at the page-level or smaller, and causes LLMs to lose the full view of the data. With LlamaParse Continuous Mode (in beta), you can now parse a document with multi-page tables and join them into a single table! This means you can now: 💡 Do contiguous chunking for RAG use cases OR 💡 Parse the table for text-to-SQL Check out our blog post highlighting this feature. Huge shoutout to Pierre-Loic Doulcet and Sacha Bron : Signup here: It's in beta, let us know your feedback!

Jerry Liu

24,245 次观看 • 1 年前

if they wanna talk about nepo babies, bring my man to the table as one of the panel because he has talked about it for more than a decade in many many interviews eventho he is friend with dozen of nepo babies and posh actors. im sure he has a lot to say
0:47

if they wanna talk about nepo babies, bring my man to the table as one of the panel because he has talked about it for more than a decade in many many interviews eventho he is friend with dozen of nepo babies and posh actors. im sure he has a lot to say

elsa

5,992,328 次观看 • 3 年前

Arkansas farmer: We've got a real disaster with this tariff deal. We produce 40% more agricultural products in this country than we can consume domestically. 1 in every 4 rows of soybeans has to go abroad. China is the largest importer of soybeans in the world. For the first time in 20 years, China's bought zero soybeans and zero new crop corn out of this country. We went from $12 billion to zero.
1:31

Arkansas farmer: We've got a real disaster with this tariff deal. We produce 40% more agricultural products in this country than we can consume domestically. 1 in every 4 rows of soybeans has to go abroad. China is the largest importer of soybeans in the world. For the first time in 20 years, China's bought zero soybeans and zero new crop corn out of this country. We went from $12 billion to zero.

FactPost

1,345,168 次观看 • 10 个月前

LLMs/general agents still struggle to make sense of messy and complex Excel data. You can't easily dump all cells into the context window, and using the code interpreter is inefficient. LlamaSheets is one of my favorite releases from last year. We've embarked on an effort to build state-of-the-art algorithms and models to segment and parse complex Excel tables - including merged cells, hierarchical rows/columns. This includes both sheet-level and table-level understanding. We think there's a ton of use cases that this can help solve (simplest example: structuring your income/P&L/cash statements to be LLM-ready), and we'd love to get your feedback. Come check it out and let us know your thoughts! Sign up: Docs:
0:30

LLMs/general agents still struggle to make sense of messy and complex Excel data. You can't easily dump all cells into the context window, and using the code interpreter is inefficient. LlamaSheets is one of my favorite releases from last year. We've embarked on an effort to build state-of-the-art algorithms and models to segment and parse complex Excel tables - including merged cells, hierarchical rows/columns. This includes both sheet-level and table-level understanding. We think there's a ton of use cases that this can help solve (simplest example: structuring your income/P&L/cash statements to be LLM-ready), and we'd love to get your feedback. Come check it out and let us know your thoughts! Sign up: Docs:

Jerry Liu

30,162 次观看 • 6 个月前

😭😭😭 Impressive AF!!!!!!!!!!!!!!! Jin doing TRX Table Top Rows like it's absolutely nothing is seriously insane. This is one of the toughest suspension-training exercises. You have to keep your body in a strong tabletop position, hips lifted, core fully engaged while rowing your body up and maintaining perfect alignment throughout the movement. It challenges your back, shoulders, arms, and core all at once, and even standard TRX rows are difficult for most people. Meanwhile, Jin is knocking out rep after rep with excellent form, following every coaching cue ("lift your hips like a bridge," keep your gaze up, etc.) and still managing to smile through it all. Sergeant Kim Seokjin era clearly never left. 💪✨ Who else is impressed? Jin never misses. I love you, Jin. 😘 As a gym rat, watching him make one of the hardest TRX variations look effortless is honestly ridiculous.💪😆 NO JIN NO LIFE 🩷 #JIN #방탄소년단진 #진
0:32

😭😭😭 Impressive AF!!!!!!!!!!!!!!! Jin doing TRX Table Top Rows like it's absolutely nothing is seriously insane. This is one of the toughest suspension-training exercises. You have to keep your body in a strong tabletop position, hips lifted, core fully engaged while rowing your body up and maintaining perfect alignment throughout the movement. It challenges your back, shoulders, arms, and core all at once, and even standard TRX rows are difficult for most people. Meanwhile, Jin is knocking out rep after rep with excellent form, following every coaching cue ("lift your hips like a bridge," keep your gaze up, etc.) and still managing to smile through it all. Sergeant Kim Seokjin era clearly never left. 💪✨ Who else is impressed? Jin never misses. I love you, Jin. 😘 As a gym rat, watching him make one of the hardest TRX variations look effortless is honestly ridiculous.💪😆 NO JIN NO LIFE 🩷 #JIN #방탄소년단진 #진

🎸JIN RADIO TASKS

35,081 次观看 • 1 个月前

i bet you haven't seen a SO101 mounted on a wall like this before if you want to do the same, here is LeSlider: i built it cause i wanted something that can cover my whole desk for tasks like organizing and cleaning i originally wanted to have a belt system like what 3D printers have, but i was too lazy and used a pinion/track with another sts3215 so: > the extra motor shares the same bus as the rest of SO101 > you can have arbitrary length of track > really cheap and easy to assemble and control it turned out better than expected with this, i'm gonna train a model to pick random stuff up across my table and put it into a bin at the end of table (realistically using yolo to scan table, two policies, one for picking up objects, one for dropping)
0:26

i bet you haven't seen a SO101 mounted on a wall like this before if you want to do the same, here is LeSlider: i built it cause i wanted something that can cover my whole desk for tasks like organizing and cleaning i originally wanted to have a belt system like what 3D printers have, but i was too lazy and used a pinion/track with another sts3215 so: > the extra motor shares the same bus as the rest of SO101 > you can have arbitrary length of track > really cheap and easy to assemble and control it turned out better than expected with this, i'm gonna train a model to pick random stuff up across my table and put it into a bin at the end of table (realistically using yolo to scan table, two policies, one for picking up objects, one for dropping)

Binh Pham

98,324 次观看 • 2 个月前

Big moment for Postgres! AI coding tools have been surprisingly bad at writing Postgres code. Not because the models are dumb, but because of how they learned SQL in the first place. LLMs are trained on the internet, which is full of outdated Stack Overflow answers and quick-fix tutorials. So when you ask an AI to generate a schema, it gives you something that technically runs but misses decades of Postgres evolution, like: - No GENERATED ALWAYS AS IDENTITY (added in PG10) - No expression or partial indexes - No NULLS NOT DISTINCT (PG15) - Missing CHECK constraints and proper foreign keys - Generic naming that tells you nothing But this is actually a solvable problem. You can teach AI tools to write better Postgres by giving them access to the right documentation at inference time. This exact solution is actually implemented in the newly released pg-aiguide by Tiger Data - Creators of TimescaleDB, which is an open-source MCP server that provides coding tools access to 35 years of Postgres expertise. In a gist, the MCP server enables: - Semantic search over the official PostgreSQL manual (version-aware, so it knows PG14 vs PG17 differences) - Curated skills with opinionated best practices for schema design, indexing, and constraints. I ran an experiment with Claude Code to see how well this works, and worked with the team to put this together. Prompt: "Generate a schema for an e-commerce site twice, one with the MCP server disabled, one with it enabled. Finally, run an assessment to compare the generated schemas." The run with the MCP server led to: - 420% more indexes (including partial and expression indexes) - 235% more constraints - 60% more tables (proper normalization) - 11 automation functions and triggers - Modern PG17 patterns throughout The MCP-assisted schema had proper data integrity, performance optimizations baked in, and followed naming conventions that actually make sense in production. pg-aiguide works with Claude Code, Cursor, VS Code, and any MCP-compatible tool. It's free and fully open source. I have shared the repo in the replies!
0:25

Big moment for Postgres! AI coding tools have been surprisingly bad at writing Postgres code. Not because the models are dumb, but because of how they learned SQL in the first place. LLMs are trained on the internet, which is full of outdated Stack Overflow answers and quick-fix tutorials. So when you ask an AI to generate a schema, it gives you something that technically runs but misses decades of Postgres evolution, like: - No GENERATED ALWAYS AS IDENTITY (added in PG10) - No expression or partial indexes - No NULLS NOT DISTINCT (PG15) - Missing CHECK constraints and proper foreign keys - Generic naming that tells you nothing But this is actually a solvable problem. You can teach AI tools to write better Postgres by giving them access to the right documentation at inference time. This exact solution is actually implemented in the newly released pg-aiguide by Tiger Data - Creators of TimescaleDB, which is an open-source MCP server that provides coding tools access to 35 years of Postgres expertise. In a gist, the MCP server enables: - Semantic search over the official PostgreSQL manual (version-aware, so it knows PG14 vs PG17 differences) - Curated skills with opinionated best practices for schema design, indexing, and constraints. I ran an experiment with Claude Code to see how well this works, and worked with the team to put this together. Prompt: "Generate a schema for an e-commerce site twice, one with the MCP server disabled, one with it enabled. Finally, run an assessment to compare the generated schemas." The run with the MCP server led to: - 420% more indexes (including partial and expression indexes) - 235% more constraints - 60% more tables (proper normalization) - 11 automation functions and triggers - Modern PG17 patterns throughout The MCP-assisted schema had proper data integrity, performance optimizations baked in, and followed naming conventions that actually make sense in production. pg-aiguide works with Claude Code, Cursor, VS Code, and any MCP-compatible tool. It's free and fully open source. I have shared the repo in the replies!

Avi Chawla

186,931 次观看 • 6 个月前

Most people think they have a strong back because they can move big weight on barbell rows. But in reality? They are just efficient at cheating. And the barbell row is arguably the easiest exercise to fake. You can add a little hip swing, use a bit of momentum, and suddenly you’re moving heavy weight. You feel strong, but your lats are barely working. That’s why I program technique refinement phases like this one to force a reality check. I’m not interested in just moving weight from A to B. I want maximum efficiency per rep. So, I swap the heavy loads for this variation: Iso-Hold Rows. The concept is simple: Pull and hold for 2-3 seconds. When you introduce that pause, momentum dies. You can’t bounce the weight. You have to physically own it at the hardest part of the movement. It’s the ultimate quality control for your back training. If you can’t hold the squeeze for 2 seconds, you aren’t controlling the weight. The momentum is. Control first. Load second.
0:37

Most people think they have a strong back because they can move big weight on barbell rows. But in reality? They are just efficient at cheating. And the barbell row is arguably the easiest exercise to fake. You can add a little hip swing, use a bit of momentum, and suddenly you’re moving heavy weight. You feel strong, but your lats are barely working. That’s why I program technique refinement phases like this one to force a reality check. I’m not interested in just moving weight from A to B. I want maximum efficiency per rep. So, I swap the heavy loads for this variation: Iso-Hold Rows. The concept is simple: Pull and hold for 2-3 seconds. When you introduce that pause, momentum dies. You can’t bounce the weight. You have to physically own it at the hardest part of the movement. It’s the ultimate quality control for your back training. If you can’t hold the squeeze for 2 seconds, you aren’t controlling the weight. The momentum is. Control first. Load second.

Dr Neha Chawla || FreeGym

41,559 次观看 • 5 个月前

Soybean farmer reacts to Trump’s bailouts: "A government payment is nothing more than throwing a dollar bill on a spilled glass of milk on your kitchen table. It's designed to not make us whole as farmers, and that's the biggest misnomer of all. We're lucky if we get 3 cents on the dollar for a program that is designed to make up for a man-made disaster. This is a man-made disaster. This is caused by this administration and their actions"
1:21

Soybean farmer reacts to Trump’s bailouts: "A government payment is nothing more than throwing a dollar bill on a spilled glass of milk on your kitchen table. It's designed to not make us whole as farmers, and that's the biggest misnomer of all. We're lucky if we get 3 cents on the dollar for a program that is designed to make up for a man-made disaster. This is a man-made disaster. This is caused by this administration and their actions"

FactPost

585,119 次观看 • 9 个月前

A player in Where Winds Meet made a Keanu Reeves character and played See You Again with Kong Hou. Yes, you can basically do anything in this Wuxia open world RPG with MMO features. For those curious, Kong Hou is an Chinese harp-like instrument from the Tang Dynasty. There are 2 versions, one is the modern Kong Hou, which is basically a western harp but with 2 rows of strings. The traditional Tang dynasty version looks similar to the one in this video with a single row of strings. The strings are attached to the hollow structure at the top of the Kong Hou, which creates a very unique sound.
3:49

A player in Where Winds Meet made a Keanu Reeves character and played See You Again with Kong Hou. Yes, you can basically do anything in this Wuxia open world RPG with MMO features. For those curious, Kong Hou is an Chinese harp-like instrument from the Tang Dynasty. There are 2 versions, one is the modern Kong Hou, which is basically a western harp but with 2 rows of strings. The traditional Tang dynasty version looks similar to the one in this video with a single row of strings. The strings are attached to the hollow structure at the top of the Kong Hou, which creates a very unique sound.

Zhao DaShuai 东北进修🇨🇳 Commentary

230,270 次观看 • 7 个月前

🔥 This is not your average barbecue. Instead of using a regular grill, this man prepared enough food to feed around 1,000 people using a traditional cooking method. He arranged rows of ribs in a huge circle, surrounded them with firewood, and lit the entire setup. The fire burned slowly through the night, allowing the meat to cook low and slow for hours. By morning, the ribs were so tender they were practically falling apart. Watching this incredible tradition come to life is amazing. The amount of time, effort, and skill that goes into preparing a meal on this scale is truly something special. 🍖🔥
0:28

🔥 This is not your average barbecue. Instead of using a regular grill, this man prepared enough food to feed around 1,000 people using a traditional cooking method. He arranged rows of ribs in a huge circle, surrounded them with firewood, and lit the entire setup. The fire burned slowly through the night, allowing the meat to cook low and slow for hours. By morning, the ribs were so tender they were practically falling apart. Watching this incredible tradition come to life is amazing. The amount of time, effort, and skill that goes into preparing a meal on this scale is truly something special. 🍖🔥

FinleyFinn

1,129,228 次观看 • 4 天前