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You'll never have to write SQL again. We built Ana to: 📊 Connect to ANY data warehouse 🧑‍💻Write SQL and Python autonomously 🔐Fully adhere to Enterprise security (SOC2, VPC, etc.) ⚡️Get set up in 10 minutes Here's Matt and Ben walking through Ana's capability

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TextQL

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33,426 просмотров • 1 год назад •via X (Twitter)

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YC W23's Brewit allows you to use plain English to write SQL, create charts, and generate dashboards/reports based on your database. With built-in data catalogs and monitoring tools, Brewit ensures its accuracy and capability to self-improve.
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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 By the time your agent retrieves the data, it's already outdated.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!
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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!

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95% of Healthcare data lives in petabytes of SQL databases The tools for AI to use that data haven't existed Today we fix that with TextQL Healthcare 100,000+ tables. Trillions of rows. Petabytes of data. 15 minutes to insights. Epic systems with 100,000+ tables. Cerner environments. Claims databases. Clinical notes. Prior authorizations. Healthcare organizations have more data complexity than any other industry - and exactly zero AI platforms built to handle it. Until now. Here's what makes it different: 1. Direct access to ALL your systems. No migration required. Epic + Cerner + Claims + Snowflake + Databricks. Everything. At once. Other platforms: 6-month ETL projects. TextQL: Connect Monday. Query Tuesday. First insights in 15 minutes. 2. Healthcare-compliant execution environment. Autonomous agents running production code in SOC 2 Type II, HIPAA-compliant infrastructure. Full audit trails. On-premise deployment available. 3. Structured AND unstructured data. Simultaneously. Everyone else: Claims records OR clinical notes. Us: Both. At the same time. Make sense of 100,000s of tables without months of data prep. We're not launching with pilots. We're launching with Lumeris - powering their Tom™ AI platform delivering care to millions of Americans. Live partnership. Production workloads. Enterprise healthcare data at scale. Advisory Board of operators who've run organizations serving 120M+ Americans: - Varsha Rao (former CEO Nurx, COO Clover Health) - David Griffith (Trinity Life Sciences, ex-Pfizer) - Sam Mohanty (former CDO, Prime Therapeutics) - Jean-Claude Saghbini (CTO, Lumeris) - Raghu Chandra (30 year EHR Veteran) These aren't advisors. They're the people who built the systems we're now optimizing. Meet us at HLTH Conference next week - Booth #4060 Or request a demo: Comment "HEALTHCARE" and we'll reach out for a customized demo!
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142,872 просмотров • 5 месяцев назад

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. 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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.
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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.

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16,883 просмотров • 9 месяцев назад

Everyone is building RAG applications, but nobody is talking about the data these systems use. You are delusional if you think clients will have their data sitting in a folder waiting for you to process it. Data is everywhere: in Google Drive, Dropbox, S3, Gmail, Slack, you name it. And, of course, no sane developer wants to build connections to every one of these systems. This would be suicide. I'm working with Ragie, and they released Ragie Connect to solve this problem. First, their RAG system is top-notch (they have published how they do on several RAG benchmarks), and with Connect, they made it very simple to integrate client data without having to write any code. (Well, in reality, you still have to write a few lines, but it's minimal.) Instead of developing one-off integrations for Drive, Dropbox, etc, you can use Connect to integrate with all of them and let Ragie handle authentication, authorization, and automatic data synchronization. This is a huge time saver!
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Everyone is building RAG applications, but nobody is talking about the data these systems use. You are delusional if you think clients will have their data sitting in a folder waiting for you to process it. Data is everywhere: in Google Drive, Dropbox, S3, Gmail, Slack, you name it. And, of course, no sane developer wants to build connections to every one of these systems. This would be suicide. I'm working with Ragie, and they released Ragie Connect to solve this problem. First, their RAG system is top-notch (they have published how they do on several RAG benchmarks), and with Connect, they made it very simple to integrate client data without having to write any code. (Well, in reality, you still have to write a few lines, but it's minimal.) Instead of developing one-off integrations for Drive, Dropbox, etc, you can use Connect to integrate with all of them and let Ragie handle authentication, authorization, and automatic data synchronization. This is a huge time saver!

Santiago

72,996 просмотров • 1 год назад

Today we’re shipping some updates to our Magic AI assistant that make it more powerful, intuitive, and faster. We built these AI tools to help humans do their data work— writing queries, futzing with Python code, digging up answers— faster and with more confidence. Our goal is to augment, not replace, human insight. That means Magic isn't an "AI data analyst", because honestly, we don’t really think that should be a thing. Today's updates let you: * Generate multiple cells in your notebook with AI, chaining together SQL, Python, and Charts. * "@ mention" warehouse tables and dataframes to point Magic at specific resources. * Quickly switch between editing, fixing, and generating new cells with keyboard shortcuts * Work faster and more confidently with overall improvements to generation speed and accuracy. Check out some unedited examples of Magic being helpful in the thread below, and let us know what you think of these new features!
2:52

Today we’re shipping some updates to our Magic AI assistant that make it more powerful, intuitive, and faster. We built these AI tools to help humans do their data work— writing queries, futzing with Python code, digging up answers— faster and with more confidence. Our goal is to augment, not replace, human insight. That means Magic isn't an "AI data analyst", because honestly, we don’t really think that should be a thing. Today's updates let you: * Generate multiple cells in your notebook with AI, chaining together SQL, Python, and Charts. * "@ mention" warehouse tables and dataframes to point Magic at specific resources. * Quickly switch between editing, fixing, and generating new cells with keyboard shortcuts * Work faster and more confidently with overall improvements to generation speed and accuracy. Check out some unedited examples of Magic being helpful in the thread below, and let us know what you think of these new features!

Hex

42,419 просмотров • 2 лет назад

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 🚀

39,331 просмотров • 6 месяцев назад

In 2026, will you still be doing work in the most manual way possible? Research suggests that employees spend up to 100 hours a month on work that shouldn’t require humans. Good news: the agentic era is already here. Enterprise teams are automating repetitive, tedious workflows with AI agents that connect securely to tools and data. Fully governed, compliant AI orchestration, with built-in analytics and security—that's StackAI.
0:59

In 2026, will you still be doing work in the most manual way possible? Research suggests that employees spend up to 100 hours a month on work that shouldn’t require humans. Good news: the agentic era is already here. Enterprise teams are automating repetitive, tedious workflows with AI agents that connect securely to tools and data. Fully governed, compliant AI orchestration, with built-in analytics and security—that's StackAI.

StackAI

418,314 просмотров • 8 месяцев назад

You can now run Claude Code locally for FREE. → Using open-source models → Locally and fully private → Full agent + tool workflows Here's the FULL step-by-step tutorial to get it all set up in less than 10 minutes:
8:32

You can now run Claude Code locally for FREE. → Using open-source models → Locally and fully private → Full agent + tool workflows Here's the FULL step-by-step tutorial to get it all set up in less than 10 minutes:

Alvaro Cintas

1,319,012 просмотров • 7 месяцев назад

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

52,040 просмотров • 1 месяц назад

New course: Building Coding Agents with Tool Execution, taught by Tereza Tizkova and Fra from E2B. Most AI agents are limited to predefined function calls. This short course teaches you to build agents that write and execute code to accomplish tasks, accessing entire programming language ecosystems instead of being restricted to a fixed set of tools. You'll learn to run agent-generated code safely in sandboxed cloud environments that protect your systems from harmful operations. Skills you'll gain: - Build agents that write and execute code, manage files, and handle errors autonomously through feedback loops - Run agent code safely in E2B cloud sandboxes and understand tradeoffs between local, containerized, and cloud execution - Create a data analyst agent that explores visualizes data with Pandas - Create a full-stack agent that builds complete Next.js web applications Join and build agents that code their way through complex tasks:
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New course: Building Coding Agents with Tool Execution, taught by Tereza Tizkova and Fra from E2B. Most AI agents are limited to predefined function calls. This short course teaches you to build agents that write and execute code to accomplish tasks, accessing entire programming language ecosystems instead of being restricted to a fixed set of tools. You'll learn to run agent-generated code safely in sandboxed cloud environments that protect your systems from harmful operations. Skills you'll gain: - Build agents that write and execute code, manage files, and handle errors autonomously through feedback loops - Run agent code safely in E2B cloud sandboxes and understand tradeoffs between local, containerized, and cloud execution - Create a data analyst agent that explores visualizes data with Pandas - Create a full-stack agent that builds complete Next.js web applications Join and build agents that code their way through complex tasks:

Andrew Ng

203,918 просмотров • 8 месяцев назад

Holy moly Never seen someone being that bullish for AI developement as Dario Amodei: - There's just an exponential just like we had an exponential with Moore's law - I think the models are just going to get more and more capable at everything - I've had internal people at Anthropic say, "I don't write any code anymore. I don't write I don't open up an editor and write code. I just let Claude code write the first draft and and all I do is edit it. - What what we're going to see in the future is just like we've going to be seen in the past except more so - the models are just going to get more and more intellectually capable and you know the the revenue is going to keep adding zeros. holy moly, Dario!
1:06

Holy moly Never seen someone being that bullish for AI developement as Dario Amodei: - There's just an exponential just like we had an exponential with Moore's law - I think the models are just going to get more and more capable at everything - I've had internal people at Anthropic say, "I don't write any code anymore. I don't write I don't open up an editor and write code. I just let Claude code write the first draft and and all I do is edit it. - What what we're going to see in the future is just like we've going to be seen in the past except more so - the models are just going to get more and more intellectually capable and you know the the revenue is going to keep adding zeros. holy moly, Dario!

Chubby♨️

77,711 просмотров • 8 месяцев назад

When data agents fail, they often fail silently - giving confident-sounding answers that are wrong, and it can be hard to figure out what caused the failure. "Building and Evaluating Data Agents" is a new short course created with Snowflake and taught by Anupam Datta and Josh Reini that teaches you to build data agents with comprehensive evaluation built in. Skills you'll gain: - Build reliable LLM data agents using the Goal-Plan-Action framework and runtime evaluations that catch failures mid-execution - Use OpenTelemetry tracing and evaluation infrastructure to diagnose exactly where agents fail and systematically improve performance - Orchestrate multi-step workflows across web search, SQL, and document retrieval in LangGraph-based agents The result: visibility into every step of your agent's reasoning, so if something breaks, you have a systematic approach to fix it. Sign up to get started:
3:20

When data agents fail, they often fail silently - giving confident-sounding answers that are wrong, and it can be hard to figure out what caused the failure. "Building and Evaluating Data Agents" is a new short course created with Snowflake and taught by Anupam Datta and Josh Reini that teaches you to build data agents with comprehensive evaluation built in. Skills you'll gain: - Build reliable LLM data agents using the Goal-Plan-Action framework and runtime evaluations that catch failures mid-execution - Use OpenTelemetry tracing and evaluation infrastructure to diagnose exactly where agents fail and systematically improve performance - Orchestrate multi-step workflows across web search, SQL, and document retrieval in LangGraph-based agents The result: visibility into every step of your agent's reasoning, so if something breaks, you have a systematic approach to fix it. Sign up to get started:

Andrew Ng

101,930 просмотров • 11 месяцев назад

Stubb: In WWII, the Soviet Union invaded us. We ended up having to give up 10% of our territory. We felt very strongly for Ukraine — we said never alone again. Finland is a security provider. We have conscription, mandatory military service, about 1M men and women have done it.
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Stubb: In WWII, the Soviet Union invaded us. We ended up having to give up 10% of our territory. We felt very strongly for Ukraine — we said never alone again. Finland is a security provider. We have conscription, mandatory military service, about 1M men and women have done it.

Tymofiy Mylovanov

23,804 просмотров • 3 месяцев назад

Without proper governance, an AI agent might autonomously access sensitive data, expose personal information, or modify sensitive records. In our new short course: “Governing AI Agents,” created with Databricks and taught by Amber Roberts, you’ll design AI agents that handle data safely, securely, and transparently across their entire lifecycle. You’ll learn to integrate governance into your agent’s workflow by controlling data access, ensuring privacy protection and implementing observability. Skills you'll gain: - Understand the four pillars of agent governance: Lifecycle management, risk management, security, and observability - Define appropriate data permissions for your agent - Create views or SQL queries that return only the data your agent should access - Anonymize and mask sensitive data like social security numbers and employee IDs - Log, evaluate, version, and deploy your agents on Databricks If you’re building or deploying AI agents, learning how to govern them is key to keeping systems safe and production-ready. Sign up here:
2:01

Without proper governance, an AI agent might autonomously access sensitive data, expose personal information, or modify sensitive records. In our new short course: “Governing AI Agents,” created with Databricks and taught by Amber Roberts, you’ll design AI agents that handle data safely, securely, and transparently across their entire lifecycle. You’ll learn to integrate governance into your agent’s workflow by controlling data access, ensuring privacy protection and implementing observability. Skills you'll gain: - Understand the four pillars of agent governance: Lifecycle management, risk management, security, and observability - Define appropriate data permissions for your agent - Create views or SQL queries that return only the data your agent should access - Anonymize and mask sensitive data like social security numbers and employee IDs - Log, evaluate, version, and deploy your agents on Databricks If you’re building or deploying AI agents, learning how to govern them is key to keeping systems safe and production-ready. Sign up here:

Andrew Ng

78,182 просмотров • 10 месяцев назад

"Claude Code is the first AI tool that can actually recreate how a data scientist thinks." Here's my new episode with Sumeet Marwaha (Head of Data at Brex) on exactly how to build an AI analyst with Claude Code. We covered: ✅ 3 queries to build a data analyst MCP ✅ How to give the analyst both data + context ✅ Exclusive Brex data on which AI tools are crushing it in startups and enterprise Some quotes from Sumeet: "I've set up dashboards many times in my career. They end up getting ignored. Claude will always read it. Claude will always ask the right questions." "You have to set up Claude Code to not just look at the data. It should be able to search Slack, find the incident, and understand why your metrics look wrong." "This tool is absolutely crushing it based on our data. It's both the startup AND enterprise coding tool of choice." 📌 Watch now:
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"Claude Code is the first AI tool that can actually recreate how a data scientist thinks." Here's my new episode with Sumeet Marwaha (Head of Data at Brex) on exactly how to build an AI analyst with Claude Code. We covered: ✅ 3 queries to build a data analyst MCP ✅ How to give the analyst both data + context ✅ Exclusive Brex data on which AI tools are crushing it in startups and enterprise Some quotes from Sumeet: "I've set up dashboards many times in my career. They end up getting ignored. Claude will always read it. Claude will always ask the right questions." "You have to set up Claude Code to not just look at the data. It should be able to search Slack, find the incident, and understand why your metrics look wrong." "This tool is absolutely crushing it based on our data. It's both the startup AND enterprise coding tool of choice." 📌 Watch now:

Peter Yang

39,600 просмотров • 7 месяцев назад

KAMYSHIN: There are two nations on the continent who know how to produce 10 million drones a year. One of them wants not to integrate into Europe, but to invade. Russians have always been very vocal about their targets. When we check tanks we captured from Russia in our tank repair factories, because we grab them, repair them, and try to send them back to Russia to fight against Russia, so when we check those tanks, they never write to Kyiv. They always write to Berlin, NATO, Prague, whatever. They can't admit to themselves that they fight Ukraine only. They believe they fight with NATO. They've always been very vocal about their targets. They want to invade Europe, period. There's another nation which is also capable of producing whatever is needed in big war, and we are asking to get us integrated into Europe. So, you have to choose whether you want to have Ukraine integrated into European defense industry and European Union, rather than Russia invading. Because I've never thought that Ukraine would be integrated into European Union via security and defense road. And it looks like we will be there.
1:32

KAMYSHIN: There are two nations on the continent who know how to produce 10 million drones a year. One of them wants not to integrate into Europe, but to invade. Russians have always been very vocal about their targets. When we check tanks we captured from Russia in our tank repair factories, because we grab them, repair them, and try to send them back to Russia to fight against Russia, so when we check those tanks, they never write to Kyiv. They always write to Berlin, NATO, Prague, whatever. They can't admit to themselves that they fight Ukraine only. They believe they fight with NATO. They've always been very vocal about their targets. They want to invade Europe, period. There's another nation which is also capable of producing whatever is needed in big war, and we are asking to get us integrated into Europe. So, you have to choose whether you want to have Ukraine integrated into European defense industry and European Union, rather than Russia invading. Because I've never thought that Ukraine would be integrated into European Union via security and defense road. And it looks like we will be there.

Kate from Kharkiv

151,090 просмотров • 2 месяцев назад