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Meet the new Microsoft PostgreSQL extension for Visual Studio Code - packed with powerful tools to supercharge your PostgreSQL workflow, like: 🤖 Agent mode integration 💡 Schema visualization ⚡ Instant Docker PostgreSQL 🔎 IntelliSense-powered queries Learn more:

246,187 次观看 • 1 年前 •via X (Twitter)

10 条评论

Nick Muller 的头像
Nick Muller1 年前

Curious if this can finally replace Azure Data Studio’s Postgres plugin for me 😁

Manticore Search 的头像
Manticore Search1 年前

🚀 Star Manticore Search on #GitHub today and join our quest for the ultimate search solution!

Ben Dower 的头像
Ben Dower1 年前

i usually used to tell AI to make a script that sets up the database.

NFLComedySkits 的头像
NFLComedySkits1 年前

VS Code has been cooking lately! My next project will be creating a database so I need to try this extension 💯

Kingsley Uyi Idehen 的头像
Kingsley Uyi Idehen1 年前

Why is it DBMS-specific in the age of MCP (Model Context Protocol) and the existence of MCP servers for ODBC, JDBC, and

Vinayak Soni 的头像
Vinayak Soni1 年前

hi can we use mssql database with this? or any other mcp you know @code

ἀꑭατολή ✙ ➔ 的头像
ἀꑭατολή ✙ ➔1 年前

Finally

Andy Targaryen 的头像
Andy Targaryen1 年前

wow

Iliya Kisliy 的头像
Iliya Kisliy1 年前

@theo wdyt

Vishal 的头像
Vishal1 年前

This Microsoft PostgreSQL extension looks really useful. It has smart tools to make working with PostgreSQL easier and faster.

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Fetch.ai

38,274 次观看 • 1 年前

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 LLMs are trained on the internet, which is full of outdated Stack Overflow answers and quick-fix tutorials.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!

Avi Chawla

186,931 次观看 • 6 个月前