applin-ios VS hamilton

> Beyond Python and React ... Implementing frontends for other platforms like mobile ...

Shameless plug: I built a mobile version of this: https://www.applin.dev

I built a thing that makes it much simpler to make apps: https://www.applin.dev

You make a web server that returns JSON defining your UI. Then you make a native iOS app by copy/pasting the provided Main.swift file and adding the URL of your server. The app uses an iOS client library, fetches the JSON page definition, and builds/updates the page with native widgets. I'm planning to eventually build Android, web, and desktop clients.

Hi HN, I'm a backend engineer who made an app and didn’t like the tools. Then I made the thing I needed: a mobile app toolkit for backend engineers. I'm calling it Applin™. :)

https://www.applin.dev/

How it works: You make an HTTP server that returns JSON objects that define page content. Then you make a mobile app that calls the server and renders the pages using native widgets. Applin is the server and client libraries that make this easy.

Server libraries: Currently there's Rails https://rubygems.org/gems/applin-rails and https://github.com/leonhard-llc/applin-rails-demo . Which languages shall I add next?

Client libraries: Currently there's iOS https://github.com/leonhard-llc/applin-ios . Which platform shall I add next?

They say, if you're not embarrassed by the quality, then you're launching too late. Applin is usable and not yet pretty and not yet comprehensive. I need customer feedback on priority and requirements.

To try it out right away, use https://apps.apple.com/us/app/applin-tester/id6464230000 and tap the rails-demo link.

The hardest part of this project was making the client update the page without losing keyboard focus and scrolling to the top. To do that, the code must pick the correct existing widgets for each new version of the widget tree. The current (working) version performs five passes over the widget tree: first picking focused widgets and their ancestors, then focus-able widgets, then other stateful widgets, then widgets with matching attributes (label, URL, etc.), and finally former siblings of the correct type. Then it creates any new widgets. Now that it has widgets for the new tree, the code updates the widget tree without removing any sub-widget that will be added again. This prevents losing keyboard focus and prevents resetting scroll positions. Here's the code:

https://github.com/leonhard-llc/applin-ios/blob/main/Sources/ApplinIos/page/widget_cache.swift

Please try out Applin, use it at your company (buy a license), and let me know what features to build first! Post a comment here, add a GitHub issue, or email me at [email protected] .

To get updates, join https://groups.google.com/g/applin-announce .

Thanks for reading! :) --Michael

Note that this uses simple OpenAI calls — you can replace this with Langchain, LlamaIndex, Hamilton (or something else) if you prefer more abstraction, and delegate to whatever LLM you like to use. And, you should probably use something a little more concrete (E.G. instructor) to guarantee output shape.

In this post, we’ll show how your team can turn any utility function(s) into reusable IPython Jupyter magics for a better notebook experience. As an example, we’ll use Hamilton, my open source library, to motivate the creation of a magic that facilitates better development ergonomics for using it. You needn’t know what Hamilton is to understand this post.

We built an app with it -- https://blog.dagworks.io/p/building-a-lightweight-experiment. You can see the code here https://github.com/DAGWorks-Inc/hamilton/blob/main/hamilton/....

Usually we've been prototyping with streamlit, but found that at times to be clunky. FastUI still has rough edges, but we made it work for our lightweight app.

This library is old news? Is there anything new that they've added that's noteworthy to take it for another spin?

[disclaimer I'm a maintainer of Hamilton] Otherwise FYI Prophet gels well with https://github.com/DAGWorks-Inc/hamilton for setting up your features and dataset for fitting & prediction[/disclaimer].

I had been hearing these pains from Langchain users for quite a while. Suffice to say I think:

1. too many layers of OO abstractions are a liability in production contexts. I'm biased, but a more functional approach is a better way to model what's going on. It's easier to test, wrap a function with concerns, and therefore reason about.

2. as fast as the field is moving, the layers of abstractions actually hurt your ability to customize without really diving into the details of the framework, or requiring you to step outside it -- in which case, why use it?

Otherwise I definitely love the small amount of code you need to write to get an LLM application up with Langchain. However you read code more often than you write it, in which case this brevity is a trade-off. Would you prefer to reduce your time debugging a production outage? or building the application? There's no right answer, other than "it depends".

To that end - we've come up with a post showing how one might use Hamilton (https://github.com/dagWorks-Inc/hamilton) to easily create a workflow to ingest data into a vector database that I think has a great production story. https://open.substack.com/pub/dagworks/p/building-a-maintain...

Note: Hamilton can cover your MLOps as well as LLMOps needs; you'll invariably be connecting LLM applications with traditional data/ML pipelines because LLMs don't solve everything -- but that's a post for another day.

This is me. I drive an open source library Hamilton that people doing time-series/ML work love to use. I'm building a paid product around it at DAGWorks, and I'm after feedback on our current version. Can I entice anyone to:

From a nuts and bolts perspective, I've been thinking of building some reactivity on top of https://github.com/dagworks-inc/hamilton (author here) that could get at this. (If you have a use case that could be documented, I'd appreciate it.)