Hey everyone, just spent the morning putting the newly announced multimodal features through their paces. I was really hoping for a leap forward, especially for automating some of our image-to-config workflows, but my initial impression is... underwhelming? It feels more like a checkbox feature than a truly integrated, powerful tool.
I tested the "describe and generate" pipeline they're showcasing. The idea is you feed it an image of a UI or a diagram, and it can both describe it and generate code or configs. In practice, for anything beyond a trivial example, the output was too generic to be useful in a real platform-engineering context.
Here's a quick test I ran with a screenshot of a simple Argo CD ApplicationSet manifest (YAML) displayed on a monitor:
* **Their output:** A basic description like "A code editor showing a YAML file with keywords like 'apiVersion' and 'kind'." It then generated a *very* generic Kubernetes pod spec, not the ApplicationSet I was hoping for.
* **What I needed:** Something that could parse the actual YAML structure from the image and reproduce it, or better yet, suggest optimizations (e.g., "This could be refactored into a Helm chart with parameters for `replicaCount`").
The vision is fantastic – imagine pointing it at a whiteboard sketch of a service mesh and getting back a working Istio `VirtualService` and `DestinationRule` manifest! But the current execution lacks the precision we need for DevOps and GitOps. It's great for social media content maybe, but not for our use-cases.
Has anyone else tried integrating these features into a CI/CD pipeline or an internal developer platform tool? I'm curious if you've found a clever way to constrain the prompts or pre-process images to get more deterministic, useful outputs. The pricing for the high-res multimodal calls is also pretty steep for what's essentially a cool demo right now.
Maybe I'm missing a trick here. My test workflow looked something like this, but the results weren't promising enough to automate:
```python
# Pseudo-code of my test integration
base64_image = encode_screenshot("argo-appset-ui.png")
prompt = """
Describe this YAML configuration in detail, then generate an improved, production-ready version using GitOps best practices.
Focus on the ApplicationSet generator and template sections.
"""
response = playgroundai.multimodal_completion(
image=base64_image,
prompt=prompt,
model="reality-2025" # or whatever the new model is
)
# The response was descriptive, not operational.
```
Until it can reliably understand and output complex, domain-specific configs (Kubernetes, Terraform, Ansible, pipeline definitions), I'll probably stick with more traditional automation scripts. The potential is huge, but we're not there yet.
bw
Automate all the things.