DeepMenu VS Pulumi

I've been working on the menuing side [1] based on crossing Fitt's Law with Huffman trees. But, don't know the constraints for ALS.

Hopefully, whomever takes this on doesn't take the standard Accessibility approach, which is adding an extra layer of complexity on an existing UI.

A good friend, Gordon Fuller, found out he was going blind. So, he co-founded one of the first VR startups in the 90's. Why? For wayfinding.

What we came up with a concept of Universal design. Start over from first principles. Seeing Gordon use an Accessible UI is painful to watch, it takes three times as many steps to navigate and confirm. So, what is the factor? 0.3 X?

Imagine if we could refactor all apps with a LLM, and then couple it with an auto compete menu. Within that menu is personal history of all your past transversals.

What would be the result? A 10X? Would my sister in a wheelchair be able to use it? Would love to find out!

[1] https://github.com/musesum/DeepMenu

Am experimenting with a new menuing idiom with SwiftUI [1]. It is both delightful and infuriating. Am delighted by autolayout. But, way too much semantics for observers. To simplify, I intentionally avoided structs, for view models, and resorted to classes.

I hope to create a package and use it for metal based visual music synthesizer. But, those complaints about playing nice with UIKit? Am rather worried.

[1] https://github.com/musesum/DeepMenu

Pulumi lets you define cloud infrastructure using real programming languages — TypeScript, Python, Go, C#, Java — instead of YAML or HCL. You get loops, conditionals, functions, type checking, and IDE autocomplete for your infrastructure.

This is different from having Cursor write Terraform or Pulumi. Those tools let you define infrastructure explicitly, which means Cursor would need to generate proper Terraform configurations, understand AWS resource dependencies, and manage state correctly. AI-generated infrastructure code risks hallucinations that create security vulnerabilities, resource misconfigurations, and version conflicts - meaning you need to carefully review thousands of lines of generated IaC before deploying.

Pulumi put together a full breakdown, but here is the technical signal behind the announcements.

Of course, deploying Lambda Functions and Lambda Layers depend a lot on the IAC tool you use. We use SST, which is Pulumi under the hood.

Website: https://www.pulumi.com/

Tools like Terraform, Pulumi, and CloudFormation.

The DevOps way had already seen adoption of Infrastructure as Code, and tools like Terraform, Chef, Pulumi, etc. enabled us to think of our cloud resources as configurations and code. Cloud Engineering needed to evolve along similar lines, and the elephant in the room was - a need for standardization, enter GitOps!

Pulumi infra-as-code but with real programming languages

Terraform isn't without issue, though. Some feel that the descriptive configuration language is limiting. Pulumi offers to describe the infrastructure in a couple of existing programming languages, e.g., Python, JavaScript, and Kotlin. Instead of repeating ten configuration lines with only a single parameter changed, you can write functions and loops.

Thank you to Pulumi for sponsoring this challenge and giving the community a chance to build and deploy something awesome!