Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

Visualizing a SQL query in 5 mins SQL statements are executed by the database system in several steps, including: - Parsing the SQL statement and checking its validity - Transforming the SQL into an internal representation, such as relational algebra - Optimizing the internal representation and creating an execution...

alexxubyte's profile picture

Alex Xu

293,223 subscribers

22,638 görüntüleme • 11 gün önce •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

GPT-5.6-Sol-Ultra built a Doom-like game in SQL: the game is running in the terminal on the left, while the SQL powering the game in the terminal on the right. How it works: DOOMQL is a game engine implemented in 2,000+ lines of SQL. Every frame, SQL raycasts the scene, calculates every RGB pixel, and encodes those pixels as coloured terminal characters. The same SQL then handles controls, movement, collision, enemy AI and combat, while Python connects SQLite to the keyboard, clock and terminal.
0:31

GPT-5.6-Sol-Ultra built a Doom-like game in SQL: the game is running in the terminal on the left, while the SQL powering the game in the terminal on the right. How it works: DOOMQL is a game engine implemented in 2,000+ lines of SQL. Every frame, SQL raycasts the scene, calculates every RGB pixel, and encodes those pixels as coloured terminal characters. The same SQL then handles controls, movement, collision, enemy AI and combat, while Python connects SQLite to the keyboard, clock and terminal.

Peter Gostev (SF 24-28 August)

122,063 görüntüleme • 1 ay önce

Introducing my SQL Agent: How to automate SQL with AI. Today I'll share how to make a SQL AI Agent that can automatically connect to databases, understand the tables and schema, and write and execute high-quality SQL queries. I'll guide you through setting up the SQL Agent, creating dozens of SQL queries, and returning the data from those queries. This AI is a huge help!! Table of Contents: 00:00 Introduction 01:30 How to Get The AI Data Science Agents 03:23 AI Tips Newsletter: Get The Code 04:00 Project Setup 06:30 Setting Up Your First SQL Agent 11:00 Running SQL Queries with the SQL Agent 14:25 Next Steps, Project Roadmap, & AI Bootcamp Github to AI Data Science Team (Army of Copilots): Get the Code by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, February 12th, I'm sharing one of my best AI Projects: SQL-Writing Business Intelligence Team Register here (570+ registered):
17:04

Introducing my SQL Agent: How to automate SQL with AI. Today I'll share how to make a SQL AI Agent that can automatically connect to databases, understand the tables and schema, and write and execute high-quality SQL queries. I'll guide you through setting up the SQL Agent, creating dozens of SQL queries, and returning the data from those queries. This AI is a huge help!! Table of Contents: 00:00 Introduction 01:30 How to Get The AI Data Science Agents 03:23 AI Tips Newsletter: Get The Code 04:00 Project Setup 06:30 Setting Up Your First SQL Agent 11:00 Running SQL Queries with the SQL Agent 14:25 Next Steps, Project Roadmap, & AI Bootcamp Github to AI Data Science Team (Army of Copilots): Get the Code by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, February 12th, I'm sharing one of my best AI Projects: SQL-Writing Business Intelligence Team Register here (570+ registered):

Matt Dancho (Business Science)

77,527 görüntüleme • 1 yıl önce

Can adding an index bring down the cost of that SQL query from $0.00000208 to $0.00000018? And how would you know? Aurora DSQL's new EXPLAIN ANALYZE VERBOSE feature tells you exactly how much a SQL query costs to run, so you can see exactly how tuning efforts affect your cost.
0:50

Can adding an index bring down the cost of that SQL query from $0.00000208 to $0.00000018? And how would you know? Aurora DSQL's new EXPLAIN ANALYZE VERBOSE feature tells you exactly how much a SQL query costs to run, so you can see exactly how tuning efforts affect your cost.

Marc Brooker

47,031 görüntüleme • 9 ay önce

"nooo you cant map government spending in a SQL database and allow everyone to use it, you dont have a degree" The beaver builds the dam regardless
0:27

"nooo you cant map government spending in a SQL database and allow everyone to use it, you dont have a degree" The beaver builds the dam regardless

Beaver 🦁

86,519 görüntüleme • 8 ay önce

Google open-sourced MCP Toolbox for Databases. I gave it access to everything else. For context, Google's MCP Toolbox for Databases is an open-source server that lets AI agents securely query structured databases like PostgreSQL and MySQL through the MCP protocol However, most enterprise knowledge doesn't actually live in databases. It's scattered across emails, Slack threads, GitHub repos, Salesforce records, customer reviews, and internal docs. So Agents can't see any of it, which means they're working with a fraction of the context they need. I fixed that using MindsDB. It acts as a universal SQL layer that sits on top of all your data sources: structured, semi-structured, and unstructured. This means you can query Salesforce, Gmail, GitHub, S3 files, Jira, and 200+ more sources using SQL syntax. The clever part is how it connects to the MCP Toolbox. MindsDB exposes everything through MySQL, so from the Agent's perspective, it's just running SQL and getting context back. It doesn't know or care that the data came from five different sources behind the scenes. This setup unlocks some powerful capabilities: → One SQL interface for dozens of enterprise sources → Cross-datasource joins (combine GitHub and CRM data in a single query) → Built-in ML capabilities for working with unstructured data → Simple MCP tools that now have massively expanded reach In the video below, the Agent queries GitHub data and a customer review database in one SQL query. So what used to require ETL pipelines and weeks of engineering effort now happens instantly. At the end of the day, AI agents are only as useful as the data they can access. This gives them a lot more to work with. I have shared the GitHub repo in the replies, where you can find more details about this.
0:22

Google open-sourced MCP Toolbox for Databases. I gave it access to everything else. For context, Google's MCP Toolbox for Databases is an open-source server that lets AI agents securely query structured databases like PostgreSQL and MySQL through the MCP protocol However, most enterprise knowledge doesn't actually live in databases. It's scattered across emails, Slack threads, GitHub repos, Salesforce records, customer reviews, and internal docs. So Agents can't see any of it, which means they're working with a fraction of the context they need. I fixed that using MindsDB. It acts as a universal SQL layer that sits on top of all your data sources: structured, semi-structured, and unstructured. This means you can query Salesforce, Gmail, GitHub, S3 files, Jira, and 200+ more sources using SQL syntax. The clever part is how it connects to the MCP Toolbox. MindsDB exposes everything through MySQL, so from the Agent's perspective, it's just running SQL and getting context back. It doesn't know or care that the data came from five different sources behind the scenes. This setup unlocks some powerful capabilities: → One SQL interface for dozens of enterprise sources → Cross-datasource joins (combine GitHub and CRM data in a single query) → Built-in ML capabilities for working with unstructured data → Simple MCP tools that now have massively expanded reach In the video below, the Agent queries GitHub data and a customer review database in one SQL query. So what used to require ETL pipelines and weeks of engineering effort now happens instantly. At the end of the day, AI agents are only as useful as the data they can access. This gives them a lot more to work with. I have shared the GitHub repo in the replies, where you can find more details about this.

Akshay 🚀

40,562 görüntüleme • 6 ay önce

Natural Language to SQL is really powerful. Built to let anyone query the census database with just natural language. "cities with a population over 100,000 and lowest crime" "highest income areas in california" code is open-source and linked below
0:35

Natural Language to SQL is really powerful. Built to let anyone query the census database with just natural language. "cities with a population over 100,000 and lowest crime" "highest income areas in california" code is open-source and linked below

rahul

567,427 görüntüleme • 3 yıl önce

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!
9:28

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Akshay 🚀

65,672 görüntüleme • 9 ay önce

Dear Friend, I wrote this book for you. For the past year, I have labored to create a product that will help you learn and master SQL. I have been there. I have felt the frustration of trying to learn SQL and not knowing where to begin. I have lived through the struggle of setting up a platform to run SQL queries. Most platforms require sign-ups and logins that create a headache for learners. I also know the challenge of finding proper SQL exercises that mirror the real-world experience of a data analyst. Yes, I have been in your shoes. That’s why I created SQL Essentials for Data Analysis: A 50-Day Hands-on Challenge Book (Go From Beginner to Pro). Yes, to give you a clear, practical path from beginner to confident SQL user. ✅Why SQL Still Matters You may be wondering if SQL still matters in 2025. The answer: it has never mattered more. SQL is the lingua franca of data. Data still lives in databases, and the only language it truly understands is SQL. Think about it, even in Python, SQL is there. You’ve probably heard about the powerful pandas library. Guess what? It also has some SQL. And don’t get me started on BigQuery, Tableau, Power BI, and Databricks; the answer is the same: they all rely on SQL. SQL is the big shadow that hovers over everything data. This is why learning SQL is a must for data analysts, engineers, scientists, and anyone working with data. SQL connects everything: exploration, extraction, transformation, modeling, validation, and reporting. ✅Why I Wrote This Book Dear friend, I wanted to create a resource that gives you everything you need to learn SQL for data analysis. Quite often, resources are scattered across different places. You might learn theory in one place, search for datasets in another, and hunt for questions somewhere else. More often than not, the only place you can tackle SQL challenges is online. But online platforms usually focus on syntax and don’t reflect the messiness of real-world data. I wrote this book to give you the best of both worlds: theory and practice. I don’t want you to be worrying about where to find resources. I want you to focus only on learning SQL. If you are new to SQL or need a refresher on the fundamentals, Part 1 of the book has you covered. If you are looking for practice, Part 2 is 49 days of hands-on SQL challenges designed to mirror real-world tasks. Each day in the book is designed to feel like a mini project, rather than isolated exercises. Take Day 15: Standardize Climbers Data, for example: On this day, you’re not just writing a single query; you’re working with a dataset from start to finish. By combining these tasks, you experience a full data preprocessing workflow, just like a real project. You get to practice loading, transforming, cleaning, and validating data, all in one challenge. This approach makes every day a hands-on project, not just an isolated query. You’re learning how SQL is used in real-world scenarios, not just memorizing syntax. By the end of each day, you’ve solved a problem that feels meaningful and practical: yes, something that mirrors data analysts’ and engineers’ work in real life. In this book I use SQLite. I chose SQLite because it’s simple, lightweight, and runs on any system without complicated setups or cloud accounts. You don’t need to worry about complex configurations. SQLite allows you to focus entirely on learning SQL concepts, queries, and logic without distractions. You will just have to import it. I also structured the book for use in Jupyter or Google Colab notebooks. These are playgrounds for data analysts, engineers, and scientists. These environments are interactive and flexible. They let you run queries, visualize results, and experiment in real time. Using notebooks ensures that you can practice SQL while documenting your work and learning at your own pace, all in one place. No need for sign-ups. ✅Why 50 Days? I chose 50 days intentionally. Learning SQL isn’t a sprint; it’s a habit. You can’t truly master a language by cramming a few queries in one sitting. 50 days creates a commitment. You attach yourself to a goal, a tangible outcome. Every day is a small win, a step forward, and by the end of the journey, you’ve transformed your understanding of SQL. By spreading the learning over 50 days, you build momentum, consistency, and confidence. Think of it like training for a marathon. You don’t run 26 miles on the first day. You run a little each day, gradually building strength, endurance, and skill. By the end of the 50 days, you’ll have tackled a wide range of SQL tasks: from simple filtering to window functions, date operations, joins, and performance tuning. You’ll have not just learned SQL but truly internalized it. The goal isn’t to overwhelm you. It’s to give you a structured, achievable path that fits into your daily routine, so learning SQL becomes natural, steady, and rewarding. Even if you don’t finish within 50 days, the 50-day structure gives you a rhythm, a habit, and a sense of accomplishment. The kind of outcome that sticks long after the book is finished. In summary, I wrote the book to address these pain points: 🔶Not knowing where to start: The book gives you a clear roadmap that guides you day by day. 🔶Too much theory, not enough practice: Reading about SQL is not the same as doing SQL. This book includes hands-on challenges that mirror real-world scenarios, so you’re not just memorizing commands; you’re learning to think like a data analyst. 🔶Complex setup: Many learners get stuck setting up databases or configuring environments. You will not worry about complex setups; everything runs in SQLite3 inside Jupyter Notebook, so you start immediately. 🔶Disconnected learning: The challenges mirror real-world analytics problems. Every day here is like a mini project, giving you the experience of exploring, cleaning, transforming, and analyzing data ✅What I ask of You I wrote this book for you because I want you to succeed, but books alone don’t create mastery; your effort does. I have provided the tools. All I ask is that you show up every day. Even if it’s just 20–30 minutes, take the challenge seriously. Tackle the problems, experiment with your queries, make mistakes, and fix them. That’s how real learning happens. I also ask that you trust the process. The book is designed to guide you from beginner to confident SQL user, step by step. Some days will feel "easy" and others "hard." Stay the course, and by the end, you’ll see how all the pieces fit together. Finally, I ask that you bring curiosity and persistence. SQL is a language of logic and structure, but it’s also a language of insight. The more you explore, the more patterns you’ll discover, and the more confident you’ll become in solving real-world problems. Don’t be scared to experiment. If you commit to this, I promise you’ll finish 50 days with more than just knowledge. You’ll have the skills, confidence, and habit of thinking like a data analyst. To make starting even easier, as a subscriber to this newsletter, I’m giving you an exclusive 35% launch discount. You can grab your copy today and start the 50-day journey at a reduced price. Grab SQL Essentials for Data Analysis here: I can’t wait to hear about your progress, the insights you uncover, and the confidence you gain along the way. If you have any questions, feel free to reach out to me or post them in the comments section. Let’s start this journey together: one challenge, one query, one day at a time. Warmly, Benjamin PS. Please repost.
2:03

Dear Friend, I wrote this book for you. For the past year, I have labored to create a product that will help you learn and master SQL. I have been there. I have felt the frustration of trying to learn SQL and not knowing where to begin. I have lived through the struggle of setting up a platform to run SQL queries. Most platforms require sign-ups and logins that create a headache for learners. I also know the challenge of finding proper SQL exercises that mirror the real-world experience of a data analyst. Yes, I have been in your shoes. That’s why I created SQL Essentials for Data Analysis: A 50-Day Hands-on Challenge Book (Go From Beginner to Pro). Yes, to give you a clear, practical path from beginner to confident SQL user. ✅Why SQL Still Matters You may be wondering if SQL still matters in 2025. The answer: it has never mattered more. SQL is the lingua franca of data. Data still lives in databases, and the only language it truly understands is SQL. Think about it, even in Python, SQL is there. You’ve probably heard about the powerful pandas library. Guess what? It also has some SQL. And don’t get me started on BigQuery, Tableau, Power BI, and Databricks; the answer is the same: they all rely on SQL. SQL is the big shadow that hovers over everything data. This is why learning SQL is a must for data analysts, engineers, scientists, and anyone working with data. SQL connects everything: exploration, extraction, transformation, modeling, validation, and reporting. ✅Why I Wrote This Book Dear friend, I wanted to create a resource that gives you everything you need to learn SQL for data analysis. Quite often, resources are scattered across different places. You might learn theory in one place, search for datasets in another, and hunt for questions somewhere else. More often than not, the only place you can tackle SQL challenges is online. But online platforms usually focus on syntax and don’t reflect the messiness of real-world data. I wrote this book to give you the best of both worlds: theory and practice. I don’t want you to be worrying about where to find resources. I want you to focus only on learning SQL. If you are new to SQL or need a refresher on the fundamentals, Part 1 of the book has you covered. If you are looking for practice, Part 2 is 49 days of hands-on SQL challenges designed to mirror real-world tasks. Each day in the book is designed to feel like a mini project, rather than isolated exercises. Take Day 15: Standardize Climbers Data, for example: On this day, you’re not just writing a single query; you’re working with a dataset from start to finish. By combining these tasks, you experience a full data preprocessing workflow, just like a real project. You get to practice loading, transforming, cleaning, and validating data, all in one challenge. This approach makes every day a hands-on project, not just an isolated query. You’re learning how SQL is used in real-world scenarios, not just memorizing syntax. By the end of each day, you’ve solved a problem that feels meaningful and practical: yes, something that mirrors data analysts’ and engineers’ work in real life. In this book I use SQLite. I chose SQLite because it’s simple, lightweight, and runs on any system without complicated setups or cloud accounts. You don’t need to worry about complex configurations. SQLite allows you to focus entirely on learning SQL concepts, queries, and logic without distractions. You will just have to import it. I also structured the book for use in Jupyter or Google Colab notebooks. These are playgrounds for data analysts, engineers, and scientists. These environments are interactive and flexible. They let you run queries, visualize results, and experiment in real time. Using notebooks ensures that you can practice SQL while documenting your work and learning at your own pace, all in one place. No need for sign-ups. ✅Why 50 Days? I chose 50 days intentionally. Learning SQL isn’t a sprint; it’s a habit. You can’t truly master a language by cramming a few queries in one sitting. 50 days creates a commitment. You attach yourself to a goal, a tangible outcome. Every day is a small win, a step forward, and by the end of the journey, you’ve transformed your understanding of SQL. By spreading the learning over 50 days, you build momentum, consistency, and confidence. Think of it like training for a marathon. You don’t run 26 miles on the first day. You run a little each day, gradually building strength, endurance, and skill. By the end of the 50 days, you’ll have tackled a wide range of SQL tasks: from simple filtering to window functions, date operations, joins, and performance tuning. You’ll have not just learned SQL but truly internalized it. The goal isn’t to overwhelm you. It’s to give you a structured, achievable path that fits into your daily routine, so learning SQL becomes natural, steady, and rewarding. Even if you don’t finish within 50 days, the 50-day structure gives you a rhythm, a habit, and a sense of accomplishment. The kind of outcome that sticks long after the book is finished. In summary, I wrote the book to address these pain points: 🔶Not knowing where to start: The book gives you a clear roadmap that guides you day by day. 🔶Too much theory, not enough practice: Reading about SQL is not the same as doing SQL. This book includes hands-on challenges that mirror real-world scenarios, so you’re not just memorizing commands; you’re learning to think like a data analyst. 🔶Complex setup: Many learners get stuck setting up databases or configuring environments. You will not worry about complex setups; everything runs in SQLite3 inside Jupyter Notebook, so you start immediately. 🔶Disconnected learning: The challenges mirror real-world analytics problems. Every day here is like a mini project, giving you the experience of exploring, cleaning, transforming, and analyzing data ✅What I ask of You I wrote this book for you because I want you to succeed, but books alone don’t create mastery; your effort does. I have provided the tools. All I ask is that you show up every day. Even if it’s just 20–30 minutes, take the challenge seriously. Tackle the problems, experiment with your queries, make mistakes, and fix them. That’s how real learning happens. I also ask that you trust the process. The book is designed to guide you from beginner to confident SQL user, step by step. Some days will feel "easy" and others "hard." Stay the course, and by the end, you’ll see how all the pieces fit together. Finally, I ask that you bring curiosity and persistence. SQL is a language of logic and structure, but it’s also a language of insight. The more you explore, the more patterns you’ll discover, and the more confident you’ll become in solving real-world problems. Don’t be scared to experiment. If you commit to this, I promise you’ll finish 50 days with more than just knowledge. You’ll have the skills, confidence, and habit of thinking like a data analyst. To make starting even easier, as a subscriber to this newsletter, I’m giving you an exclusive 35% launch discount. You can grab your copy today and start the 50-day journey at a reduced price. Grab SQL Essentials for Data Analysis here: I can’t wait to hear about your progress, the insights you uncover, and the confidence you gain along the way. If you have any questions, feel free to reach out to me or post them in the comments section. Let’s start this journey together: one challenge, one query, one day at a time. Warmly, Benjamin PS. Please repost.

Benjamin Bennett Alexander

16,883 görüntüleme • 9 ay önce

Your AI work assistant should understand you. Ask Amazon Quick a question in plain English. Quick translates the prompt into a query across your CRM, pipeline tables, and dashboards, then returns the answer. Pipeline information, ranked leads, and account insights are delivered in seconds. No SQL, no filters, no pivots, no BI tool expertise required.
0:47

Your AI work assistant should understand you. Ask Amazon Quick a question in plain English. Quick translates the prompt into a query across your CRM, pipeline tables, and dashboards, then returns the answer. Pipeline information, ranked leads, and account insights are delivered in seconds. No SQL, no filters, no pivots, no BI tool expertise required.

Amazon Web Services

8,787,033 görüntüleme • 3 ay önce

We built a SQL and NoSQL co-pilot: Generate an answer with accompanying code by typing a question to ask your database - built in code editor - AI sanity check - schema explorer - shareable perma-links to past queries Join the private beta here:
0:34

We built a SQL and NoSQL co-pilot: Generate an answer with accompanying code by typing a question to ask your database - built in code editor - AI sanity check - schema explorer - shareable perma-links to past queries Join the private beta here:

Use Bink

74,965 görüntüleme • 3 yıl önce

Great to see this formalized Fable 5 is really great at refactoring / optimizing PRs, but often I want to understand why Rather than asking follow ups at $10 / $50 per million tokens, I hand these off to Cursor Composer 2.5 (5% of the cost) Recently, I asked Fable 5 to refactor a PR - I noticed it made a large change to a SQL statement and added a CTE I handed off a transcript of the chat to Composer and asked "Why did you change the SQL statement for the queued message updates?" Fable 5 works while the Cursor harness explains The same thing can be done with a plan + implement approach (Fable 5 -> GPT 5.5) Since 5.5 is ~50% cheaper, the math still holds
0:48

Great to see this formalized Fable 5 is really great at refactoring / optimizing PRs, but often I want to understand why Rather than asking follow ups at $10 / $50 per million tokens, I hand these off to Cursor Composer 2.5 (5% of the cost) Recently, I asked Fable 5 to refactor a PR - I noticed it made a large change to a SQL statement and added a CTE I handed off a transcript of the chat to Composer and asked "Why did you change the SQL statement for the queued message updates?" Fable 5 works while the Cursor harness explains The same thing can be done with a plan + implement approach (Fable 5 -> GPT 5.5) Since 5.5 is ~50% cheaper, the math still holds

matt palmer

22,035 görüntüleme • 2 ay önce

This is a very clever idea to use an LLM with your SQL data. SQL + AI has been tried before, but one of the best parts of this solution is getting the same exact OpenAI's completion API. In other words: You are now talking to an LLM that knows everything about your database and you don't even notice it! I recorded a 2-min video to show you how it works. Thanks to the team for the collaboration, listening to my feedback, and helping me understand what they built. Go to to try this yourself.
2:07

This is a very clever idea to use an LLM with your SQL data. SQL + AI has been tried before, but one of the best parts of this solution is getting the same exact OpenAI's completion API. In other words: You are now talking to an LLM that knows everything about your database and you don't even notice it! I recorded a 2-min video to show you how it works. Thanks to the team for the collaboration, listening to my feedback, and helping me understand what they built. Go to to try this yourself.

Santiago

230,503 görüntüleme • 2 yıl önce

Prisma Next (soon Prisma 8) now lets you inspect a database’s applied migration history in Prisma Studio, with the schema changes and exact SQL that ran.
0:43

Prisma Next (soon Prisma 8) now lets you inspect a database’s applied migration history in Prisma Studio, with the schema changes and exact SQL that ran.

Prisma

10,815 görüntüleme • 1 ay önce

You can work with large SQL scripts easily in DBeaver using the Outline panel. The panel displays the full structure of your script, including all data types, and lets you quickly navigate between statements. This feature is available in desktop editions starting from version 24.0.
0:26

You can work with large SQL scripts easily in DBeaver using the Outline panel. The panel displays the full structure of your script, including all data types, and lets you quickly navigate between statements. This feature is available in desktop editions starting from version 24.0.

DBeaver

19,210 görüntüleme • 9 ay önce

New short course: Building Your Own Database Agent, created with Microsoft Microsoft Azure's Adrián González Sánchez (He/Him/His). You'll learn to build an AI assistant that translates natural language questions into SQL queries. Querying with natural language empowers everyone in your organization, from business leaders to developers, to access data insights directly, using plain English. You’ll use the Azure OpenAI Service and LangChain to implement retrieval augmented generation and function calling, and leverage the Assistants API. You'll gain hands-on experience building an agent that can reason over both CSV files and SQL databases. Please sign up here!
2:40

New short course: Building Your Own Database Agent, created with Microsoft Microsoft Azure's Adrián González Sánchez (He/Him/His). You'll learn to build an AI assistant that translates natural language questions into SQL queries. Querying with natural language empowers everyone in your organization, from business leaders to developers, to access data insights directly, using plain English. You’ll use the Azure OpenAI Service and LangChain to implement retrieval augmented generation and function calling, and leverage the Assistants API. You'll gain hands-on experience building an agent that can reason over both CSV files and SQL databases. Please sign up here!

Andrew Ng

109,951 görüntüleme • 2 yıl önce

Announcing Declarative Schema, a new way to simplify managing your growing database schema! Declarative schema stores the final state of the database in SQL files, making them the single source of truth. We use it internally to manage our prod db, and we hope you love it too!
0:47

Announcing Declarative Schema, a new way to simplify managing your growing database schema! Declarative schema stores the final state of the database in SQL files, making them the single source of truth. We use it internally to manage our prod db, and we hope you love it too!

Supabase

33,894 görüntüleme • 1 yıl önce

Mesa County, Colorado Dominion System Was WIDE OPEN, any computer in the WORLD could connect to the EMS server and flip votes with a few clicks in the 2020 Election. 36 wireless devices, global SQL access via port 1433, uncertified software, and live vote manipulation DEMONSTRATED. Certification was illegal. System should have been decertified immediately. This is the 2nd forensic report to Mesa County, Colorado, connected to Tina Peters. The most significant findings allowed unrestricted global SQL access. The EMS server was configured so any computer in the world could connect directly to the SQL databases via port 1433, default SQL port. Windows Firewall allowed inbound connections from any IP address with no restrictions. All SQL protocols were enabled. Shared Memory, Named Pipes, and TCP/IP, making remote access trivial. 36 wireless devices identified built into the DVS D-Suite system, 35 in voting components like Dell Latitude/Optiplex ICVA/ICC machines and ICX tablets + 1 Dell E3DW wireless printer on the EMS server. These were vulnerable to known attacks (KRACK). Self-signed certificate used for encryption, vulnerable to man-in-the-middle attacks. Shared/default passwords for administrative accounts, violating user accountability requirements. Audit logs overwritten/deleted by design and configuration, building on Report #1 findings. Uncertified software, Microsoft SQL Server Management Studio (SSMS) was installed on the server, May 7, 2017, but not listed in the official certification application or test report for DVS D-Suite 5.1-CO. Live vote manipulation was demonstrated using SSMS, changing just two values in the dbo.Choice table, internalMachineId for candidates, flipped vote totals between candidates, one example altered tens of thousands of votes in the demo. Multiple election databases, including 2020 General, municipal, etc., were fully accessible and modifiable. Anyone at a logged-in EMS server could alter databases in seconds with SSMS. From any non-Dominion computer on the same subnet. From a cell phone/iPhone connected to the network using a free SQL app, successfully identified the server and modified data. These are HUGE security risks of enormous proportions, compromising the 2020 Election in multiple ways with multiple attack vectors.
8:02

Mesa County, Colorado Dominion System Was WIDE OPEN, any computer in the WORLD could connect to the EMS server and flip votes with a few clicks in the 2020 Election. 36 wireless devices, global SQL access via port 1433, uncertified software, and live vote manipulation DEMONSTRATED. Certification was illegal. System should have been decertified immediately. This is the 2nd forensic report to Mesa County, Colorado, connected to Tina Peters. The most significant findings allowed unrestricted global SQL access. The EMS server was configured so any computer in the world could connect directly to the SQL databases via port 1433, default SQL port. Windows Firewall allowed inbound connections from any IP address with no restrictions. All SQL protocols were enabled. Shared Memory, Named Pipes, and TCP/IP, making remote access trivial. 36 wireless devices identified built into the DVS D-Suite system, 35 in voting components like Dell Latitude/Optiplex ICVA/ICC machines and ICX tablets + 1 Dell E3DW wireless printer on the EMS server. These were vulnerable to known attacks (KRACK). Self-signed certificate used for encryption, vulnerable to man-in-the-middle attacks. Shared/default passwords for administrative accounts, violating user accountability requirements. Audit logs overwritten/deleted by design and configuration, building on Report #1 findings. Uncertified software, Microsoft SQL Server Management Studio (SSMS) was installed on the server, May 7, 2017, but not listed in the official certification application or test report for DVS D-Suite 5.1-CO. Live vote manipulation was demonstrated using SSMS, changing just two values in the dbo.Choice table, internalMachineId for candidates, flipped vote totals between candidates, one example altered tens of thousands of votes in the demo. Multiple election databases, including 2020 General, municipal, etc., were fully accessible and modifiable. Anyone at a logged-in EMS server could alter databases in seconds with SSMS. From any non-Dominion computer on the same subnet. From a cell phone/iPhone connected to the network using a free SQL app, successfully identified the server and modified data. These are HUGE security risks of enormous proportions, compromising the 2020 Election in multiple ways with multiple attack vectors.

The SCIF

52,109 görüntüleme • 2 ay önce