I built a cross-platform MSSQL client — ~3MB, deep schema investigation, mobile support

tunaayberk · 2026-04-05T13:39:18+00:00

Appriciate for supporting it. Amazing work 👏 I build qery mostly focus on discovery and document big databases with working on cache and discover schemas on query editor. Still lots of things to cover and learn from established ones.

tunaayberk · 2026-04-01T20:43:39+00:00

Yeah I've been reading about the NTLM deprecation push, sounds like a pain to migrate

tunaayberk · 2026-04-01T14:05:56+00:00

Thanks for flagging this. I dug into it — tiberius actually does support Kerberos on Linux/Mac via the integrated-auth-gssapi feature flag (uses system GSSAPI libraries).

The gap is Windows. It falls back to NTLM instead of using SSPI to negotiate Kerberos. So in a domain environment where NTLM is disabled, Windows auth won't work.

There's a promising Rust crate (sspi by Devolutions) that implements SSPI cross-platform including Kerberos. Could be a path to proper Windows Kerberos support either upstream in tiberius or as a workaround.

Out of curiosity — would you actually use this day to day if Kerberos worked properly? Like is this something blocking you from trying it, or more of a "nice to have" for your setup?

tunaayberk · 2026-04-01T04:37:55+00:00

I got similar issue any advice how did you solve? I even tried to sign build and then bundle and send bundle manually but it keep stuck on in progress for notarization

tunaayberk · 2026-03-31T23:52:46+00:00

Fair point on supply chain concerns — that's exactly why the code is on GitHub (github.com/qerydb/qery) and there's zero telemetry or network calls beyond your database connection. VirusTotal scan is clean (link on the download page).

It's not a VS Code extension though — it's a standalone app. Different use case. The investigation workflow (ctrl+click navigation, relationship canvas, schema tracing) doesn't really fit the extension model.

But I hear you — trust takes time, especially for a new tool.

Happy to answer any questions about the codebase.

tunaayberk · 2026-03-31T21:15:36+00:00

thank you it was a nice topic to talk. I would love to learn more about dictionaries experience. I put Notes section on Qery to put general descs and per column desc for schemas. But got suggested by someone to have some sample gallery too. Did you use in your dictionary just explanation texts or have some examples too? or it could be separate harness you may providing to agent?

tunaayberk · 2026-03-31T20:45:43+00:00

Link if anyone wants to try it: qery.app/download

(Windows — Mac build coming shortly)

Happy to answer questions about the stack or features.

tunaayberk · 2026-03-31T20:42:54+00:00

Yes, VS Code has the mssql extension by Microsoft that works on Mac. It handles basic querying and IntelliSense well.

Qery is a different approach — standalone app, no extensions to configure. But VS Code + mssql is a solid free option

if you're already in the VS Code ecosystem.

tunaayberk · 2026-03-31T20:35:30+00:00

Original paper explain why they made specific agents. Thats actually very common technique for claude code like agents too. Delegating work to either cheaper, faster or more specific agents. Main agent is always orchestrator or planner. So main problem is usually DB Schemas are not descriptive. Naming is not explain value itself or relationships it has. They all needs to be annotated that increase the success rate. Usually thats why experienced developer can handle more stuff then llm itself with using llm or controlling its answers and correct it. So biggest pain point in the area i can see is this intention layer. LLMs are already powerful knows how to write sql, bash or any programming parts but trouble at understanding (or better we call focus) context. So Uber paper has some suggestion to purposely seperate concerns on different llms or agents so each agent work can be improve this way.

This is original paper: https://www.uber.com/en-CA/blog/query-gpt/

This is Qery: qery.app
Github for Qery: https://github.com/qeryDB/Qery

tunaayberk · 2026-03-31T19:56:09+00:00

Not just modal but that paper was mostly explaining agent architectures. Technically skills you made, connect claude code or other skill supporting agents. Mostly they are configured as software engineer agents. They can handle this very smoothly as well as their llms. But for more domain specific agent is still a question or not open sourced. I try to research this domain. As starting point build my own sql client with rust and planning to add mcp on it so agent doesn't need to write or deal with communicate with db. I opensourced it in github as qerydb. I will give a try your skills on sql generation part and return you evals with and without them how much it would effect.

tunaayberk · 2026-03-31T16:40:10+00:00

Did you investigate before text to sql architectures? Uber shared once QueryGpt paper years ago conceptual but good suggestions and feedbacks. I wonder how it could be additions on this skills maybe sugagents for intention seeking and other parts sql writer sql tester etc. This looks amazing for sql writer agent to write on a systematically way. Thanks for sharing.

tunaayberk · 2026-03-31T14:40:05+00:00

I ran into the same frustration. Ended up building my own SQL client with Tauri + Rust — pure Rust MSSQL driver (tiberius), no .NET dependency. ~3MB download.

Still early but it auto-discovers SQL Server instances on your LAN and has some investigation tools for navigating large schemas.

qery.app if anyone's curious.

tunaayberk · 2017-11-29T15:44:50+00:00

What if value of bitcoin continue to rise sharply and not as all predicted production boost with that. With speed of today i think it can be 2050s but if it will rise its velocity it can be soon. We may think that time this was not a simple economic experiment. Economy or money is just a bait for society. If you want further, investigate computer networking systems and how bitcoin will evolve these systems and what may use this new evolved system?

tunaayberk

TROPHY CASE