正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

It's launch week . 5 days 5 launches Day 1: SQL Server Data Connector Now you can connect your SQL Server to Julius in just a few clicks and instantly explore your data. Track adoption, analyze performance, and access KPIs all without writing a single line of SQL

rahul

64,396 subscribers

48,216 次观看 • 9 个月前 •via X (Twitter)

新闻政治喜剧

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

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):

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)

77,293 次观看 • 1 年前

We're also launching 🔌Data Connectors in Julius AI 📊 Julius can now connect directly to your Postgres database, reason through the schemas, write & run SQL queries and create visualizations in seconds You can also pull datasets from Google Drive and OneDrive into Julius, and export dataframes to Google Sheets and Drive

We're also launching 🔌Data Connectors in Julius AI 📊 Julius can now connect directly to your Postgres database, reason through the schemas, write & run SQL queries and create visualizations in seconds You can also pull datasets from Google Drive and OneDrive into Julius, and export dataframes to Google Sheets and Drive

rahul

65,778 次观看 • 11 个月前

Introducing Copilot Chronicle. All of your session data indexed and searchable by the agent via FTS and SQL queries. Analyze your sessions to auto-update instructions, find details about that bug you fixed last week, and figure out which times of day you're most productive.

Introducing Copilot Chronicle. All of your session data indexed and searchable by the agent via FTS and SQL queries. Analyze your sessions to auto-update instructions, find details about that bug you fixed last week, and figure out which times of day you're most productive.

Evan Boyle

78,057 次观看 • 4 个月前

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.

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

16,646 次观看 • 8 个月前

Proof of SQL is the only way to ZK prove data source, storage, and compute in a database. Connect verifiable queries against 100TB of preloaded Web3 data indexed from major chains (and your own offchain datasets) to your smart contract. Prove your queries sub-second. Only with Space and Time.

Proof of SQL is the only way to ZK prove data source, storage, and compute in a database. Connect verifiable queries against 100TB of preloaded Web3 data indexed from major chains (and your own offchain datasets) to your smart contract. Prove your queries sub-second. Only with Space and Time.

Space and Time

25,864 次观看 • 2 年前

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!

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 次观看 • 8 个月前

AI Agents can now connect to 100,000+ APIs instantly. Postman's AI Agent builder can turn any API into an MCP server in seconds without writing a single line of Code. You can use it in Claude, Cursor, Windsurf and other AI tools.

AI Agents can now connect to 100,000+ APIs instantly. Postman's AI Agent builder can turn any API into an MCP server in seconds without writing a single line of Code. You can use it in Claude, Cursor, Windsurf and other AI tools.

Shubham Saboo

79,784 次观看 • 1 年前

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

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 🚀

39,331 次观看 • 5 个月前

MCP is mind-blowing. Here is a genius setup that will earn you a promotion with your manager. I have multiple data sources: data in S3, MySQL, and local files. Using Spice (an open-source project), I can co-locate all of that data behind a single SQL endpoint and augment it using MCP. Before, accessing data was slow, and I had to make multiple requests to get anything. After using Spice, I can query the data much faster (since it's mirrored locally), and I don't have to worry about where the data is coming from. Spice acts as an MCP server, so I can connect to it from my IDE or the Claude Desktop app and have access to all the data. But even more cool than that: Spice also acts as an MCP client, so it can connect to other MCP servers. This is where things get really interesting! Watch the video. I'm querying multiple data sources from my IDE and using the Google Maps MCP server for driving directions.

MCP is mind-blowing. Here is a genius setup that will earn you a promotion with your manager. I have multiple data sources: data in S3, MySQL, and local files. Using Spice (an open-source project), I can co-locate all of that data behind a single SQL endpoint and augment it using MCP. Before, accessing data was slow, and I had to make multiple requests to get anything. After using Spice, I can query the data much faster (since it's mirrored locally), and I don't have to worry about where the data is coming from. Spice acts as an MCP server, so I can connect to it from my IDE or the Claude Desktop app and have access to all the data. But even more cool than that: Spice also acts as an MCP client, so it can connect to other MCP servers. This is where things get really interesting! Watch the video. I'm querying multiple data sources from my IDE and using the Google Maps MCP server for driving directions.

Santiago

142,696 次观看 • 1 年前

English Is The New SQL LLMs have made it easy for everyone to create on-the-fly dashboards and plots. You can connect to your database and then use a SOTA LLM like 4o, Opus, or Gemini to generate SQL and Python code to create on-the-fly dashboards and plots. ChatLLM makes it possible across all state-of-the-art LLMs -

English Is The New SQL LLMs have made it easy for everyone to create on-the-fly dashboards and plots. You can connect to your database and then use a SOTA LLM like 4o, Opus, or Gemini to generate SQL and Python code to create on-the-fly dashboards and plots. ChatLLM makes it possible across all state-of-the-art LLMs -

Bindu Reddy

50,564 次观看 • 2 年前

I can't call Warp a terminal anymore. Warp is now a fully-fledged AI coding platform for your command line. They just added support to MCP, so I can literally plug in any server and have Warp do whatever I need! Here is me using Stripe and running SQL queries in English straight from the command line.

I can't call Warp a terminal anymore. Warp is now a fully-fledged AI coding platform for your command line. They just added support to MCP, so I can literally plug in any server and have Warp do whatever I need! Here is me using Stripe and running SQL queries in English straight from the command line.

Santiago

20,689 次观看 • 1 年前

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.

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

46,819 次观看 • 16 天前

Amazon Redshift RG, powered by AWS Graviton, deliver better performance - running data warehouse and data lake workloads up to 2.4x as fast as previous generation RA3 at 30% lower cost per vCPU. Run SQL analytics across your data warehouse and data lake from a single engine — including on open formats such as Apache Iceberg and Apache Parquet. Built to meet the demands of today’s analytics and agentic AI workloads.

Amazon Redshift RG, powered by AWS Graviton, deliver better performance - running data warehouse and data lake workloads up to 2.4x as fast as previous generation RA3 at 30% lower cost per vCPU. Run SQL analytics across your data warehouse and data lake from a single engine — including on open formats such as Apache Iceberg and Apache Parquet. Built to meet the demands of today’s analytics and agentic AI workloads.

Amazon Web Services

13,468 次观看 • 2 个月前

AI Agents can now access your tools with Composio MCP. You can connect Claude, Cursor, Windsurf to 250+ apps and 20,000+ API actions while skipping complex authentication setup. All of this without writing a single line of Python Code.

AI Agents can now access your tools with Composio MCP. You can connect Claude, Cursor, Windsurf to 250+ apps and 20,000+ API actions while skipping complex authentication setup. All of this without writing a single line of Python Code.

Shubham Saboo

72,358 次观看 • 1 年前

DeFi data just became model-ready. Introducing Query Builder for DefiLlama Sheets. Instead of writing complex formulas or stitching together multiple queries, you can now pull exactly the data you want into Sheets or Excel in a few clicks. > Build spreadsheet formulas visually > Query multiple projects in a single request > Analyze historical metrics side by side with clean, consistent data

DeFi data just became model-ready. Introducing Query Builder for DefiLlama Sheets. Instead of writing complex formulas or stitching together multiple queries, you can now pull exactly the data you want into Sheets or Excel in a few clicks. > Build spreadsheet formulas visually > Query multiple projects in a single request > Analyze historical metrics side by side with clean, consistent data

DefiLlama.com

21,653 次观看 • 5 个月前

INCREDIBLE!!! Gemini did 6 hours of manual data analysis work in just 2 mins. Gemini in a Colab notebook now lets you plan, analyze, and visualize your data. I used a dataset of 100k rows and didn't write or execute a single line of code. Completely hands-off!

INCREDIBLE!!! Gemini did 6 hours of manual data analysis work in just 2 mins. Gemini in a Colab notebook now lets you plan, analyze, and visualize your data. I used a dataset of 100k rows and didn't write or execute a single line of code. Completely hands-off!

Avi Chawla

300,110 次观看 • 1 年前

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.

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

19,094 次观看 • 7 个月前

You can now build a Deep Research Agent over your organization's data in literally 1 tool call using ZeroEntropy (YC W25)'s MCP server! We just launched our MCP server, which you can connect to the OpenAI Deep Research API! This demo shows to find ZeroEntropy's secret buried in 50k+ documents

You can now build a Deep Research Agent over your organization's data in literally 1 tool call using ZeroEntropy (YC W25)'s MCP server! We just launched our MCP server, which you can connect to the OpenAI Deep Research API! This demo shows to find ZeroEntropy's secret buried in 50k+ documents

Ghita

17,447 次观看 • 1 年前