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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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212,291 views • 4 months ago •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?
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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?

Zach Wilson

34,452 views • 3 months ago

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

79,681 views • 1 year ago

Galaxy Z Fold8 has a very useful feature: app-level display customization. For example, YouTube normally shows only two rows of video recommendations on the screen. After reducing the display size, it can show three rows of videos at the same time, allowing me to browse more content without constantly scrolling. This is one of the advantages of Samsung’s foldable experience. Samsung always gives users more control and more customization options, allowing each app to be displayed in the way that best fits their usage habits. A larger screen is not just about making everything bigger. It is about giving users more freedom to decide how they want to use that space.
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Galaxy Z Fold8 has a very useful feature: app-level display customization. For example, YouTube normally shows only two rows of video recommendations on the screen. After reducing the display size, it can show three rows of videos at the same time, allowing me to browse more content without constantly scrolling. This is one of the advantages of Samsung’s foldable experience. Samsung always gives users more control and more customization options, allowing each app to be displayed in the way that best fits their usage habits. A larger screen is not just about making everything bigger. It is about giving users more freedom to decide how they want to use that space.

Ice Universe

89,842 views • 1 month ago

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
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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 views • 1 year ago

Qwen3.8-Flash-Next now fits on a 48gb macbook air Got q4 running on MLX using only 39gb of memory and decoding at 28 tok/s 🔥 Also q8 running on just 50gb of memory I stacked two tricks so far: 1. REAP pruning cut the expert pool from 68gb to 35gb 2. the 51b n-gram table now lives on the ssd instead of ram My patch memmaps the shards and reads ~100 bytes per lookup straight off disk, dequantized on cpu and verified bit-exact against the gpu path Qwen3.8-Flash-Next at 288 experts with its PLE table on nvme loads in 6 seconds, 39gb resident, 28 tok/s decode, 600 tok/s prefill. Logits identical to the ram-resident version in every way 97gb stock Q4 down to 68gb if pruned to 288 experts down to 39gb if pruned to 288 experts with the table on disk humaneval scoring 91.5% with 288 experts vs. 93.9% for stock I'm not done. next: 1. MTP drafter for speculative decode 2. n-gram lookup drafter both are built, benchmarking them now
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Qwen3.8-Flash-Next now fits on a 48gb macbook air Got q4 running on MLX using only 39gb of memory and decoding at 28 tok/s 🔥 Also q8 running on just 50gb of memory I stacked two tricks so far: 1. REAP pruning cut the expert pool from 68gb to 35gb 2. the 51b n-gram table now lives on the ssd instead of ram My patch memmaps the shards and reads ~100 bytes per lookup straight off disk, dequantized on cpu and verified bit-exact against the gpu path Qwen3.8-Flash-Next at 288 experts with its PLE table on nvme loads in 6 seconds, 39gb resident, 28 tok/s decode, 600 tok/s prefill. Logits identical to the ram-resident version in every way 97gb stock Q4 down to 68gb if pruned to 288 experts down to 39gb if pruned to 288 experts with the table on disk humaneval scoring 91.5% with 288 experts vs. 93.9% for stock I'm not done. next: 1. MTP drafter for speculative decode 2. n-gram lookup drafter both are built, benchmarking them now

Eyal Toledano

10,362 views • 12 days ago

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 TimescaleDB (by Tiger Data) 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 TimescaleDB (by Tiger Data) team for partnering with me on this post!

Santiago

110,133 views • 1 year ago

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⭐️

65,889 views • 7 months ago

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

16,293 views • 1 year ago

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 views • 2 years ago

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 views • 1 year ago

Gilbert Strang, the legendary mathematician who taught linear algebra for 61 years and became the most watched math professor in history: "I used to think a matrix was just a grid of numbers, until I proved that its rows and columns always agree on one number no matter how you look at them. That fact still feels like magic to me after sixty years." this is the exact proof sitting quietly underneath every factor model a risk desk trusts with real capital, and almost nobody outside a math department has ever seen it. strip away the notation and the idea is almost absurdly simple. take any matrix, any grid of numbers, and count how many of its rows are truly independent, meaning none of them can be built out of the others. now count the independent columns instead, a completely different question on the surface. those two numbers, row independence and column independence, always turn out exactly equal, no matter how large or lopsided the matrix is. nobody presenting a clean risk model out loud credits a decades old proof for the reason the math even holds together. zoom out to what this means for anything built on a grid of numbers today. a portfolio, a covariance matrix, a neural network's weights, all of them hide a true dimension smaller than their size suggests, and that hidden number is exactly what this proof pins down. the industry sells complexity as scale, more assets, more parameters, more rows and columns. but the real question was never how big the matrix is. it's how many independent directions are actually hiding inside it. the size of the grid was never the real story. it was the one number both sides of it were quietly agreeing on the whole time.
41:55

Gilbert Strang, the legendary mathematician who taught linear algebra for 61 years and became the most watched math professor in history: "I used to think a matrix was just a grid of numbers, until I proved that its rows and columns always agree on one number no matter how you look at them. That fact still feels like magic to me after sixty years." this is the exact proof sitting quietly underneath every factor model a risk desk trusts with real capital, and almost nobody outside a math department has ever seen it. strip away the notation and the idea is almost absurdly simple. take any matrix, any grid of numbers, and count how many of its rows are truly independent, meaning none of them can be built out of the others. now count the independent columns instead, a completely different question on the surface. those two numbers, row independence and column independence, always turn out exactly equal, no matter how large or lopsided the matrix is. nobody presenting a clean risk model out loud credits a decades old proof for the reason the math even holds together. zoom out to what this means for anything built on a grid of numbers today. a portfolio, a covariance matrix, a neural network's weights, all of them hide a true dimension smaller than their size suggests, and that hidden number is exactly what this proof pins down. the industry sells complexity as scale, more assets, more parameters, more rows and columns. but the real question was never how big the matrix is. it's how many independent directions are actually hiding inside it. the size of the grid was never the real story. it was the one number both sides of it were quietly agreeing on the whole time.

MindArch

18,494 views • 1 month ago

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

5,993,098 views • 3 years ago

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

1,345,565 views • 11 months ago

February 1971. State House Entebbe. On the gardens before the grand residence, party was underway. Former monarchs and royal representatives, from Buganda, Toro, Ankole, Bunyoro, sat in rows of chairs. Before them, a table draped in white cloth. Behind it sat Idi Amin, an army officer, and a few white dignitaries. The press stood to the side. Traditional dancers moved in front. The scene was carefully arranged. Amin, chest out, swaggering with the confidence of a man recently crowned by force, sat behind the table with the microphone. Beside him, an army officer and a handful of white dignitaries, lingering evidence of the old colonial networks that still hovered around Uganda's new power. The table was draped in white cloth, and the microphone stood at its centre, waiting to carry his words to the nation beyond the gates. On either side of the table, in neat rows of chairs, sat the remnants of Uganda's ancient dynasties. From Buganda came Prince Badru Kakungulu, Prince Mawanda, and Princess Mazzi, along with Nnabagereka of Buganda, widow of the exiled Kabaka. From Toro came the aged former Omukama, and from Ankole and Bunyoro came their own representatives, princes and elders who had once presided over proud kingdoms. They had come hoping for something. Perhaps restoration. Perhaps recognition. Perhaps simply the assurance that their place in Uganda's story had not been erased entirely. In front of the seated dignitaries, traditional dancers from various regions twirled and stamped, their drums beating out rhythms that predated the colonial era itself. The press stood to the side, cameras clicking, recording every moment for the world outside. It was a spectacle designed to project unity, a gathering of cultures, a celebration of heritage. Then Captain Ochima rose and read the soldiers' declaration. Then Amin himself leaned into the microphone. The message was swift and absolute. Uganda would remain a Republic. The kingdoms would not return. In one gesture, he dismissed both the colonial constitutional compromises and Obote's revolutionary centralism. As he would later declare, "there can be no return of feudal kings or kingdoms, because Uganda is going forward, not backward." The royal representatives listened in silence. They had been honoured, but not restored. Amin had called them to Entebbe not to return their crowns, but to remind them, and the nation, that only one seat of power remained. The message had been delivered. #ughistory Government of Uganda #IdiAmin
1:16

February 1971. State House Entebbe. On the gardens before the grand residence, party was underway. Former monarchs and royal representatives, from Buganda, Toro, Ankole, Bunyoro, sat in rows of chairs. Before them, a table draped in white cloth. Behind it sat Idi Amin, an army officer, and a few white dignitaries. The press stood to the side. Traditional dancers moved in front. The scene was carefully arranged. Amin, chest out, swaggering with the confidence of a man recently crowned by force, sat behind the table with the microphone. Beside him, an army officer and a handful of white dignitaries, lingering evidence of the old colonial networks that still hovered around Uganda's new power. The table was draped in white cloth, and the microphone stood at its centre, waiting to carry his words to the nation beyond the gates. On either side of the table, in neat rows of chairs, sat the remnants of Uganda's ancient dynasties. From Buganda came Prince Badru Kakungulu, Prince Mawanda, and Princess Mazzi, along with Nnabagereka of Buganda, widow of the exiled Kabaka. From Toro came the aged former Omukama, and from Ankole and Bunyoro came their own representatives, princes and elders who had once presided over proud kingdoms. They had come hoping for something. Perhaps restoration. Perhaps recognition. Perhaps simply the assurance that their place in Uganda's story had not been erased entirely. In front of the seated dignitaries, traditional dancers from various regions twirled and stamped, their drums beating out rhythms that predated the colonial era itself. The press stood to the side, cameras clicking, recording every moment for the world outside. It was a spectacle designed to project unity, a gathering of cultures, a celebration of heritage. Then Captain Ochima rose and read the soldiers' declaration. Then Amin himself leaned into the microphone. The message was swift and absolute. Uganda would remain a Republic. The kingdoms would not return. In one gesture, he dismissed both the colonial constitutional compromises and Obote's revolutionary centralism. As he would later declare, "there can be no return of feudal kings or kingdoms, because Uganda is going forward, not backward." The royal representatives listened in silence. They had been honoured, but not restored. Amin had called them to Entebbe not to return their crowns, but to remind them, and the nation, that only one seat of power remained. The message had been delivered. #ughistory Government of Uganda #IdiAmin

UgHistory

35,040 views • 22 days ago

JupCallers Season 1 is live with up to $5,000 in prizes on the table! Making one call in Jupiter Discord puts your name on the board. From there, the whole community watches you climb and the callers at the top walk away with the cash. Entry is free. The pool grows as more callers qualify, up to 20 paid spots. Plus there’s a Call of the Week prize every single week. Details below 👇
0:35

JupCallers Season 1 is live with up to $5,000 in prizes on the table! Making one call in Jupiter Discord puts your name on the board. From there, the whole community watches you climb and the callers at the top walk away with the cash. Entry is free. The pool grows as more callers qualify, up to 20 paid spots. Plus there’s a Call of the Week prize every single week. Details below 👇

Jupiter

41,696 views • 25 days ago

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 views • 8 months ago

Excited to announce our next dinner! 🦄 Featuring Erica Jersin (Erica (Johnson) Jersin 鄭秀穎), the co-founder of Modern Health, a Y Combinator-backed company and one of the fastest ever female-founded companies to reach unicorn status 📍July 24 · 6:30–9:00 PM MOSA | San Francisco This night is for: >male and female Founders from Seed through Series B who are building in a season of growth, change, or momentum. 🥂 Your seat includes: > five-course dinner crafted by a Michelin trained chef >intentional founder table > an intimate conversation with Erica Jersin >not a lame networking event only 20 seats at the table. once the table is full, applications will close. applications are reviewed to keep the table thoughtful, relevant, and intentionally small. Excited for this one!
0:24

Excited to announce our next dinner! 🦄 Featuring Erica Jersin (Erica (Johnson) Jersin 鄭秀穎), the co-founder of Modern Health, a Y Combinator-backed company and one of the fastest ever female-founded companies to reach unicorn status 📍July 24 · 6:30–9:00 PM MOSA | San Francisco This night is for: >male and female Founders from Seed through Series B who are building in a season of growth, change, or momentum. 🥂 Your seat includes: > five-course dinner crafted by a Michelin trained chef >intentional founder table > an intimate conversation with Erica Jersin >not a lame networking event only 20 seats at the table. once the table is full, applications will close. applications are reviewed to keep the table thoughtful, relevant, and intentionally small. Excited for this one!

Alesya

28,561 views • 2 months ago

😭😭😭 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 views • 3 months ago

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

187,076 views • 8 months ago