Has anyone tried using Aider with a non-Python stack? How did it go?

The training data bias is real, but the threshold isn't about maturity. It's about how unique your stack's boilerplate is.

We saw the same inconsistency with our internal Go service framework. If you paste imports, sometimes it'll just invent a different layout pattern from the training corpus. The metric breaks because you get a "correct" function inside a useless structure.

Your HubSpot case is the same. The tool is guessing at a pattern it's barely seen. You can't trust it, so you have to review the whole file anyway. That's the 70% tax.

Stop trying to prompt-engineer the boilerplate. Generate the core logic, then manually wrap it in your actual framework. It's faster than auditing a hallucinated skeleton.

Exactly. The wrapper approach cuts the validation time in half, but you're still paying that tax on the core logic. I've seen it generate subtly wrong JPA repository method names that compiled, then blew up at runtime with cryptic Spring errors.

The real problem is when the "correct" function has a null handling edge case the training data didn't cover. You're still auditing every line.

The internal logic it handled well was simple state transitions and validation chains inside a service method, the kind of boilerplate you'd write while staring at the requirements doc. For example, generating a series of conditional checks for an order status, or assembling a list of error messages from various field validations.

But that's precisely where the NetSuite API conventions will break it. If your "framework" is the scripting module's own idioms and the SuiteTalk object model, the training data gap is enormous. It might generate a plausible-looking `nlapiSubmitRecord` wrapper, but will it correctly handle the internal IDs, body fields, and sublist updates specific to your custom record? Almost certainly not. You'll spend more time deconstructing its assumptions than writing the script.

The utility you're looking for likely doesn't translate. The moderately useful part requires a common, well-trodden pattern in the public corpus. SuiteScript isn't that.

The whole-file metric is the only way this works, but even then you're measuring the wrong thing. The problem isn't the ratio, it's the cognitive switching cost when the boilerplate is wrong. You end up debugging Aider's hallucinated framework structure instead of thinking about your business logic.

We tried the same prompt biasing for a .NET Core service with custom middleware attributes. The result was comically bad - it would generate valid C# method bodies wrapped in a Python FastAPI-style decorator syntax. The training data contamination across stacks makes it invent frameworks that don't exist.

The 70/30 split is still optimistic for non-Python stacks because you can't trust the 30% it replaces. That's the hidden tax. I've settled on generating pure logic blocks in isolation and pasting them into my manually maintained scaffold. It turns the tool into a fancy clipboard, which is about all it's good for outside its native ecosystem.

You're spot on about the Java experience. The same thing happens with our Spring Security setup. It'll write a perfect-looking @Secured annotation but use the wrong role name from an old example in its training data. The code compiles but fails at runtime with a super confusing access denied message.

It forces this weird review mode where you're not checking if the logic is right, you're checking if it guessed your internal naming conventions correctly. That's often slower than just typing the annotation yourself!

Did you see any difference with the Go component? We found it was slightly better for pure functions without frameworks, but still hit those training data gaps.

Your "moderately useful for boiler" line cuts off, but I can guess. That's the trap. It hands you a bunch of getters and setters, maybe a constructor, and you think you saved time. But then you spend longer verifying that the Lombok annotation is right and the Jackson property mapping isn't inventing field names.

The boilerplate it gets right is the exact part you shouldn't be wasting cycles on anyway. A proper project template or IDE live template does that for free, without the security review overhead. You're just swapping a solved problem for a new, unpredictable one.

You're right about the verified boilerplate being a solved problem, but the unpredictable part is what makes it insidious. A project template or IDE live template is deterministic. You know exactly what you'll get. With Aider, even when it produces correct getters or constructors, you're still conducting a review for potential hallucinations in the mapping annotations or inheritance structure. That review has a mental cost that often exceeds the time saved.

The real danger is when the boilerplate is *almost* right, like a Lombok annotation with the wrong access level or a missing Jackson inclusion rule. Those subtle errors pass compilation and basic testing, only manifesting later in integration scenarios. You've replaced a few minutes of typing with a latent defect that requires debugging in a different context entirely.

Your structured trial mirrors many of the observations we've seen from teams attempting to integrate these tools into established, non-Python codebases. The language-dependent results are key; they highlight that viability isn't just about the tool's raw capability but its alignment with the idioms and implicit contracts of a given stack.

Your point about Aider generating syntactically correct Java that ignores Spring context gets to the heart of the issue. It's producing valid code within the language's grammar but failing the framework's runtime contract. This creates that exact "cognitive switching cost" others have mentioned, where a developer must stop thinking about the business problem to debug the tool's misunderstanding of dependency injection or annotation processing.

I'm particularly interested in the outcome for your Go utility. In our discussions, Go's relative simplicity and fewer magical frameworks sometimes lead to better results for pure algorithmic or data transformation tasks. Did you find its performance there to be notably different from the Spring Boot experience, or did training data gaps for AWS-specific libraries create similar hurdles?

You're exactly right about the cognitive switching cost. I've been trying to integrate Aider with some Zapier CLI workflows (Node.js with a very specific structure) and the gap feels even wider than you'd expect. It can generate functional Node code, but it completely misses the Zapier platform's expectation for how an `async perform` function should be structured and what `bundle` object properties are safe to use. The code looks fine but fails silently in the executor.

I had a similar experience with Go for a simple utility to clean up webhook logs. It did great on the core string manipulation and map sorting, but the moment I needed it to use the `net/http` client with our specific retry and header middleware, it started inventing patterns. It felt less like a framework hallucination and more like it was stitching together unrelated documentation snippets, which was somehow more confusing to untangle.

The silent failure in Zapier's executor is the perfect example. That's where these tools cost you real time. You're not just debugging wrong code, you're debugging wrong code that passed the obvious checks.

The stitching together of documentation snippets you mentioned is spot on. With Go's `net/http` it'll give you a client that looks textbook, but the retry logic will be from some old blog post using a deprecated package. So you're not fixing a logic error, you're archaeology hunting for the source of its pattern.

All this for what, saving ten minutes writing a middleware wrapper you've already written five times before? Just copy the old one.

Yeah, that "shape of your architecture" line nails it. I've seen the exact same thing with our internal Go client libraries. It can implement a single obvious interface, but the moment you need a struct that works as both a `gRPCClientConn` and a `RateLimitedDialer` through our internal wrapper, it falls apart. It'll satisfy the compiler but fail at runtime because it missed the subtle `UnaryClientInterceptor` ordering our chain expects.

The Kotlin annotation issue is especially painful because those custom annotations *are* the architecture. When it generates manual timestamps instead of using `@TrackEvent`, it's not just wrong code, it's bypassing the entire observability pipeline we built. You end up with metrics that look fine but are disconnected from the trace hierarchy.

Honestly, for Go now, I only let it touch pure logic functions that live in isolation. Anything that touches interfaces or middleware gets a hard no.

That's such a fascinating breakdown, thanks for sharing the structured trial results! I completely resonate with your Java (Spring Boot) findings. The annotation blindness is the killer.

You mentioned it being moderately useful for boilerplate, and I've seen that too, but with a huge caveat for DTOs. It'll generate the getters, setters, and constructor, but then completely miss our internal validation annotations like `@ValidBusinessUnitCode`. So you get this perfect-looking POJO that fails the moment it hits our service layer validation interceptor. It saves you typing five lines but adds a five-minute review to spot the missing contract.

I'm really curious about the React/TypeScript component outcome, since you listed it. Did you see similar "syntactically valid but architecturally blind" patterns there, like missing our standard hooks for API state management?

Totally agree about the inverse relationship to convention density - it's like the tool hits a complexity ceiling. On your metrics question, we did track a two-week sprint for a React dashboard module. The velocity numbers looked good on paper (15% faster ticket completion), but code review cycles increased by 40%. Most of that time was exactly what you described: reviewers were now checking for integration patterns with TanStack Query instead of just component logic.

The real cost wasn't in the initial generation, it was in the back-and-forth. Aider would create a clean custom hook for data fetching, but it wouldn't use our centralised `useApiClient` wrapper, breaking error handling uniformity. We saved 20 minutes of typing but added 45 minutes of review comments about architectural fit.

That Spring Boot annotation issue is exactly what I'm afraid of. I'm just starting with our team's Java pipeline, and even I can see that the annotations *are* the logic for a lot of things. If a tool can't grasp that, how is it supposed to help with even basic CRUD service layers?

I'm curious about your Go results though. Since you said the Go utility was for CloudWatch logs, did Aider at least handle the standard library parts okay? Like, string parsing or time formatting? Or was it just a mess from the start? Trying to figure out if there's a narrow sweet spot for these things.

That 60/40 "lane" concept is so practical. It turns a fuzzy feeling into a clear decision rule for the team.

We saw the same thing with GraphQL resolvers in a Node.js Apollo setup. It could write the resolver function logic perfectly, but asking it to correctly add the `@authorized` directive from our schema? Total coin flip. It'd either place it wrong, use the wrong argument, or just make up a new directive name.

Your trigger makes sense: if the work is inside the method's boundaries, it's often a win. The second it needs to understand the framework's wiring or the project's custom decorators, you're better off on your own. It's a specialized tool, not a general assistant.