TgSQL
The smartest way to run SQL on the go
What it does
TgSQL: The Ultimate SQL Client for Telegram Manage your databases on the go with a full-featured Mini App. Supports MySQL & PostgreSQL with native SSH tunneling and military-grade encryption by default. Leverage our AI Assistant to generate complex queries from plain text instantly
Does the same job
all alternatives →
- CGChatGDB – GPT-Powered GDB Assistant2023 · github.com · ▲189
ChatGDB is a tool designed to superpower your debugging experience with GDB, a debugger for compiled languages. Use it to accelerate your debugging workflow by leveraging the power of ChatGPT to assist you while using GDB! It allows you to explain in natural language what you want to do, and then automatically execute the relevant command. Optionally, you can ask ChatGPT to explain the command it just ran or even pass in any question for it to answer. Focus on what's important - figuring out that nasty bug instead of chasing down GDB commands at the tip of your tongue. See it here:…
- GAGoSQL – A query engine in 319 LoC2024 · github.com · ▲81
I've always been curious about how SQL engines actually work. So I built a minimum viable SQL engine in Go. - Supports CSV files as tables - Supports SELECT, FROM, WHERE, LIMIT It's very simple: 1. Parses query string 2. Converts it into an AST representation 3. Executes the query against the CSV 4. Returns the results
- SDSnapQL – Desktop app to query Postgres with AI2025 · github.com · ▲104
SnapQL is an open-source desktop app (built with Electron) that lets you query your Postgres database using natural language. It’s schema-aware, so you don’t need to copy-paste your schema or write complex SQL by hand. Everything runs locally — your OpenAI API key, your data, and your queries — so it's secure and private. Just connect your DB, describe what you want, and SnapQL writes and runs the SQL for you.
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Parallel agents, diff reviewer, and multi-model comparisons
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- NW
Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
Life & fun · May 2026 · github.com
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Dev tools · May 2026 · github.com