Used Ghostwriter for a few months now, trying to push it. The marketing makes it sound like a silver bullet.
Spoiler: it's not. It's fine for boilerplate, quick API calls, or refactoring simple functions. But for anything with actual business logic? It hallucinates libraries, makes up syntax, and writes insecure code if you're not watching every line. You still need to know *exactly* what you're doing.
My verdict: good assistant, terrible engineer. You wouldn't ship its code without a thorough review. So, "production code"? Only if you enjoy debugging AI-generated spaghetti. 🍝
CRM is a means, not an end.
Exactly my experience. It's a productivity multiplier for the boring stuff, not a replacement for thinking.
The real question is ROI - does the time saved on boilerplate outweigh the extra time spent reviewing and fixing its "creative" interpretations of your prompts? For well-defined, repetitive tasks, yes. For logic that's complex or unique to your domain, it's a net loss.
I've seen teams treat it like a junior dev: give it clear, narrow specs and expect to review everything. Works fine under those rules.
Ask me about hidden egress costs.
This nails it. The key mistake is treating it as an engineer rather than a specialized tool.
It's like using AWS Lambda for everything because it's serverless. Great for event triggers and APIs, disastrous for stateful or long-running processes. You still pick the right tool.
Ghostwriter's "hallucinations" are a direct TCO issue. If you're not catching them, you're paying for that bug fix in production later, at 10x the cost.
Show me the bill
"good assistant, terrible engineer" is a generous way to put it. The real question is how much that 'assistant' actually costs when you factor in the mandatory review time.
If you need to watch every line and know exactly what you're doing, the value proposition crumbles for anything but the simplest scripts. You're paying a subscription for a faster typist that occasionally inserts subtle bugs. That's a weird premium feature.
always ask for a multi-year discount
That line about needing to know exactly what you're doing is so true. It reminds me of handing a complex task to a new intern without any context - you wouldn't do it.
The real value for my team has been in those specific, boring areas you mentioned. Getting it to draft a standard API endpoint structure or a repetitive config file saves mental energy. But we treat the output like a first draft that always has errors, never a final product.
If you go in expecting it to "engineer" a solution, you'll have a bad time. If you use it to skip the tedious first 30% of a well-understood task, it can genuinely help.
Totally feel the "first draft that always has errors" mindset. That's exactly how I've started using it for our data pipeline configs, like generating the skeleton for a new Airflow DAG. It gets the basic operators and dependencies down, which is the boring part I hate, but I still have to go in and fix the imports and set the right retry delays every single time.
The intern analogy is perfect. I wouldn't ask an intern to design the whole pipeline, but I'd absolutely have them start the YAML for a new dbt model. It saves that initial friction.
My question is, where do you draw the line on "well-understood task"? I found it starts to fall apart the moment there's any company-specific logic, even if the overall pattern is repetitive. How does your team define that boundary?
null
Your boundary question is exactly where our team formalized a rule: we only let Ghostwriter touch code that's already templated in our internal docs. If we have a documented pattern for, say, a Lambda function with our standard observability wrapper and IAM permissions pattern, it can draft the 80% boilerplate. The moment the task requires synthesizing *two* such patterns or applying a business rule not in those templates, it's off-limits.
It's less about "well-understood task" and more about "verbatim reproduction." Think of it as a fancy paste-from-example tool, not a synthesis engine. The Airflow DAG skeleton is a perfect example - you're having it reproduce a known structure, not invent the orchestration logic.
Your point about company-specific logic is the critical failure mode. It has no domain knowledge. We tried having it implement a retry handler using our internal circuit breaker library, and it invented an API that didn't exist. The cost of that "first draft" was higher than writing it from scratch because we had to debug its fiction.
This "fancy paste-from-example tool" framing is spot on. It's the only way I've seen it work without creating technical debt.
Our line is similar, but we define failure by time. If it takes longer to give the AI the perfect context via examples and prompts than to just write the function, you've lost. The retry handler example is a perfect case of that - you spent time explaining the library only to get nonsense back. That's a net negative ROI.
The real test is: could a competent junior dev do this by copying from an internal README? If yes, Ghostwriter might save 10 minutes. If no, you're the one doing the synthesis, and the AI is just a middleman that adds risk.
Oh that "TCO issue" part hit home for me. I'm still new to this, but I saw our team spend a whole afternoon fixing a bug that came from an AI-generated API client. It was so subtle, like a missing retry config.
The Lambda analogy helps me understand why it feels unpredictable. Is there a way to *know* when you're in that "event trigger" territory where it's safe versus the "stateful process" where it'll probably break? Or is it just trial and error?