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`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 🍥

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50,537 Aufrufe • vor 7 Monaten •via X (Twitter)

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Ryan Peterman

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Stanislav Kozlovski

16,889 Aufrufe • vor 5 Monaten

Big moment for Postgres! AI coding tools have been surprisingly bad at writing Postgres code. Not because the models are dumb, but because of how they learned SQL in the first place. LLMs are trained on the internet, which is full of outdated Stack Overflow answers and quick-fix tutorials. So when you ask an AI to generate a schema, it gives you something that technically runs but misses decades of Postgres evolution, like: - No GENERATED ALWAYS AS IDENTITY (added in PG10) - No expression or partial indexes - No NULLS NOT DISTINCT (PG15) - Missing CHECK constraints and proper foreign keys - Generic naming that tells you nothing But this is actually a solvable problem. You can teach AI tools to write better Postgres by giving them access to the right documentation at inference time. This exact solution is actually implemented in the newly released pg-aiguide by Tiger Data - Creators of TimescaleDB, which is an open-source MCP server that provides coding tools access to 35 years of Postgres expertise. In a gist, the MCP server enables: - Semantic search over the official PostgreSQL manual (version-aware, so it knows PG14 vs PG17 differences) - Curated skills with opinionated best practices for schema design, indexing, and constraints. I ran an experiment with Claude Code to see how well this works, and worked with the team to put this together. Prompt: "Generate a schema for an e-commerce site twice, one with the MCP server disabled, one with it enabled. Finally, run an assessment to compare the generated schemas." The run with the MCP server led to: - 420% more indexes (including partial and expression indexes) - 235% more constraints - 60% more tables (proper normalization) - 11 automation functions and triggers - Modern PG17 patterns throughout The MCP-assisted schema had proper data integrity, performance optimizations baked in, and followed naming conventions that actually make sense in production. pg-aiguide works with Claude Code, Cursor, VS Code, and any MCP-compatible tool. It's free and fully open source. I have shared the repo in the replies!

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Avi Chawla

187,076 Aufrufe • vor 9 Monaten

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Introducing db-mover A GUI tool to copy or download database content directly from your browser. Try it here: Check code: I work with a lot of databases: Mongo, Postgres, MySQL, Redis. Every time I need to move data, I am digging through old bash scripts and praying I do not mess up. Recently I got stuck. I was moving a staging database to a new server and I was not at my PC. I only had my phone. I could not run my scripts and had to wait until I got home to fix it. That's why I built db-mover. It is simple, paste your source URL, paste your destination URL, and hit start. You get real-time progress bars and live logs. 100% open source. Give it a try if you want to move data without the terminal. You can also suggest new feature , send in improvements you want added. #OpenSource #DevTools #Database #mongodb #postgres #mysql

JC Coder

13,632 Aufrufe • vor 8 Monaten

How Cloudflare cut query times by 35x without leaving Postgres: Their Postgres tables hit billions of rows, and every time-range query started getting slower. Plain Postgres scans the entire table on every query, regardless of the time window. They tried the manual route by building precomputed aggregates with cron jobs and evaluating ClickHouse, which needed a full ingestion pipeline just to handle their write pattern. Here's where most teams get stuck at exactly this point. They utilize manual partitioning, splitting data into child tables by day or month. Then they wire up cron jobs to refresh aggregate tables. Every schema change after that requires updating the cron logic and coordinating across teams. The infrastructure becomes the project. Tiger Cloud is managed TimescaleDB by Tiger Data - Creators of TimescaleDB on the Postgres you already know, with automatic time-based partitioning, continuous aggregates, and compression built in from the start. Cloudflare moved to TimescaleDB after exhausting the manual route and saw 5-35x query performance improvement on the same data. Here is how it works: → Hypertables partition data by timestamp automatically. Every time-range query hits only the relevant chunk, not the full table. → Continuous aggregates refresh incrementally in the background, with no cron jobs to maintain. To show what this looks like in practice, I built a real-time earthquake intelligence dashboard on a 3D globe using Claude Code and Tiger Cloud in a single session. The USGS earthquake catalog has 400,000+ events since 1900, each a timestamped row. The dashboard plots them as ripple animations sized by magnitude and colored by depth, with a time slider that scrubs through 120 years of seismic history. Every slider position fires a live query against the hypertable, and the side panel pulls from continuous aggregates. Claude Code connected to Tiger Cloud through the Tiger CLI MCP server, provisioned the database, pulled the USGS catalog, and assembled the full Next.js and Three.js frontend without leaving the session. The video below shows the final build in action, and I worked with the Tiger Data team to put this together. Tiger CLI is open-source (Apache 2.0) and works with Claude Code, Cursor, Codex, Gemini CLI, and VS Code. To try this yourself: → Sign up for Tiger Cloud here: New accounts get $1,000 in free credits, no credit card required. → Install Tiger CLI: curl -fsSL https(:)//cli(.)tigerdata(.)com | sh → Run tiger mcp install claude-code → Give Claude Code a prompt and let it build. I also wrote a full walkthrough on how you can turn any coding agent into a production-grade data engineer that can manage over a billion-row Postgres workloads. It covers everything from the database setup to the final build. Read it below.

How Cloudflare cut query times by 35x without leaving Postgres: Their Postgres tables hit billions of rows, and every time-range query started getting slower. Plain Postgres scans the entire table on every query, regardless of the time window. They tried the manual route by building precomputed aggregates with cron jobs and evaluating ClickHouse, which needed a full ingestion pipeline just to handle their write pattern. Here's where most teams get stuck at exactly this point. They utilize manual partitioning, splitting data into child tables by day or month. Then they wire up cron jobs to refresh aggregate tables. Every schema change after that requires updating the cron logic and coordinating across teams. The infrastructure becomes the project. Tiger Cloud is managed TimescaleDB by Tiger Data - Creators of TimescaleDB on the Postgres you already know, with automatic time-based partitioning, continuous aggregates, and compression built in from the start. Cloudflare moved to TimescaleDB after exhausting the manual route and saw 5-35x query performance improvement on the same data. Here is how it works: → Hypertables partition data by timestamp automatically. Every time-range query hits only the relevant chunk, not the full table. → Continuous aggregates refresh incrementally in the background, with no cron jobs to maintain. To show what this looks like in practice, I built a real-time earthquake intelligence dashboard on a 3D globe using Claude Code and Tiger Cloud in a single session. The USGS earthquake catalog has 400,000+ events since 1900, each a timestamped row. The dashboard plots them as ripple animations sized by magnitude and colored by depth, with a time slider that scrubs through 120 years of seismic history. Every slider position fires a live query against the hypertable, and the side panel pulls from continuous aggregates. Claude Code connected to Tiger Cloud through the Tiger CLI MCP server, provisioned the database, pulled the USGS catalog, and assembled the full Next.js and Three.js frontend without leaving the session. The video below shows the final build in action, and I worked with the Tiger Data team to put this together. Tiger CLI is open-source (Apache 2.0) and works with Claude Code, Cursor, Codex, Gemini CLI, and VS Code. To try this yourself: → Sign up for Tiger Cloud here: New accounts get $1,000 in free credits, no credit card required. → Install Tiger CLI: curl -fsSL https(:)//cli(.)tigerdata(.)com | sh → Run tiger mcp install claude-code → Give Claude Code a prompt and let it build. I also wrote a full walkthrough on how you can turn any coding agent into a production-grade data engineer that can manage over a billion-row Postgres workloads. It covers everything from the database setup to the final build. Read it below.

Avi Chawla

22,094 Aufrufe • vor 1 Monat

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 Aufrufe • vor 10 Monaten

Just shipped a new Next.js template! • Uses Postgres and Drizzle for the database • UI components with Tailwind and shadcn/ui • Email/password authentication with JWTs stored to cookies • Dashboard pages with CRUD operations on users/teams • Basic RBAC with Owner and Member roles • Pricing page (/) which connects to Stripe Checkout • Subscription management with Stripe Customer Portal • Global middleware to protect logged-in routes • Local middleware to protect Server Actions / validate schemas • Activity logging system for any user events It's not finished, and there's probably bugs, but it's time to ship 🫡 • Demo: • Code:

Just shipped a new Next.js template! • Uses Postgres and Drizzle for the database • UI components with Tailwind and shadcn/ui • Email/password authentication with JWTs stored to cookies • Dashboard pages with CRUD operations on users/teams • Basic RBAC with Owner and Member roles • Pricing page (/) which connects to Stripe Checkout • Subscription management with Stripe Customer Portal • Global middleware to protect logged-in routes • Local middleware to protect Server Actions / validate schemas • Activity logging system for any user events It's not finished, and there's probably bugs, but it's time to ship 🫡 • Demo: • Code:

Lee Robinson

362,870 Aufrufe • vor 2 Jahren

AI is changing what data teams are expected to build. Increasingly, the focus is on AI applications, agents, and automations that turn data into action. That’s why founder Zach Wilson is launching a free six-hour Databricks boot camp starting August 3, covering: • Building data systems with Lakebase and Postgres • Working with unstructured data, vector search, and retrieval • Building AI agents with Agent Bricks The course runs entirely in Databricks Free Edition and is designed for beginners and practitioners building practical AI data engineering skills. Join here:

AI is changing what data teams are expected to build. Increasingly, the focus is on AI applications, agents, and automations that turn data into action. That’s why founder Zach Wilson is launching a free six-hour Databricks boot camp starting August 3, covering: • Building data systems with Lakebase and Postgres • Working with unstructured data, vector search, and retrieval • Building AI agents with Agent Bricks The course runs entirely in Databricks Free Edition and is designed for beginners and practitioners building practical AI data engineering skills. Join here:

Databricks

21,458 Aufrufe • vor 1 Monat

🚨 LAUNCH ALERT 🚨 Tanplate is live 👉 🔁 RT for a launch discount code 👀 The ultimate TANSTACK AI boilerplate to build & ship AI/SaaS products fast. Everything you actually need. Nothing you don’t. AI-ready • Multi-model (OpenAI / Claude / Gemini / local-ready) • Streaming UI • Audio transcription (Whisper) • Multimodal context (text / images / files) Core stack • TanStack Start / Query / Store / Form • Vite • Docker • Drizzle ORM + Postgres Auth & Payments • Better Auth • Stripe • Polar Frontend • tailwindcss • shadcn External Services • Resend • S3 • i18n No boilerplate hell. No wiring for weeks. Just clone, tweak, ship.

🚨 LAUNCH ALERT 🚨 Tanplate is live 👉 🔁 RT for a launch discount code 👀 The ultimate TANSTACK AI boilerplate to build & ship AI/SaaS products fast. Everything you actually need. Nothing you don’t. AI-ready • Multi-model (OpenAI / Claude / Gemini / local-ready) • Streaming UI • Audio transcription (Whisper) • Multimodal context (text / images / files) Core stack • TanStack Start / Query / Store / Form • Vite • Docker • Drizzle ORM + Postgres Auth & Payments • Better Auth • Stripe • Polar Frontend • tailwindcss • shadcn External Services • Resend • S3 • i18n No boilerplate hell. No wiring for weeks. Just clone, tweak, ship.

Rayan

28,088 Aufrufe • vor 9 Monaten

stop using curl | sh to download *any* installer you find on the internet, stop using npm to download global dependencies this guy just built `safe`, a utility that inspects remote installer scripts before executing them, (all of this for just one star on github) it downloads the artifact, runs static analysis, checks against vulnerability databases and reputation sources, then decides whether it's safe to run here's an example showing Safe warning the user about the openclaw's npm install script: github:

stop using curl | sh to download any installer you find on the internet, stop using npm to download global dependencies this guy just built `safe`, a utility that inspects remote installer scripts before executing them, (all of this for just one star on github) it downloads the artifact, runs static analysis, checks against vulnerability databases and reputation sources, then decides whether it's safe to run here's an example showing Safe warning the user about the openclaw's npm install script: github:

forloop

14,417 Aufrufe • vor 3 Monaten

Chrome DevTools MCP is going to be a Swiss-Army knife MCP. Watch me use it here with Gemini CLI to open Google Scholar, search for a term, and save the top 5 PDFs to a local folder. I cannot stop thinking of all sorts of personal automation possibilities with this. Amazing release from the Chrome for Developers team!

Chrome DevTools MCP is going to be a Swiss-Army knife MCP. Watch me use it here with Gemini CLI to open Google Scholar, search for a term, and save the top 5 PDFs to a local folder. I cannot stop thinking of all sorts of personal automation possibilities with this. Amazing release from the Chrome for Developers team!

Delip Rao e/σ

239,248 Aufrufe • vor 1 Jahr