Skip to main content

Vibe Coding with GitHub Spark: From idea to app in minutes

There are multiple platforms available to support you in your vibe coding experience. I mostly used bolt.new but also experimented with lovable.dev and v0.app. But did you know that Github has there own vibe coding platform?

Time to give Github Spark a try…

Enter GitHub Spark

GitHub Spark, it's an AI development platform that converts plain English descriptions into complete, deployable full-stack applications. And I do mean complete—frontend, backend, authentication, database, hosting, the works.

Its main focus is simplicity, allowing you to create simple applications without having to worry about all the technical details.

Let’s dig into some of the features it has to offer.

Pure natural language development

The starting point is the same as with any of the other available platforms. You can enter a prompt that describes what you want.

"I want to create mobile friendly 7 minutes workout app.."

That's it. No setup, no configuration, no choosing between frameworks. Spark handles everything.

Spark starts its magic and spits out a working application after some minutes.

Specification driven

A nice functionality of GitHub Spark is that it creates a PRD markdown file with the specifications of your application. If you later ask to add some extra features, the markdown file is updated as well.

Real-Time Preview

As Spark generates your application, you see it come to life in real-time. The live preview updates instantly, showing you exactly what's being built. This is not different compared to any of the other platforms but still a must have.

 

Three Ways to Work

Spark adapts to your skill level:

  • Pure vibe coding: Use natural language prompts exclusively
  • Visual editing: Adjust UI elements with visual controls
  • Direct coding: Dive into the code editor with Copilot assistance

It is very easy to switch between the 3 modes allowing you to choose what is most appropriate.

Some remarks I got when trying Spark:

  • It only seems to know React and Typescript (which doesn’t have to be a problem).
  • The model used seems a little outdated as it got stuck on some rather obvious changes I asked it to make where most coding models don’t struggle with.
    • E.g. Add support for PWA to this application
  • There seems a lot of reasoning involved when using the model. But I noticed that it got stuck a few times. As the train-of-thought is shared during the process, I was able to interrupt and bring the AI back on the right track in some situations.

Other features

Spark offers some other features worth exploring but I’ll leave that for a follow up post tomorrow.

More information

GitHub Spark · Dream it. See it. Ship it. 

Building and deploying AI-powered apps with GitHub Spark - GitHub Docs

Labels:

Popular posts from this blog

Azure DevOps/ GitHub emoji

I’m really bad at remembering emoji’s. So here is cheat sheet with all emoji’s that can be used in tools that support the github emoji markdown markup: All credits go to rcaviers who created this list.
Read more

.NET 8–Keyed/Named Services

A feature that a lot of IoC container libraries support but that was missing in the default DI container provided by Microsoft is the support for Keyed or Named Services. This feature allows you to register the same type multiple times using different names, allowing you to resolve a specific instance based on the circumstances. Although there is some controversy if supporting this feature is a good idea or not, it certainly can be handy. To support this feature a new interface IKeyedServiceProvider got introduced in .NET 8 providing 2 new methods on our ServiceProvider instance: object? GetKeyedService(Type serviceType, object? serviceKey); object GetRequiredKeyedService(Type serviceType, object? serviceKey); To use it, we need to register our service using one of the new extension methods: Resolving the service can be done either through the FromKeyedServices attribute: or by injecting the IKeyedServiceProvider interface and calling the GetRequiredKeyedServic...
Read more

Kubernetes–Limit your environmental impact

Reducing the carbon footprint and CO2 emission of our (cloud) workloads, is a responsibility of all of us. If you are running a Kubernetes cluster, have a look at Kube-Green . kube-green is a simple Kubernetes operator that automatically shuts down (some of) your pods when you don't need them. A single pod produces about 11 Kg CO2eq per year( here the calculation). Reason enough to give it a try! Installing kube-green in your cluster The easiest way to install the operator in your cluster is through kubectl. We first need to install a cert-manager: kubectl apply -f https://github.com/cert-manager/cert-manager/releases/download/v1.14.5/cert-manager.yaml Remark: Wait a minute before you continue as it can take some time before the cert-manager is up & running inside your cluster. Now we can install the kube-green operator: kubectl apply -f https://github.com/kube-green/kube-green/releases/latest/download/kube-green.yaml Now in the namespace where we want t...
Read more