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Even the most powerful artificial minds need infrastructure to understand the world. We've launched a radical project to change the way we build AI Agents. A graph layer that uses up to 34x less RAM than neo4j. To start: pgGraph (Full Open Souce, Rust), is a postgres extension that... show more

dale

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14,888 次观看 • 29 天前 •via X (Twitter)

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

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

rahul

65,778 次观看 • 11 个月前

Introducing pg_strict for Postgres. Our new extension adds a safety net to Postgres, catching dangerous queries before they run.

Introducing pg_strict for Postgres. Our new extension adds a safety net to Postgres, catching dangerous queries before they run.

PlanetScale

114,704 次观看 • 5 个月前

We built a no-signup Postgres tool 📸 - Get a Postgres URL in a second, from your browser or terminal - If you want to keep your database, transfer it to your Neon account It uses Cloudflare, React Router, and Drizzle ORM under the hood

We built a no-signup Postgres tool 📸 - Get a Postgres URL in a second, from your browser or terminal - If you want to keep your database, transfer it to your Neon account It uses Cloudflare, React Router, and Drizzle ORM under the hood

Neon Postgres

26,526 次观看 • 1 年前

All Create apps now ship with a built-in Postgres database (powered by Neon Postgres) • zero setup. just chat about your app • migrations and queries, added automagically • edit data like a spreadsheet in our viewer • 1 GB free storage Build full stack apps that scale big

All Create apps now ship with a built-in Postgres database (powered by Neon Postgres) • zero setup. just chat about your app • migrations and queries, added automagically • edit data like a spreadsheet in our viewer • 1 GB free storage Build full stack apps that scale big

Anything

70,037 次观看 • 1 年前

Let Postgres own the Iceberg catalog and delegate analytics to DuckDB. The result => transactional lakehouse updates with fast analytical queries. This isn’t a concept. It’s exactly what pg_lake delivers today. pg_lake combines a set of extensions and components that let you query and modify Iceberg tables (and other lakehouse formats) directly from Postgres. DuckDB is used to accelerate analytical queries and runs in a sidecar process called pgduck_server, which communicates with Postgres during query execution. How it works (diagram below): 1. An application sends a query to Postgres to calculate unrealized PnL (Profit and Loss) for the Disney ticker. 2. Postgres parses the query and identifies the part that computes the average price from historical lakehouse data. 3. That part is forwarded to pgduck_server for accelerated execution. 4. pgduck_server delegates execution to DuckDB, which queries the lakehouse (reusing cached data if available). 5. DuckDB computes the average price and returns it to Postgres. 6. Postgres joins the result with local portfolio data and computes the unrealized PnL. 7. The final result is returned to the application.

Let Postgres own the Iceberg catalog and delegate analytics to DuckDB. The result => transactional lakehouse updates with fast analytical queries. This isn’t a concept. It’s exactly what pg_lake delivers today. pg_lake combines a set of extensions and components that let you query and modify Iceberg tables (and other lakehouse formats) directly from Postgres. DuckDB is used to accelerate analytical queries and runs in a sidecar process called pgduck_server, which communicates with Postgres during query execution. How it works (diagram below): 1. An application sends a query to Postgres to calculate unrealized PnL (Profit and Loss) for the Disney ticker. 2. Postgres parses the query and identifies the part that computes the average price from historical lakehouse data. 3. That part is forwarded to pgduck_server for accelerated execution. 4. pgduck_server delegates execution to DuckDB, which queries the lakehouse (reusing cached data if available). 5. DuckDB computes the average price and returns it to Postgres. 6. Postgres joins the result with local portfolio data and computes the unrealized PnL. 7. The final result is returned to the application.

Denis Magda

28,473 次观看 • 4 个月前

Finish the week with a Postgres video 🌟 All about using JSON in Postgres: - when you should use it - how to use it efficiently - how JSONs actually get stored in postgres.

Finish the week with a Postgres video 🌟 All about using JSON in Postgres: - when you should use it - how to use it efficiently - how JSONs actually get stored in postgres.

Gwen (Chen) Shapira

27,599 次观看 • 1 年前

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 次观看 • 7 个月前

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

`bunx pg-here` global one-liner to run a standalone postgres instance with all data within the current folder throw-away postgres anytime anywhere. cli args for version/user/pass/db configs. docs: stop using docker just to launch a postgres!!

`bunx pg-here` global one-liner to run a standalone postgres instance with all data within the current folder throw-away postgres anytime anywhere. cli args for version/user/pass/db configs. docs: stop using docker just to launch a postgres!!

murat 🍥

50,537 次观看 • 4 个月前

Build better RAG by letting a team of agents extract and connect your reference materials into a knowledge graph. Our new short course, “Agentic Knowledge Graph Construction,” taught by Neo4j Innovation Lead Andreas Kollegger, shows you how. Knowledge graphs are an important way to store information accurately but they are a lot of work to build manually. In this course you’ll learn how to build a team of agents that turn data– in this case product reviews and invoices from suppliers–into structured graphs of entities and relationships for RAG. Learn how agents can automatically handle the time-consuming work of building graphs — extracting entities and relationships (e.g., Product "contains" Assembly, Part "supplied_by" Supplier, Customer review "mentions" Product), deduplicating them, fact-checking them, and committing them to a graph database — so your retrieval system can find right information to generate accurate output. For example, you can use agents to help trace customer complaints directly to specific suppliers, manufacturing processes, and product hierarchies, thus turning fragmented information into queryable business intelligence. Skills you’ll gain: - Build, store, and access knowledge graphs using the Neo4j graph database - Build multi-agent systems using Google’s Agent Development Kit (ADK) - Set up a loop of agentic workflows to propose and refine a graph schema through fact-checking - Connect agent-generated graphs of unstructured and structured data into a unified knowledge graph This course gets into the practicum of why knowledge graphs give more accurate information retrieval than vector search alone, especially for high-stakes applications where precision matters more than fuzzy similarity matching. Sign up here:

Build better RAG by letting a team of agents extract and connect your reference materials into a knowledge graph. Our new short course, “Agentic Knowledge Graph Construction,” taught by Neo4j Innovation Lead Andreas Kollegger, shows you how. Knowledge graphs are an important way to store information accurately but they are a lot of work to build manually. In this course you’ll learn how to build a team of agents that turn data– in this case product reviews and invoices from suppliers–into structured graphs of entities and relationships for RAG. Learn how agents can automatically handle the time-consuming work of building graphs — extracting entities and relationships (e.g., Product "contains" Assembly, Part "supplied_by" Supplier, Customer review "mentions" Product), deduplicating them, fact-checking them, and committing them to a graph database — so your retrieval system can find right information to generate accurate output. For example, you can use agents to help trace customer complaints directly to specific suppliers, manufacturing processes, and product hierarchies, thus turning fragmented information into queryable business intelligence. Skills you’ll gain: - Build, store, and access knowledge graphs using the Neo4j graph database - Build multi-agent systems using Google’s Agent Development Kit (ADK) - Set up a loop of agentic workflows to propose and refine a graph schema through fact-checking - Connect agent-generated graphs of unstructured and structured data into a unified knowledge graph This course gets into the practicum of why knowledge graphs give more accurate information retrieval than vector search alone, especially for high-stakes applications where precision matters more than fuzzy similarity matching. Sign up here:

Andrew Ng

167,807 次观看 • 10 个月前

📣 We're excited to announce Mastering Postgres, a video course coming October 15th. Steve Tenuto and I started this business with dreams to improve the quality of database education. With the help of Xata 🦋, this course is our next step in that direction.

📣 We're excited to announce Mastering Postgres, a video course coming October 15th. Steve Tenuto and I started this business with dreams to improve the quality of database education. With the help of Xata 🦋, this course is our next step in that direction.

Aaron Francis

282,966 次观看 • 1 年前

Claude Code Templates is now part of the Neon Postgres Open Source Program. One command to get a full Postgres setup for Claude Code. What you get: - 2 Skills (instant Postgres provisioning + best practices) - 5 Agents (schema design, auth, migrations, optimization, general) - 1 MCP (Neon management API) - 2 Statuslines (dev metrics + resource monitoring) 10 components with one command. Neon Database expert ready in 5 seconds

Claude Code Templates is now part of the Neon Postgres Open Source Program. One command to get a full Postgres setup for Claude Code. What you get: - 2 Skills (instant Postgres provisioning + best practices) - 5 Agents (schema design, auth, migrations, optimization, general) - 1 MCP (Neon management API) - 2 Statuslines (dev metrics + resource monitoring) 10 components with one command. Neon Database expert ready in 5 seconds

Daniel San

12,443 次观看 • 5 个月前

🚨 Tiger Data - Creators of TimescaleDB (Yes! the team behind TimescaleDB) is quietly revolutionizing how we build agents. The Old Way: Glueing together Postgres + Vector DBs + Search tools = complexity. The New Way: Tiger Data’s Agentic Postgres handles it all natively 🔥 What’s now possible 🧵 ↓ ✦ Hybrid retrieval → combine keyword filtering and vector similarity in a single query ✦ Forkable databases → retrieval and safe experimentation without external systems ✦ Native Postgres semantics → everything stays queryable, inspectable, and debuggable in SQL Why this matters: → Fewer moving parts in RAG pipelines → No extra infrastructure to stitch together → Agents can iterate and learn safely → Production data stays isolated and protected This is an awesome shift! Stop bolting workflows onto your database. Let your database be the workflow 🔥

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Charly Wargnier

20,181 次观看 • 4 个月前

Setting up a database shouldn't take longer than writing the app. Node + Postgres in one deployment. Done in 30 seconds.

Setting up a database shouldn't take longer than writing the app. Node + Postgres in one deployment. Done in 30 seconds.

Kova

22,120 次观看 • 5 个月前

Just Use Postgres feat. Denis Magda Denis literally just published a book called “Just Use Postgres”. In this conversation, that turned out to be super fun, we talked about: • what MySQL got wrong w.r.t community • the recent explosion of Postgres development in the world (Supabase, Neon, Crunchy, ClickHouse) • when Postgres instead of Kafka • the significance of the meme “just use postgres” • Postgres for data analytics Chapters (00:00) Intro (04:26) The Just Use Postgres Movement (08:36) The Small Data Trend and Its Implications (15:49) The Shift from Distributed Systems to Simplicity (17:15) Updating your priors re: PG capabilities (20:28) MySQL's OSS community split & our experiences with such things (39:48) Postgres for Analytics (55:49) PG in the lake house (59:27) AI Marketing Slop (01:02:19) How you'd build a brand new app on Postgres (01:05:24) Speedrun through PG extensions (01:11:56) PG as a Message Queue

Stanislav Kozlovski

13,036 次观看 • 5 个月前

We're winning the AI browser wars, and we're not even trying. Our mission isn't to build a browser. We're an agent company, not a browser company. We've seen firsthand how teams struggle to get started with AI. The tools they tried are too complex and lack the context you need. We decided to build a browser since it's already the tool you work in. We all know how to use a browser. You can pick up Strawberry and start using AI agents from day one. By building a browser we are also positioning ourselves to build towards the internet of the future. An internet where trillions of agents will be communicating with each other. This is the biggest opportunity of our lifetime. But someone has to make sure it is safe, effective and accessible to teams across the world. That is our mission.

We're winning the AI browser wars, and we're not even trying. Our mission isn't to build a browser. We're an agent company, not a browser company. We've seen firsthand how teams struggle to get started with AI. The tools they tried are too complex and lack the context you need. We decided to build a browser since it's already the tool you work in. We all know how to use a browser. You can pick up Strawberry and start using AI agents from day one. By building a browser we are also positioning ourselves to build towards the internet of the future. An internet where trillions of agents will be communicating with each other. This is the biggest opportunity of our lifetime. But someone has to make sure it is safe, effective and accessible to teams across the world. That is our mission.

Charles Maddock

37,810 次观看 • 15 天前

i'm building an AI Postgres database studio that connects to your database and generates (and runs) queries based on prompts. i initially built it to be a personal tool but i think others may find it useful. will be releasing v1 soon. here's a quick demo:

i'm building an AI Postgres database studio that connects to your database and generates (and runs) queries based on prompts. i initially built it to be a personal tool but i think others may find it useful. will be releasing v1 soon. here's a quick demo:

zilla builds cool stuff

14,323 次观看 • 6 个月前

With PGlite + Electric sync you can build an issue tracker handling 100k issues + 500k comments, synced from Postgres to PGlite in the browser! Super fast UI updates with all writes going though the local database, and Postgres FTS in PGlite for search 🔥

With PGlite + Electric sync you can build an issue tracker handling 100k issues + 500k comments, synced from Postgres to PGlite in the browser! Super fast UI updates with all writes going though the local database, and Postgres FTS in PGlite for search 🔥

Sam Willis

55,403 次观看 • 1 年前

Our new short course, Knowledge Graphs for RAG, is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications. In this course, taught by Andreas Kollegger of Neo4j, you’ll - Explore how knowledge graphs work by building a graph of public financial documents from scratch - Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot - Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems Sign up here!

Our new short course, Knowledge Graphs for RAG, is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications. In this course, taught by Andreas Kollegger of Neo4j, you’ll - Explore how knowledge graphs work by building a graph of public financial documents from scratch - Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot - Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems Sign up here!

Andrew Ng

244,266 次观看 • 2 年前

📣 My next course, Introduction to Postgres, is going to be 100% free! The kind folks at Supabase are sponsoring it so that everyone can learn how to use Postgres confidently. But that's not all... I'm changing things up a bit. I'm making a bet. Watch the video to see more!

📣 My next course, Introduction to Postgres, is going to be 100% free! The kind folks at Supabase are sponsoring it so that everyone can learn how to use Postgres confidently. But that's not all... I'm changing things up a bit. I'm making a bet. Watch the video to see more!

Aaron Francis

332,560 次观看 • 8 个月前