Hi all! I've opened an RFC on the main repo to publish a testing module - Genkit has a bunch of internal testing helpers at the moment, but not a unified way for app developers to mock models etc when they want to test their genkit flows (without hitting an API)
Let me know what you think:
Hey ! 👋
I’ve been diving deep into the new experimental flutter/genui package. The architecture and the DataModel approach for AI-driven UIs are fantastic, but I noticed a major friction point regarding Developer Experience (DX).
The Problem: Manually writing JSON schemas (using json_schema_builder), casting properties, and maintaining the CatalogItem registry by hand is tedious and error-prone. If you add a parameter to your Flutter widget, you have to remember to update the schema manually. It breaks the DRY principle.
The Solution: I built a companion codegen package to fully automate this: GenUI (Annotations & Builder).
Instead of writing the catalog manually, you just decorate your standard Flutter widget:
@generativeUI
class UserProfile extends StatelessWidget {
final String name;
final int age;
// ...
}
At compile time, the package uses AST to analyze your constructors and automatically generates the exact CatalogItem and json_schema_builder structures that the official flutter/genui package expects. Zero boilerplate, fully type-safe.
I’d love for you to check it out and hear your feedback!
Live Demo (play with it in your browser):
GitHub Repo:
Let me know what you think! 🚀
I’ve been thinking a lot about the best way to deploy GenAI backends with Genkit.
The idea I kept coming back to was simple: what if deploying AI tools, prompts, and flows could feel as native to Kubernetes as deploying any other backend?
So I started building Genkit Operator 🚀
With it, you can describe your Genkit app using Kubernetes resources like Prompt, Model, PluginConfig, and Flow, and the operator takes care of exposing them as HTTP endpoints.
Apply YAML. Get a production-ready Genkit endpoint.
It supports multiple providers like OpenAI, Anthropic, Google AI, Vertex AI, AWS Bedrock, and Ollama, and it is designed to work nicely with GitOps tools like Argo CD and Flux.
Still early, but I’m excited about the direction: making GenAI backends more declarative, portable, and Kubernetes-native.
Check it out here:
