I'm building my own mobile app

@alvinjonathan

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Why it worked

The video provides a clear and concise overview of the tech stack used for building a mobile app, appealing to developers and tech enthusiasts interested in app development and AI integration. The use of on-screen text and clear explanations makes it easy to follow.

Summary

The creator details the tech stack used to build their mobile app, including Swift UI for the front end, Apple's foundational models for on-device ML, FastAPI for the back end, and Supabase for data storage and authentication. They also mention using external LLMs like OpenAI and Perplexity Sonar for complex AI tasks.

Structure

1Introduction to building a mobile app and its tech stack
2Front-end development with Swift UI for iOS
3On-device machine learning with Apple's foundational models
4Back-end development with Python's FastAPI
5Data storage and authentication with Supabase
6Integration of external LLMs for complex AI tasks

Product placement

The video showcases the tech stack for building a mobile app, including Swift UI, Apple's foundational models, FastAPI, Supabase, OpenAI, and Perplexity Sonar.

Original caption

The tech stack behind my mobile app #code #app #softwareengineer #developer #techstack

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On-screen text

I'm building my own MOBILE APP and here's the tech stack that I'm using for the front end I'm using Swift UI I'm mainly focusing on iOS devices so this makes things way easier. it helps me build interfaces that look and work well with the latest iPhone features and updates I'm also trying Apple's foundational for on device machine learning this lets me run powerful AI tasks right on the user's device which means faster better privacy and no extra cloud cost since the data processing stays local for the back end I'm using Python's fast API it's great because it's super easy to develop with and scales well as the app grows I use this to handle the AI logic of my app connect to external large language models and set things up for future additional features like a RAG pipeline for data storage and authentication I'm using Supabase it's easy it's easy to set up and keeps early costs down it also it also lets me store data manage user and do smart searches all in one place finally finally on top of the foundational model I'm also using external LLMs like Open AI and Perplexity Sonar to handle bigger and more complex AI tasks

Transcript

I'm building my own mobile app and here's the text tag that I'm using. For the front end, I'm using Swift UI. I'm mainly focusing on iOS devices for now, so this makes things way easier. It helps me build interfaces that look good and work well with the latest iPhone features and updates. I'm also trying Apple's foundational models for on device machine learning. This lets me run powerful AI tasks right on the user's device, which means faster features, better privacy and no extra cloud cost since the data processing stays local. For the back end, I'm using Python first API. It's great because it's super fast, easy to develop with and scales well as the app grows. I use this to handle the AI logic of my app, connect to external large language models and set things up for future additional features like a Rec pipeline. For data storage and authentication, I'm using super base. It's easy to set up and keeps early costs down. It also lets me store data, manage user accounts and do smart searches all in one place. Finally, on top of the foundational model, I also using external LLMs like open AI and proximity sensor to handle bigger and more complex AI tasks.