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Series
AI for Mobile Developers: Learning Local LLMs is a public learning journey documenting how a React Native developer explores practical AI integration for mobile apps.
This series focuses on understanding how Large Language Models work and how they can run directly on mobile devices using local inference.
Instead of deep AI theory, the goal is to learn from a developer perspective — experimenting with tools, running models locally, and eventually integrating AI features inside mobile applications.
Journey Context After 6.5 years of building react-native apps. Most of my work has been focused on building production apps, integrating APIs, optimizing performance, and shipping features. But instea
Journey Context After deciding to go "Local," I realized I couldn't just throw a 50-page PDF at a mobile LLM and expect it to work. I had to go back to the basics of how these models "read." LLMs are
Context I used to think LLMs were like databases—you ask a question, you get the stored answer. But after playing with Temperature and System Prompts, I realized they are more like improv actors. They
I used to think that "smaller model" just meant "worse model." But today I learned that there are two separate ways to make an AI fit on a phone: you can make its memory less precise (Quantization), o
I used to think that "Chatting" with an AI was like a real conversation. But as a developer, I’ve realized it’s more like a movie script that we keep rewriting and resending every time the user types
I’ve spent the last few posts learning how to talk to an AI. Now, I’m learning how to make the AI talk back in a way my code understands. If I want to build a weather app, I don't want a poem about ra
