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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.show more

Akshay 🚀

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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 views • 1 year ago

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 views • 7 months ago

Just announced: Additional capabilities in MCP Toolbox for Databases designed to empower AI-assisted development! Toolbox is an open-source MCP server that allows developers to easily connect gen AI agents to enterprise data. Learn more →

Just announced: Additional capabilities in MCP Toolbox for Databases designed to empower AI-assisted development! Toolbox is an open-source MCP server that allows developers to easily connect gen AI agents to enterprise data. Learn more →

Google Cloud Tech

31,996 views • 1 year ago

Today we're launching Amplitude MCP MCP was made for data analytics. Ask a vague question and have an AI model iteratively query Amplitude to find you insights. Amplitude MCP exposes all of Amplitude's functionality so an agent can interact with it directly. It allows you to use Amplitude without having to know anything about Amplitude's user interface or your data taxonomy. It uses the intelligence of off the shelf AI agents, including Claude, Gemini, and Cursor, to query Amplitude data you already have to find answers. It leverages all of your existing Amplitude data and how it's used to navigate through large complex behavioral datasets. You can have conversations about your product data directly inside any AI model with MCP. It can create Amplitude dashboards and notebooks with what it finds and even make recommendations. This is the best MCP use case I've ever seen. Watching an AI agent think, reason, query Amplitude, and then repeat that process iteratively is magical. See it in action:

Today we're launching Amplitude MCP MCP was made for data analytics. Ask a vague question and have an AI model iteratively query Amplitude to find you insights. Amplitude MCP exposes all of Amplitude's functionality so an agent can interact with it directly. It allows you to use Amplitude without having to know anything about Amplitude's user interface or your data taxonomy. It uses the intelligence of off the shelf AI agents, including Claude, Gemini, and Cursor, to query Amplitude data you already have to find answers. It leverages all of your existing Amplitude data and how it's used to navigate through large complex behavioral datasets. You can have conversations about your product data directly inside any AI model with MCP. It can create Amplitude dashboards and notebooks with what it finds and even make recommendations. This is the best MCP use case I've ever seen. Watching an AI agent think, reason, query Amplitude, and then repeat that process iteratively is magical. See it in action:

Spenser Skates

32,295 views • 8 months ago

You can now access AI directly from your database! Here is a step-by-step demo that uses GPT-4 to classify customer reviews from a MySQL dataset. And I'm only writing SQL instructions! You have to see it! The model acts as another table in the database. I can query it and join it with other tables. It's awesome! This is all thanks to mindshub. Here is their GitHub repository: They are sponsoring this post. Watch the video.

You can now access AI directly from your database! Here is a step-by-step demo that uses GPT-4 to classify customer reviews from a MySQL dataset. And I'm only writing SQL instructions! You have to see it! The model acts as another table in the database. I can query it and join it with other tables. It's awesome! This is all thanks to mindshub. Here is their GitHub repository: They are sponsoring this post. Watch the video.

Santiago

209,434 views • 2 years ago

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!

Andrew Ng

141,952 views • 1 year ago

wtf is MCP? Paul Klein IV, CEO of explains "Model Context Protocol" (MCP) in plain terms: "There's this new way that people are exposing tools to AI & the 'Model Context Protocol' is one of those ways that you can standardize & give your AI a set of tools to call. BrowserBase has a way to expose the browser to your agent via MCP or MCP server. More & more developers are trying to find ways to integrate more functionalities to their AI agents & BrowserBase's browsers are one of those primitive functionalities, & the MCP is just one way to integrate it into your software." Paul Klein IV From 🅱️'s GitHub: Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you’re building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need. This server provides cloud browser automation capabilities using Browserbase and Stagehand. This server enables LLMs to interact with web pages, take screenshots, and execute JavaScript in a cloud browser environment.

wtf is MCP? Paul Klein IV, CEO of explains "Model Context Protocol" (MCP) in plain terms: "There's this new way that people are exposing tools to AI & the 'Model Context Protocol' is one of those ways that you can standardize & give your AI a set of tools to call. BrowserBase has a way to expose the browser to your agent via MCP or MCP server. More & more developers are trying to find ways to integrate more functionalities to their AI agents & BrowserBase's browsers are one of those primitive functionalities, & the MCP is just one way to integrate it into your software." Paul Klein IV From 🅱️'s GitHub: Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you’re building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need. This server provides cloud browser automation capabilities using Browserbase and Stagehand. This server enables LLMs to interact with web pages, take screenshots, and execute JavaScript in a cloud browser environment.

Molly O’Shea

20,408 views • 11 months ago

Real-time knowledge bases are the future of RAG! Today, I'll show you how to build agents that can search across any app, database, or document store in real time. We'll be using Airweave, an open-source tool that builds live, bi-temporal knowledge bases so your agents always reason on the freshest facts. It seamlessly connects to tools like Notion, Google Drive, and SQL databases, transforming their contents into searchable knowledge. The best part? The entire setup runs locally inside a Docker container on my machine. You can also expose it via an API and an MCP server. In the video below, I provide the complete setup and a live demo of its capabilities. I've also shared a link to Airweave's GitHub repo in the next tweet!

Real-time knowledge bases are the future of RAG! Today, I'll show you how to build agents that can search across any app, database, or document store in real time. We'll be using Airweave, an open-source tool that builds live, bi-temporal knowledge bases so your agents always reason on the freshest facts. It seamlessly connects to tools like Notion, Google Drive, and SQL databases, transforming their contents into searchable knowledge. The best part? The entire setup runs locally inside a Docker container on my machine. You can also expose it via an API and an MCP server. In the video below, I provide the complete setup and a live demo of its capabilities. I've also shared a link to Airweave's GitHub repo in the next tweet!

Akshay 🚀

66,067 views • 8 months ago

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 views • 1 year ago

English is the most powerful language in the world. MongoDB just added support for MCP, and literally anyone can now query data without knowing anything about databases. If you know English, you are all set. I recorded a quick video to show you how I connect to a sample Airbnb database and run different queries: • How many listings? • How many listings in the US? • What's the list of countries in these listings? It takes 10 seconds to configure Claude, Cursor, WindSurf, or any other AI assistant to connect to MongoDB's MCP Server. By the way, the server supports much more than queries: • You can explore your collections • You can fetch schema information • You can generate app code snippets • You can manage your databases • You can handle cluster/user/admin tasks Here is a link to the MCP server GitHub repository: This is huge for developers. It's even bigger for non-developers. Thanks to the MongoDB team for collaborating with me on this post.

English is the most powerful language in the world. MongoDB just added support for MCP, and literally anyone can now query data without knowing anything about databases. If you know English, you are all set. I recorded a quick video to show you how I connect to a sample Airbnb database and run different queries: • How many listings? • How many listings in the US? • What's the list of countries in these listings? It takes 10 seconds to configure Claude, Cursor, WindSurf, or any other AI assistant to connect to MongoDB's MCP Server. By the way, the server supports much more than queries: • You can explore your collections • You can fetch schema information • You can generate app code snippets • You can manage your databases • You can handle cluster/user/admin tasks Here is a link to the MCP server GitHub repository: This is huge for developers. It's even bigger for non-developers. Thanks to the MongoDB team for collaborating with me on this post.

Santiago

44,818 views • 1 year ago

We can finally say it! is launching today! We spent a year building the data substrate for the agent economy, one MCP that allows your agents to query the world as a DB, so you can skip the data and focus on building what matters! Cala

We can finally say it! is launching today! We spent a year building the data substrate for the agent economy, one MCP that allows your agents to query the world as a DB, so you can skip the data and focus on building what matters! Cala

Elisenda Bou-Balust

18,568 views • 1 month ago

In case you missed it, earlier this week we launched "MCP: Build Rich-Context AI Apps with Anthropic." Enroll now and: ✅ Explore how MCP standardizes access to tools and data for AI applications, its underlying architecture, and how it simplifies the integration of new tools and connections to external systems (e.g., GitHub repos, Google Docs, local files). ✅ Build and deploy an MCP server that provides tools, resources, and prompts, and add it to the configuration of AI applications, such as Claude Desktop, to extend them. ✅ Build an MCP-compatible application that hosts multiple MCP clients, each maintaining 1-to-1 connection to an MCP server. Join in for free:

In case you missed it, earlier this week we launched "MCP: Build Rich-Context AI Apps with Anthropic." Enroll now and: ✅ Explore how MCP standardizes access to tools and data for AI applications, its underlying architecture, and how it simplifies the integration of new tools and connections to external systems (e.g., GitHub repos, Google Docs, local files). ✅ Build and deploy an MCP server that provides tools, resources, and prompts, and add it to the configuration of AI applications, such as Claude Desktop, to extend them. ✅ Build an MCP-compatible application that hosts multiple MCP clients, each maintaining 1-to-1 connection to an MCP server. Join in for free:

DeepLearning.AI

23,829 views • 1 year ago

Good news for devs working in Visual Studio Code! The MongoDB MCP Server connects your MongoDB data to AI clients, such as GitHub Copilot in VS Code. Now you can ask for collection schemas and data samples, manage users and access, and generate queries all using natural language, right from your editor. Give it a try 👉

Good news for devs working in Visual Studio Code! The MongoDB MCP Server connects your MongoDB data to AI clients, such as GitHub Copilot in VS Code. Now you can ask for collection schemas and data samples, manage users and access, and generate queries all using natural language, right from your editor. Give it a try 👉

MongoDB

19,994 views • 1 year ago

Big moment for Postgres! AI agents broke the idea of what a database is supposed to do. Traditional databases were built for humans, and Agents broke that model. - They branch endlessly. - They run ten experiments at once. - They need isolation, context, memory, structured reasoning, and safe sandboxes. Letting agents touch production systems is terrifying because the old model of Postgres was never built for this kind of behavior. Agentic Postgres is an agent-ready version of Postgres by TimescaleDB (by Tiger Data) that solves this. I think it is one of the biggest upgrades to the Agent stack this year and Tiger Data is working with me on this post to share what they did. Some key features: > It instantly creates branches of an entire database, which is perfect for parallel agent evals, safe experiments, migrations, or isolated testing. Forks take seconds and cost almost nothing. > It comes with a built-in MCP server, which agents can use to get schema guidance, best practices, and safe, structured access to Postgres. This is also helpful to run migrations with a real understanding. > It comes with actual hybrid search (vector search and BM25), so Agents can retrieve data directly inside the database. > The database is Memory native. This gives a persistent context for Agents to evolve. This is one of the first times I have seen Postgres feel ready for the AI native era.

Big moment for Postgres! AI agents broke the idea of what a database is supposed to do. Traditional databases were built for humans, and Agents broke that model. - They branch endlessly. - They run ten experiments at once. - They need isolation, context, memory, structured reasoning, and safe sandboxes. Letting agents touch production systems is terrifying because the old model of Postgres was never built for this kind of behavior. Agentic Postgres is an agent-ready version of Postgres by TimescaleDB (by Tiger Data) that solves this. I think it is one of the biggest upgrades to the Agent stack this year and Tiger Data is working with me on this post to share what they did. Some key features: > It instantly creates branches of an entire database, which is perfect for parallel agent evals, safe experiments, migrations, or isolated testing. Forks take seconds and cost almost nothing. > It comes with a built-in MCP server, which agents can use to get schema guidance, best practices, and safe, structured access to Postgres. This is also helpful to run migrations with a real understanding. > It comes with actual hybrid search (vector search and BM25), so Agents can retrieve data directly inside the database. > The database is Memory native. This gives a persistent context for Agents to evolve. This is one of the first times I have seen Postgres feel ready for the AI native era.

Avi Chawla

94,290 views • 7 months ago

Today, Box is announcing major new AI agent capabilities to let customers tap into the full value of their unstructured data. First, we’re announcing all new updates to the Box AI Studio to make it even easier to build AI agents that tap into your enterprise content for any job function, business process, or industry specific use case. We are also expanding our set of foundational agents that customers will be able to use to work with their enterprise content, including new features like search and research on unstructured data. Next, we’re announcing Box Extract to enable customers to use AI agents seamlessly for complex data extraction from any type of document or content. This makes it easier than ever to pull out data from contracts, invoices, research data, marketing assets, medical charts, and more. Finally, we’re introducing Box Automate, a new workflow automation solution within Box that lets you deploy AI agents across enterprise content-centric workflows. With Box Automate, you can design your business process in a simple drag and drop builder and then drop in AI agents at any step in the process. This ensures agents execute tasks at the right steps in a workflow every time. Best of all, our AI agents and workflow tools are designed to work across any system our customers work within, whether it’s leveraging pre-built integrations, Box APIs, or the new Box MCP Server. Ultimately, all of these capabilities come together to transform how companies can work with their enterprise content. Software has historically only been good at automating work that deals with structured data, which is why ERP, CRM, and HR systems have been mainstays of enterprise software for so long. The data in these systems fits neatly into a database, and the workflows are very ripe for automation. But it turns out most of the work in the world deals with unstructured data. It’s ideating through research documents, working with a client on contracts, reviewing details for a new product launch, looking at a patient’s healthcare record to make a diagnosis, working through due diligence documents for an M&A deal, and so on. For the first time ever, we can begin to bring all new insights and automation to this work with AI agents. At Box, we’re incredibly excited to be on this journey to help customers transform how they work with their most important data.

Today, Box is announcing major new AI agent capabilities to let customers tap into the full value of their unstructured data. First, we’re announcing all new updates to the Box AI Studio to make it even easier to build AI agents that tap into your enterprise content for any job function, business process, or industry specific use case. We are also expanding our set of foundational agents that customers will be able to use to work with their enterprise content, including new features like search and research on unstructured data. Next, we’re announcing Box Extract to enable customers to use AI agents seamlessly for complex data extraction from any type of document or content. This makes it easier than ever to pull out data from contracts, invoices, research data, marketing assets, medical charts, and more. Finally, we’re introducing Box Automate, a new workflow automation solution within Box that lets you deploy AI agents across enterprise content-centric workflows. With Box Automate, you can design your business process in a simple drag and drop builder and then drop in AI agents at any step in the process. This ensures agents execute tasks at the right steps in a workflow every time. Best of all, our AI agents and workflow tools are designed to work across any system our customers work within, whether it’s leveraging pre-built integrations, Box APIs, or the new Box MCP Server. Ultimately, all of these capabilities come together to transform how companies can work with their enterprise content. Software has historically only been good at automating work that deals with structured data, which is why ERP, CRM, and HR systems have been mainstays of enterprise software for so long. The data in these systems fits neatly into a database, and the workflows are very ripe for automation. But it turns out most of the work in the world deals with unstructured data. It’s ideating through research documents, working with a client on contracts, reviewing details for a new product launch, looking at a patient’s healthcare record to make a diagnosis, working through due diligence documents for an M&A deal, and so on. For the first time ever, we can begin to bring all new insights and automation to this work with AI agents. At Box, we’re incredibly excited to be on this journey to help customers transform how they work with their most important data.

Aaron Levie

91,860 views • 9 months ago

Startups have raised $1B+ to crack Enterprise AI search. Now someone open-sourced it for FREE. The video below shows a self-hosted Slack assistant that answers your questions by searching across all your company's tools in a single query. It's built on top of Airweave, an open-source context retrieval layer that makes all your tools searchable for Agents using semantic, keyword, and agentic search. Here's how the app works: - The app watches for questions in Slack. - It searches every connected tool at once using Airweave (Notion, GitHub, Jira, Linear, etc.) to find relevant context. - The Airweave engine ranks the results by relevance and returns references to the original docs. - An LLM generates the final response and sends it back to Slack with citations. The entire stack is self-hostable via Docker. Find the project in the replies!

Startups have raised $1B+ to crack Enterprise AI search. Now someone open-sourced it for FREE. The video below shows a self-hosted Slack assistant that answers your questions by searching across all your company's tools in a single query. It's built on top of Airweave, an open-source context retrieval layer that makes all your tools searchable for Agents using semantic, keyword, and agentic search. Here's how the app works: - The app watches for questions in Slack. - It searches every connected tool at once using Airweave (Notion, GitHub, Jira, Linear, etc.) to find relevant context. - The Airweave engine ranks the results by relevance and returns references to the original docs. - An LLM generates the final response and sends it back to Slack with citations. The entire stack is self-hostable via Docker. Find the project in the replies!

Avi Chawla

11,227 views • 2 months ago

Sharing our latest short course: Building and Evaluating Data Agents, created in collaboration with Snowflake and taught by Anupam Datta (Anupam Datta) and Josh Reini (Josh Reini). A data agent extracts data from sources such as files or databases, analyzes it, and provides insights and visualizes its findings. But most data agents struggle with reliability or can't handle multi-step reasoning. In this course, you'll learn to build, trace, and evaluate a multi-agent workflow that plans tasks, pulls context from structured and unstructured data, performs web search, and summarizes or visualizes the final results. Learn more and enroll for free!

Sharing our latest short course: Building and Evaluating Data Agents, created in collaboration with Snowflake and taught by Anupam Datta (Anupam Datta) and Josh Reini (Josh Reini). A data agent extracts data from sources such as files or databases, analyzes it, and provides insights and visualizes its findings. But most data agents struggle with reliability or can't handle multi-step reasoning. In this course, you'll learn to build, trace, and evaluate a multi-agent workflow that plans tasks, pulls context from structured and unstructured data, performs web search, and summarizes or visualizes the final results. Learn more and enroll for free!

DeepLearning.AI

40,745 views • 8 months ago

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

15,375 views • 7 months ago

ChatGPT connecting to external data sources offers a glimpse into what AI interoperability looks like. Ask any question in ChatGPT and it can now pull data from Box. The future of AI is AI Agents that can access your systems and data to complete any task for you.

ChatGPT connecting to external data sources offers a glimpse into what AI interoperability looks like. Ask any question in ChatGPT and it can now pull data from Box. The future of AI is AI Agents that can access your systems and data to complete any task for you.

Aaron Levie

57,745 views • 1 year ago

The only MCP server you'll ever need! I just built an MCP server that connects to 200+ data sources and runs locally on my machine. 100% open source.

The only MCP server you'll ever need! I just built an MCP server that connects to 200+ data sources and runs locally on my machine. 100% open source.

Akshay 🚀

63,847 views • 1 year ago