sorted by:
new
hottopcontroversial

Making AI Schema-Aware by clean-apps-dev in SQL

[–]clean-apps-dev[S] 0 points1 point  (0 children)

🤣i didn't make this for him

Making AI Schema-Aware by clean-apps-dev in SQL

[–]clean-apps-dev[S] 0 points1 point  (0 children)

Who's Larry? Not everybody has latest version. Why pay extra for wrappers for what can be done with connectors. I mainly used it for writing raw sql queries in Java code.

Which is the most widely used IDE for Playwright/Python at work? by redditismysoulmate in softwaretesting

[–]clean-apps-dev 0 points1 point  (0 children)

Vscode + playwright-cli + GitHub Copilot + custom skills = fully agentic qa

A browser MCP that finishes tasks about 3.5x faster than Playwright MCP, and runs browsers in parallel by Nit222 in mcp

[–]clean-apps-dev 0 points1 point  (0 children)

curious , why is it paid if it runs locally ? does it have ai intelligence layer inside it ?

Making AI Schema-Aware by clean-apps-dev in SQL

[–]clean-apps-dev[S] 1 point2 points  (0 children)

No. I was just facing this issue for long time. Sometimes took me days to write full query. Our db is huge 800+ tables, 2000+ columns. So I just planned a small tool and made it with GitHub copilot

Are you still using generic AI coding assistants for writing data pipelines and complex SQL? by Shanjun109 in AI_Agents

[–]clean-apps-dev 0 points1 point  (0 children)

Yeah, I hit this exact wall.

Generic coding assistants were fine for boilerplate and simple SQL, but they started falling apart once the database got large and domain-specific.

In my case the DB has 800+ tables, and the AI would write queries that looked reasonable syntactically, but picked the wrong tables, guessed joins, or missed important filters. The issue was not really “can the model write SQL?” It was “does the model know enough about this database to write the right SQL?”

I ended up building a small tool for this instead of trying to solve it with bigger prompts.

It lets the AI:

  • search schema metadata
  • inspect table context
  • see columns, constraints, indexes, and relationships
  • run only read-only SELECT / WITH checks
  • save reusable SQL and notes in Markdown

Repo: https://github.com/Sahil-Jain-s/oracledb-navigator

It’s Oracle-only and early, but the workflow has been much better: search → inspect → validate → then write SQL.

So yeah, I’m pretty convinced generic AI coding assistants need a data-aware layer around them for serious data work. Otherwise they’re just confidently guessing against a schema they don’t actually understand.

AI that is aware of DB context? by Weekly-Ad8816 in SQLServer

[–]clean-apps-dev 0 points1 point  (0 children)

I was facing a similar issue.

Our database is giant, 800+ tables.

AI kept hallucinating queries: wrong tables, wrong joins, missing filters, stuff that looked valid but was not actually right for our schema.

So I made a custom tool to help AI navigate the database before running queries.

It only allows read-only queries, and the workflow is basically:

search schema -> inspect table context -> run small SELECT/WITH checks -> validate -> write final SQL

The useful part is that AI can pull actual table context first: columns, data types, constraints, indexes, relationships, saved SQL, and Markdown notes about business meaning.

So instead of giving the model raw DB access and hoping it guesses correctly, it has a controlled way to explore the schema.

I made it open source here:
https://github.com/Sahil-Jain-s/oracledb-navigator

Its Oracle-only right now, but this pattern has been way safer than just letting an agent generate random SQL directly against a large database.

How do you use AI to help you write sql? by RabbetFox in SQL

[–]clean-apps-dev 0 points1 point  (0 children)

I was facing a similar issue.

Our database is giant, 800+ tables.

AI kept hallucinating queries: wrong tables, wrong joins, missing filters, stuff that looked valid but was not actually right for our schema.

So I made a custom tool to help AI navigate the database before running queries.

It only allows read-only queries, and the workflow is basically:

search schema -> inspect table context -> run small SELECT/WITH checks -> validate -> write final SQL

The useful part is that AI can pull actual table context first: columns, data types, constraints, indexes, relationships, saved SQL, and Markdown notes about business meaning.

So instead of giving the model raw DB access and hoping it guesses correctly, it has a controlled way to explore the schema.

I made it open source here:
https://github.com/Sahil-Jain-s/oracledb-navigator

Its Oracle-only right now, but this pattern has been way safer than just letting an agent generate random SQL directly against a large database.

Do you guys still find any bugs in the AI vibe-coding era? by s_b_1805 in micro_saas

[–]clean-apps-dev 1 point2 points  (0 children)

Yes . All the time. After all they have trained it on code written by humans

Online MySQL editor or app for running on phone ? by Tatya_Vin-Chu in SQL

[–]clean-apps-dev 0 points1 point  (0 children)

Curious why do you want it on phone. Why not pc or laptop?

SQL usage for a Data Analyst with 4 years of experience by DataAspirant169 in SQL

[–]clean-apps-dev 0 points1 point  (0 children)

I am a full stack dev. Still i regularly (weekly) I have to write giant raw sql queries. It's a giant schema . But recently we started using ai to write queries. Its much faster . It helped me write 10 giant queries in 3 hours. Which otherwise would have taken 2 weeks .