I've been skeptical about AI code completion for actual backend work—especially around complex data models and performance-critical paths. However, I recently needed to build an internal VS Code extension to visualize PostgreSQL query plan differences between branches, and decided to let Copilot handle the initial grunt work. The result was surprisingly effective for the non-algorithmic boilerplate.
The extension's core is, of course, the logic that parses `EXPLAIN ANALYZE` output and performs a diff, which I wrote manually. But Copilot excelled at generating:
- The entire `package.json` manifest with activation events, commands, and views.
- The boilerplate for the Webview panel setup and lifecycle management.
- Standard event handlers and disposal patterns.
For example, after I typed a comment describing the need for a tree data provider, it generated this scaffold:
```typescript
export class QueryPlanProvider implements vscode.TreeDataProvider {
private _onDidChangeTreeData: vscode.EventEmitter = new vscode.EventEmitter();
readonly onDidChangeTreeData: vscode.Event = this._onDidChangeTreeData.event;
refresh(): void {
this._onDidChangeTreeData.fire();
}
getTreeItem(element: PlanNode): vscode.TreeItem {
return element;
}
async getChildren(element?: PlanNode): Promise {
// ... it left the async fetch logic for me to fill, but set up the structure.
}
}
```
This eliminated perhaps 80% of the tedious, repetitive code, letting me focus on the Postgres-specific parsing and the diff algorithm. The key was providing very precise comments—almost like writing the spec for a junior dev.
My main observation for backend developers: Copilot is weak on nuanced database logic (it suggested a few N+1 query patterns!), but **exceptionally strong at generating the scaffolding for plugins, CLIs, and standard API wrappers**. It saved me roughly a day of setup.
Has anyone else used it for tooling or extension development? I'm curious if the efficiency gain holds for more complex backend tooling, like a Redis cache inspector or a gRPC service mock generator.
-- latency
sub-100ms or bust
Hi there, I'm a backend lead for a mid-sized fintech, and I run our marketing automation stack and directly manage several internal tools teams. My day-to-day is split between our customer data pipelines built on Postgres and the marketing systems that act on that data - Mailchimp for broad campaigns, ActiveCampaign for lead scoring, and SendGrid for transactional emails, so I'm deep in both code and configuration.
Since you're building a VS Code extension and are evaluating AI code tools for practical backend and tooling work, here's how I see the landscape based on hands-on use and my team's experiments.
1. **Development Velocity for Boilerplate:** GitHub Copilot is the clear winner for the initial 80% of standard scaffolding, exactly as you found. It consistently generates accurate `package.json` entries, class skeletons, and common event handler patterns. For a project like your query plan visualizer, it cuts the setup time from a day to maybe an hour. However, as soon as you hit the core 20% - your unique diffing logic - the suggestions become distracting noise, and you need to turn it off to think.
2. **Accuracy on Complex Logic:** This is the main limitation for backend work. For anything involving nuanced data models, performance-sensitive database calls, or custom algorithms, Copilot will confidently suggest plausible but subtly wrong implementations. I've seen it generate N+1 query patterns inside loops, suggest outdated API signatures, and miss crucial edge cases in data transformation code. You must review every line it writes for the complex parts.
3. **Real Cost Consideration:** Copilot is $10/user/month flat, which is fine for an individual. The hidden cost is the developer context switching. It's not a set-and-forget tool; it's a conversation. For a team of 10 engineers, that's $1,200 a year, and you need to factor in the time spent correcting bad suggestions versus just writing the code yourself for complex modules.
4. **Integration with Existing Codebases:** Where it shines, beyond greenfield projects like yours, is when you're working within a large, well-established codebase with clear patterns. If you have a standard way of creating services or defining data access layers, Copilot learns those patterns quickly and can be a genuine accelerator for adding new, similar modules. It struggles in fragmented or legacy environments without consistent style.
My pick is to keep using Copilot for projects precisely like your VS Code extension - focused, greenfield tooling with a high proportion of standard framework code. I would not recommend it as the primary driver for the core, complex backend services in your main application, especially around data integrity and performance. To make a cleaner call for your team, tell us the size of your engineering team and what percentage of your work is maintaining existing business logic versus building new internal tools.
don't spam bro
That scaffold it gave you is classic. I've seen it spit out the exact same TreeDataProvider boilerplate for three different extensions. It's great for the 80%, but watch the disposal patterns it suggests. I've had to fix leaks where it generates `this._onDidChangeTreeData.dispose()` but misses registering it with the context subscriptions.
YAML all the things.
Yeah, that TreeDataProvider boilerplate is a perfect example. It's impressive how consistent it is across projects, but you're right about the disposal. I've also noticed it can be overly aggressive with creating new EventEmitter instances when sometimes a simpler pattern would do.
The real test is when you start adding dynamic data sources. Copilot might wire up the refresh methods but then miss the subtle race conditions on event firing. You still need to understand the underlying VS Code API lifecycle, or that last 20% becomes a debugging nightmare.
Connecting the dots.