Just tried Codeium on a legacy Django 4.2 project. Its completions are aggressively wrong, suggesting patterns deprecated in Django 2.0.
Example: I type a model field and it autocompletes with `on_delete=models.CASCADE` placed *outside* the ForeignKey call. That syntax hasn't worked for half a decade.
```python
# Codeium's suggestion (broken):
client = models.ForeignKey(Client, on_delete=models.CASCADE, null=True)
# Correct placement:
client = models.ForeignKey(Client, null=True, on_delete=models.CASCADE)
```
It also keeps pushing `url()` in `urls.py` instead of `path()` or `re_path()`.
* Is this a training data issue?
* Can you retrain or configure it on a per-project basis using `requirements.txt` or the Django version?
* Or do I just turn it off for `.py` files and eat the compute cost of running a local model instead?
The "cost" here is tech debt and broken migrations. Show the math.
show the math
Your example with the ForeignKey parameter ordering is especially problematic because it creates a silent failure. The code will run, but `on_delete` becomes a positional argument, which Django ignores, defaulting to CASCADE anyway. You'll only spot it during a migration or, worse, in production when a deletion behaves unexpectedly.
I suspect it's both a training data issue and a context window limitation. These models are trained on massive GitHub scrapes full of deprecated tutorials and legacy codebases. Even with a `requirements.txt` present, the tool likely prioritizes statistical patterns in its training corpus over your project's specific environment markers.
For Django work, I've had to disable inline completions entirely and rely only on the chat/command features for targeted queries. The compute cost of a local model is real, but so is the time spent debugging incorrect migrations. The math rarely favors the AI when working with opinionated frameworks with breaking changes between major versions. Have you looked into whether Codeium allows you to create custom snippet blocks or pattern denylists? That might be a middle ground.
Support is a product, not a department.
You're spot on about the silent failure - that's the real danger with these "statistically probable" completions. It's not just a style issue, it's a logic bomb.
The training data corruption point is key. These models ingest everything, and the signal-to-noise ratio for framework-specific syntax is terrible. I've seen similar junk with old Kafka producer configs. The sheer volume of outdated tutorials and abandoned repos pollutes the corpus.
Your workaround of disabling inline completions is painful but pragmatic. It feels like we're treating a tool designed for acceleration as a sometimes-useful reference manual, which kinda defeats the point. I wonder if there's a way to feed it a curated set of your own project's correct patterns as a mini-corpus, overriding the global noise? Some IDEs let you seed with existing codebases.