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。

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

TextQL

1,579 subscribers

33,426 次观看 • 10 个月前 •via X (Twitter)

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

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.

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.

Y Combinator

24,947 次观看 • 2 年前

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

Secure your code against SQL injection! 🛡️ GitHub Copilot can help you write safer SQL queries and avoid vulnerabilities. ✅ Learn how and get more security best practices with Copilot here. ⬇️

Secure your code against SQL injection! 🛡️ GitHub Copilot can help you write safer SQL queries and avoid vulnerabilities. ✅ Learn how and get more security best practices with Copilot here. ⬇️

GitHub

27,520 次观看 • 1 年前

🚨CVE-2025-25257: Pre-Auth SQL Injection to RCE - Fortinet FortiWeb Fabric Connector PoC: Write-up:

🚨CVE-2025-25257: Pre-Auth SQL Injection to RCE - Fortinet FortiWeb Fabric Connector PoC: Write-up:

Dark Web Informer

13,749 次观看 • 11 个月前

LiveSQL is your new SQL bestie - allowing you to learn, write and execute SQL all in your browser, with ZERO database setup or installations! (& did I mention it's free???) 😝

LiveSQL is your new SQL bestie - allowing you to learn, write and execute SQL all in your browser, with ZERO database setup or installations! (& did I mention it's free???) 😝

layla

31,225 次观看 • 1 年前

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

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

48,106 次观看 • 8 个月前

Computer now connects to Snowflake. Run end-to-end work against live warehouse data and get answers with SQL, source tables, filters, and metrics. It’s like a personal data science team, on call with accurate answers from live company data.

Computer now connects to Snowflake. Run end-to-end work against live warehouse data and get answers with SQL, source tables, filters, and metrics. It’s like a personal data science team, on call with accurate answers from live company data.

Perplexity

78,253 次观看 • 1 个月前

it's a pity we taught AI to write SQL when the machines come for us the end will be swift

it's a pity we taught AI to write SQL when the machines come for us the end will be swift

archie 🦋

599,216 次观看 • 1 年前

SQLFlow - a great tool to visualize SQL queries. #technology #sql #data #programming

SQLFlow - a great tool to visualize SQL queries. #technology #sql #data #programming

Dr Milan Milanović

255,720 次观看 • 2 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

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 • 7 个月前

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

13,444 次观看 • 1 个月前

🌊TrellisInsights (YC W24) makes unstructured data SQL-ready. It extracts and transforms your unstructured data to SQL-compliant tables with schema you define with natural language. Congrats on the launch, Mac Klinkachorn and Jacky Lin!

🌊TrellisInsights (YC W24) makes unstructured data SQL-ready. It extracts and transforms your unstructured data to SQL-compliant tables with schema you define with natural language. Congrats on the launch, Mac Klinkachorn and Jacky Lin!

Y Combinator

14,213 次观看 • 2 年前

Here's the first true showcase of the SQL client I'm building. I've added docking features similar to Zed/VSCode, allowing me to edit queries and view table data simultaneously

Here's the first true showcase of the SQL client I'm building. I've added docking features similar to Zed/VSCode, allowing me to edit queries and view table data simultaneously

Antfroze

235,870 次观看 • 2 年前

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!

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!

TextQL

48,310 次观看 • 8 个月前

🪄 Gemini assistance in Firebase Data Connect You can now use Gemini in Firebase to write schemas, in addition to queries and mutations! Simply tell Gemini what you would like to build, and it will write the schemas for you. Read the blog →

🪄 Gemini assistance in Firebase Data Connect You can now use Gemini in Firebase to write schemas, in addition to queries and mutations! Simply tell Gemini what you would like to build, and it will write the schemas for you. Read the blog →

Firebase

16,504 次观看 • 1 年前

We are redesigning the AI Chat Assistant to include a knowledge base and easy context addition (such as items, SQL queries, and SQL files..). It will also support other vendors and will not be limited to ChatGPT only. 👉 Will be available soon on TablePlus and Bolt SQL

We are redesigning the AI Chat Assistant to include a knowledge base and easy context addition (such as items, SQL queries, and SQL files..). It will also support other vendors and will not be limited to ChatGPT only. 👉 Will be available soon on TablePlus and Bolt SQL

TablePlus

13,356 次观看 • 1 年前

On the set of The Odyssey, Matt Damon told John Leguizamo that he inspired him and Ben Affleck to write Good Will Hunting.

On the set of The Odyssey, Matt Damon told John Leguizamo that he inspired him and Ben Affleck to write Good Will Hunting.

OUT OF ORDER

172,958 次观看 • 3 个月前