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🚀 Launched today 2.0 🐘 Modern Postgres Terminal. Fast. Native. Written in Rust. Everything you already had in the shell - now with a native TUI and PgBot built in. → SQL - write queries and inspect results → Data - browse schemas, tables and rows → Branches -... show more

alex shapalov

3,167 subscribers

66,979 次观看 • 2 个月前 •via X (Twitter)

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23 条评论

ActiveRabbit 的头像

ActiveRabbit2 个月前

Great design!

alex shapalov 的头像

alex shapalov2 个月前

tnx, open for contribution)

Sujal 的头像

Sujal2 个月前

what pushed you to native over a tui? curious whether it was rendering speed or just wanting proper mouse support in the terminal.

alex shapalov 的头像

alex shapalov2 个月前

it's a tui, that was a mock and terminals do mouse fine OSC 22 even gets you a real hand cursor. it needs to run over ssh, so native was never on the table)

Matt DeLuco 的头像

Matt DeLuco2 个月前

You should have lead with no electron! 🎉

alex shapalov 的头像

alex shapalov2 个月前

yee, and i lead with Rust!

alex shapalov 的头像

alex shapalov2 个月前

Worth trying:

The Chieftain A 的头像

The Chieftain A2 个月前

Amazing

alex shapalov 的头像

alex shapalov2 个月前

Thanks. 🙏 Some more updates will be soon

Axiom 🔬 的头像

Axiom 🔬2 个月前

The safety model is the product edge here. A DB agent needs a signed query plan, row-limit, mutation gate, explain trace, and rollback handle next to the TUI. Otherwise ask your database becomes let a chatbot discover DELETE.

Alexander Holbreich 的头像

Alexander Holbreich2 个月前

Actualy looks like fun!

alex shapalov 的头像

alex shapalov2 个月前

Yes, fan and useful

Christian Lempa 的头像

Christian Lempa2 个月前

That looks great

alex shapalov 的头像

alex shapalov2 个月前

Tnx for feedback

هزيم الرعد 的头像

هزيم الرعد2 个月前

is there an in terminal browser ?

alex shapalov 的头像

alex shapalov2 个月前

A browser based terminal wouldn’t be secure

هزيم الرعد 的头像

هزيم الرعد2 个月前

would be cool tho

alex shapalov 的头像

alex shapalov2 个月前

Maybe for test db, or I am not sure about security…

pgrun.dev 的头像

pgrun.dev2 个月前

install: curl -fsSL | sh

alex shapalov 的头像

alex shapalov2 个月前

Yes, the installation process is the same for all versions.

PuneetSingh��️ 的头像

PuneetSingh��️2 个月前

native TUI + PgBot is a nice agent surface — curious if PgBot gets schema/table browse as tools with structured returns, or if it's still chat-over-SQL. structured row previews beat dumping query text into the context window

Leo Oliemans | Refinery 的头像

Leo Oliemans | Refinery2 个月前

A native Postgres terminal plus a bot sounds useful for inspection. I’d keep the mutation boundary very visible, though: read, explain, propose, then explicitly execute. Especially when the result looks plausible but the target rows changed meanwhile.

alex shapalov 的头像

alex shapalov2 个月前

No Electron!

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🚀 launched today: 🐘 Postgres branches for every AI agent. → build a Postgres branch in ~3 seconds → production-like data → isolated database for every agent → sensitive data automatically masked → branch → work → delete Already ~100 Postgres branches/week before public launch. Now we're building the Postgres runtime for agents.

🚀 launched today: 🐘 Postgres branches for every AI agent. → build a Postgres branch in ~3 seconds → production-like data → isolated database for every agent → sensitive data automatically masked → branch → work → delete Already ~100 Postgres branches/week before public launch. Now we're building the Postgres runtime for agents.

alex shapalov

113,130 次观看 • 1 个月前

We re-architected Neon Auth to integrate it directly into the Neon platform. Auth now lives in your Postgres database and branches with your data. Built on Better Auth tech and available today 🧵

We re-architected Neon Auth to integrate it directly into the Neon platform. Auth now lives in your Postgres database and branches with your data. Built on Better Auth tech and available today 🧵

Neon Postgres

35,061 次观看 • 11 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 🔥

🚨 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 🔥

Charly Wargnier ♨️

20,190 次观看 • 9 个月前

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

94,406 次观看 • 11 个月前

💻🗄️ SQL TUTORIAL FOR BEGINNERS: LEARN HOW DATABASES REALLY WORK ⚠️ EDUCATIONAL DISCLAIMER: This content is strictly for educational purposes, database learning, and cybersecurity awareness. Practice using sample databases, your own projects, or authorized lab environments. Never access, modify, extract, or delete data from databases without explicit permission. Always protect sensitive information and follow applicable laws. 🔍 WHAT IS SQL? SQL (Structured Query Language) is used to communicate with relational databases. It allows developers to retrieve, organize, insert, update, and delete data. ### 🔑 8 SQL CONCEPTS EVERY BEGINNER SHOULD KNOW 1️⃣ Tables and Databases Databases organize information into tables containing rows and columns. For example, a customers table might store customer IDs, names, and email addresses. 2️⃣ SELECT — Retrieve Data Used to choose which columns you want to view. 3️⃣ WHERE — Filter Records Allows you to retrieve records that match specific conditions. 4️⃣ ORDER BY — Sort Results Organizes results in ascending or descending order. 5️⃣ Aggregate Functions Functions such as COUNT(), SUM(), and AVG() help summarize data. 6️⃣ GROUP BY — Organize Data into Groups Groups records with common values so you can calculate totals and statistics for each group. 7️⃣ JOIN — Connect Related Tables Combines related information from multiple tables using matching columns. 8️⃣ Primary and Foreign Keys Primary keys identify records uniquely, while foreign keys help establish relationships between tables. ### 🛡️ WHY SQL MATTERS IN CYBERSECURITY Understanding SQL helps security professionals recognize database risks, investigate application behavior, and understand vulnerabilities such as SQL injection. Developers can reduce SQL injection risks by using parameterized queries, validating input appropriately, and restricting database permissions. 🎯 KEY LESSON: Don't just memorize SQL commands. Understand how data is stored, how queries retrieve it, and how databases should be protected. Start with sample data, practise each concept, and build your skills step by step. 🔐 HACKWITHBANKS | DATABASE & CYBERSECURITY EDUCATION #Hackwithbanks #SQL #tutorial #database #Cybersecurity

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Hackswithbanks

25,600 次观看 • 10 天前

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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 • 1 年前

AI workloads need databases built for scale. Watch how OceanBase supports modern data infrastructure. Follow OceanBase for more insights on distributed SQL, cloud-native databases, and AI-ready architecture.

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OceanBase

8,134,345 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

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Neon Postgres

26,526 次观看 • 1 年前

sqlit is a lazygit style SQL TUI for the terminal. It supports 10+ databases (Postgres, SQLite, Supabase, ClickHouse, DuckDB, etc), has query history, autocomplete and more. Peter Adams (Maxteabag on GitHub) made sqlit using textualize.io and is Terminal Tool of the Week! ⭐️

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Terminal Trove

138,230 次观看 • 10 个月前

Today ✅ : Learning PostgreSQL - learned about queries and pg library -made a simple signup page with PostgreSQL db - learned about how SQL injection works and how to prevent insert queries from it - basic relationships in postgres db Prolly need to start learning C++ too for sem exams

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Sam ☕

21,908 次观看 • 3 个月前

YC W23's Brewit allows you to use plain English to write SQL, create charts, and generate dashboards/reports based on your database. With built-in data catalogs and monitoring tools, Brewit ensures its accuracy and capability to self-improve.

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25,023 次观看 • 2 年前

Replit Agent (launched today) built a startup landing page with waitlist and postgres db from start to finish in <4 mins

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aman

237,308 次观看 • 2 年前

Excited to launch Data Explorer in today! 🚀 We built this to eliminate tedious data validation steps. Now you can easily inspect and validate data at each step and ask Julius to edit it - all in one place. Feedback welcome!

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rahul

22,669 次观看 • 1 年前

most people stick to just 1 or 2 tables they are familiar with when writing their SQL queries in we built the ability to search for tables, preview tables and easily sample rows from a table so you can find the ones you need + even discover new tables data discovery is a big problem even in small companies. most data doesn't get analyzed because most of company doesn't know it exists

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rahul

11,550 次观看 • 1 年前

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 lets you run complex graph queries, like finding shortest paths using standard SQL. Directly in postgres. Coming next: Polygres, give your postgres a warp drive by combining pgGraph & pgVector into an all-in-one database. We're building the foundation of intelligence evokoa (YCF26) Links below ↓

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dale

15,146 次观看 • 5 个月前

SQL Studio is now in public beta 🚀 A modern, cross-platform SQL client built for speed and clarity. SQLite supported. More dialects coming. Bugs included 🪲 👉

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Antfroze

181,835 次观看 • 10 个月前

We built a SQL and NoSQL co-pilot: Generate an answer with accompanying code by typing a question to ask your database - built in code editor - AI sanity check - schema explorer - shareable perma-links to past queries Join the private beta here:

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Use Bink

74,965 次观看 • 3 年前

GORI(고리) is now public! A keyboard-native TUI proxy built for pentesters and hackers. Intercept, edit, and repeat traffic right in your terminal with pure muscle memory flow. Happy Hacking in Terminal :) Github: Install:

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HAHWUL

10,524 次观看 • 3 个月前

GPT-5.6-Sol-Ultra built a Doom-like game in SQL: the game is running in the terminal on the left, while the SQL powering the game in the terminal on the right. How it works: DOOMQL is a game engine implemented in 2,000+ lines of SQL. Every frame, SQL raycasts the scene, calculates every RGB pixel, and encodes those pixels as coloured terminal characters. The same SQL then handles controls, movement, collision, enemy AI and combat, while Python connects SQLite to the keyboard, clock and terminal.

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122,407 次观看 • 4 个月前