We've noticed backend teams usually care less about instant output and more about inspectable actions.

Especially when a task affects workspace setup, module choices, or project structure.

Would you trust an AI tool more if it showed a plan first and asked for confirmation before making changes?

For some teams it's the initial scaffold. For others it's the first module wiring, config setup, or figuring out what should even be in the stack.

If an AI tool could eliminate one step in that process entirely, which one would save you the most time?

When you ask AI to debug a backend issue, what output is most useful?

We've seen a few different expectations:

- probable root cause

- likely affected files

Hey everyone

We launched Workspai on Product Hunt today and wanted to share it here.

The problem we kept running into: every AI session starts from zero. You re-explain your stack, layout, and conventions every single time.

Workspai is a free VS Code extension that reads your backend workspace before every AI prompt project structure, installed modules, git changes, and team conventions and injects it automatically. Works with GitHub Copilot, no extra subscription.

f you had to choose one for first-class workspace-aware AI support, which would you pick first?

- Django

- Express

- Spring

One failure mode we keep noticing is the model recommending a package, module, or integration that sounds plausible but isn't really there.

The answer feels smart, but it creates even more cleanup.

Have you run into that more than generic architecture mistakes?

We've been thinking about this while shipping `@workspai` with `/ask` and `/debug` directly in the VS Code Chat panel.

A lot of AI tooling assumes one chat box is enough.

For backend work, we're not sure that's true.

Quick questions, structured debugging, and workspace-level actions often feel like different jobs.

Frontend often has visible feedback loops fast.

Backend work is different: multiple files, runtime config, infra assumptions, module boundaries, database state.

Where do current AI tools usually fail first in your backend workflow?

Hey PH!

I'm one of the founders of Workspai been building in the backend tooling space for the last year and finally ready to share what we've been working on.

Quick background: we started with RapidKit, an open source backend workspace platform for FastAPI, NestJS, and Go. The more we used AI tools alongside it, the more frustrated we got with the same problem AI has no idea what your project looks like. You repeat yourself every session.

Workspai is our answer to that. It's a VS Code extension that reads your backend workspace structure, modules, git changes, team conventions and feeds that context into the AI automatically. Works with GitHub Copilot, no new subscription.

We're thinking a lot about team memory before launch.

Not just file context, but the repeatable rules behind a codebase: naming conventions, architecture decisions, boundaries between modules, deployment rules.

For us, the biggest gap has been architecture context.

An AI can see the file you're in, but it usually doesn't know your project structure, installed modules, naming conventions, or recent changes.

If you could make an AI tool automatically understand 3 things about your backend workspace before every answer, what would they be?

Hey! We're launching Workspai on Tuesday a VS Code extension that gives AI tools full workspace context before you type anything.

The problem we kept hitting: every AI session starts from zero. You re-explain your project, your stack, your conventions every single time.

Curious what frustrates you most about current AI dev tools. Context amnesia? Generic answers that don't match your actual project? Something else?

Would love to hear what you'd want an AI tool to actually know about your workspace.

With the advent of clawdbots, it's as if we've all lost our inhibitions and "put our lives completely in their hands."

I'm all for delegating work, but not giving them too much personal/sensitive stuff to handle.