Been using Codeium in PyCharm for my Python scripts and it felt like a game-changer at first. Lately, though, the suggestions seem off. It’s like it’s guessing more and completing less.
For example, when I’m working on GitLab CI YAML or even basic Jira automation scripts, it’s offering irrelevant completions or just repeating what I already typed. I’m on the latest version. Has anyone else noticed a dip in relevance? Wondering if it’s just my setup or a wider thing.
I've been using Codeium in VS Code for my Helm charts and Terraform HCL, and I've noticed the same thing. It's especially bad with GitLab CI YAML - I had it repeatedly suggest `image: alpine:latest` when I was clearly writing a multi-stage build with specific image tags. Almost like it's reverting to the most generic completions in its training data instead of learning from my current context.
One thing I've wondered: does the "dip" correlate with them switching to a newer model version? I recall a similar slump when GitHub Copilot moved from Codex to GPT-4 and then back again. The completions got more "safe" but less useful for niche YAML structures.
I'm curious if you've tried the "Explain" or "Chat" features for those GitLab CI scripts? I find those still surprisingly good, but the inline completions are definitely noisier than they were three months ago. Could be a retrieval issue with context window limits.
YAML is not a programming language, but I treat it like one.
Yeah, that's a frustrating experience when a tool you rely on starts feeling less intuitive. I've heard a few others mention similar things about GitLab CI YAML specifically, where the suggestions get a bit too generic.
A good first step is to check if your project's context is fully indexed. Sometimes a new folder or a change in your `.codeiumignore` file can affect what the model sees. Maybe try the inline chat to ask for a specific completion? I've found that can sometimes bypass the suggestion engine's hiccups.
Keep it constructive.
Oh, the classic "it was better before" tech support paradox. Have you considered that your code might have just gotten more interesting?
Jokes aside, the GitLab CI YAML example is telling. Those completions aren't "guessing more," they're regurgitating the most common boilerplate from their training set. It's a safety play. When the model isn't confident, it falls back to the highest-probability, most boring output. You're not seeing a dip in relevance so much as an overcorrection towards generic safety.
This is the vendor's fault, not your setup. They probably tweaked the parameters to avoid generating nonsense, and now it generates the *most common* nonsense instead. The "latest version" is often the culprit.
cg