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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

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137,289 görüntüleme • 5 ay önce •via X (Twitter)

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nless is a TUI tool that is like Excel for live log data. It takes in logs, CSV or JSON and transforms them into columns in the terminal. You can then filter, sort, search, stream live data and more. Matt Pryor (mpryor on GitHub) made nless using textualize.io and is Terminal Tool of the Week! ⭐️
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Excited to announce the new era of terminal apps! 🎉 🐀 ratSCAD — CAD for the terminal! 🧀 Terminal IDE for OpenSCAD with live 3D previews. 🦀 Made possible by Ratty terminal: ⭐ GitHub: #rustlang #ratatui #tui #3d #openscad #terminal
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Excited to announce the new era of terminal apps! 🎉 🐀 ratSCAD — CAD for the terminal! 🧀 Terminal IDE for OpenSCAD with live 3D previews. 🦀 Made possible by Ratty terminal: ⭐ GitHub: #rustlang #ratatui #tui #3d #openscad #terminal

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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.
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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.

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All types of databases, Redis, Postgres, Clickhouse, Mysql etc. now display important stats and metrics aswell as most run queries in the Maple service map
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Wanna quickly edit files in the terminal? Say less 🤝 🌀 lazyide — A lightweight TUI IDE inspired by lazygit 💯 View, edit, navigate & copy/paste instantly. 🎨 Supports file tree, tabbed editor, LSP & themes! 🦀 Written in Rust & built with Ratatui ⭐ GitHub: #rustlang #ratatui #tui #ide #editor #terminal #lsp #devtools
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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.
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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.

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v0 now supports MCPs for Stripe, Supabase, Neon, and Upstash. Query databases, seed data, and ask about revenue, all with natural language and zero setup.
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v0 now supports MCPs for Stripe, Supabase, Neon, and Upstash. Query databases, seed data, and ask about revenue, all with natural language and zero setup.

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Finally we have notebooks in the terminal! 🔥 📓 newt — A cross-language notebook for the terminal 💯 Run Rust, Go, C++, Python & JavaScript cells in a TUI 🚀 Also supports a GUI backed by WASM or locally 🦀 Written in Rust & built with Ratatui ⭐ GitHub: #rustlang #ratatui #tui #terminal #notebooks #devtools #opensource

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Use Neon and ClickHouse to get the best of both worlds: the flexibility of Postgres for transactions + the performance of ClickHouse for analytics With PeerDB, data streaming is simple 👇

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The massive construction going on at the MMIA is one of Nigerias most audacious project in its history! Even the temporary terminal is alot more beautiful than the old terminal we had.. Watch and send some prayers for PBAT
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The massive construction going on at the MMIA is one of Nigerias most audacious project in its history! Even the temporary terminal is alot more beautiful than the old terminal we had.. Watch and send some prayers for PBAT

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HUGE news for developers. Supabase just launched the ChatGPT of databases. An AI-based Postgres service. You can build and launch databases, create charts, embeddings, see visuals of your DB, generate sample data, and more. And.. it's 100% open source.
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HUGE news for developers. Supabase just launched the ChatGPT of databases. An AI-based Postgres service. You can build and launch databases, create charts, embeddings, see visuals of your DB, generate sample data, and more. And.. it's 100% open source.

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using tools, Cursor Agent have the capability to reason about when it should - read & write code - search your codebase - call MCP servers - run terminal commands an example where it connects to a postgres database, runs a query and writes to file
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I wrote a full terminal emulator in PHP! And I wrote a testing tool that takes screenshots of the terminal to verify that my emulator is working!

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Multigres is the future of Supabase, scaling your database indefinitely. I spent a few days with the Multigres team. They have scaled some of the world’s largest databases. Now they are doing it for the postgres ecosystem. See how:
1:00:13

Multigres is the future of Supabase, scaling your database indefinitely. I spent a few days with the Multigres team. They have scaled some of the world’s largest databases. Now they are doing it for the postgres ecosystem. See how:

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