Hey folks! Been experimenting with Codeium's free tier to see if it can tighten up our PR reviews. We enforce a pretty strict gitops flow with Argo CD and GitHub Actions, so I wanted to see if AI suggestions could help catch simple issues before human review.
I set up a test using their GitHub App in a repo. The key was adding a step to our existing `on: pull_request` workflow to post comments. Here's a basic snippet:
```yaml
- name: Analyze PR with Codeium
uses: codeium/codeium-action@v1
with:
github-token: ${{ secrets.GITHUB_TOKEN }}
```
It's surprisingly lightweight! The bot comments on new commits in the PR with suggestions. I'm curious if anyone has tuned the rules? I'd love to make it focus on security smells in our Kubernetes manifests or Terraform more than style nitpicks.
> git commit -m 'done'
git push and pray
Interesting test, particularly for a gitops pipeline. The lightweight nature is its main selling point for CI use, but that's also where I'd be cautious about hidden operational costs if you scale.
You mentioned wanting to tune it for security smells in Kubernetes manifests. That's a solid goal, but the free tier's analysis depth might not reach the level of a dedicated security scanner like Checkov or Kubescape for Terraform and Kubernetes. You'll likely get generic "consider this best practice" comments rather than catching specific, nuanced misconfigurations like overly permissive IAM bindings or missing network policies. The risk is a false sense of security.
Also, while the action itself doesn't incur direct charges, remember that every AI-suggested change it triggers leads to another commit, another Argo CD sync, and another cloud resource cycle. If those suggestions aren't high-signal, you're paying for the compute and API calls of iterating on noise. I'd start with a very narrow path filter in your workflow to limit it to, say, only `.tf` and `.yaml` files in specific directories, and measure the signal-to-noise ratio of its comments before rolling it out broadly.
Always check the data transfer costs.
Great point about the hidden costs, especially that chain reaction of commits and syncs. It reminds me of when we added a linter that was a bit too eager, and suddenly our staging cluster was constantly twitching 😅
I think you're right that pairing it with a dedicated scanner like Checkov for the heavy lifting makes sense. Maybe use Codeium more for those "wait, is this variable even used?" kinds of questions in Terraform, and let the specialized tool flag the critical security stuff.
Did you find a good way to filter those low-signal comments, or is it mostly trial and error with the path patterns?
Always testing.