In our previous post, Mobile On-device AI: Smarter Faster Private Apps, we explored the fundamentals of running AI locally on mobile devices. Now, it’s time to get hands-on and see this technology in action!
This practical guide walks you through implementing mobile on-device AI using Google’s powerful Gemma model family, including the cutting-edge Gemma 3n. You’ll learn to deploy these models across iOS, Android, and web platforms using industry-standard frameworks.
While cloud computing drives many AI breakthroughs, a parallel revolution is happening right in our hands - running LLMs locally on mobile devices. This emerging field, known as Mobile On-device AI, enables us to build more private, faster and smarter app experiences - especially as mobile devices become increasingly powerful. As a developer passionate about AI and mobile, I am fascinated by the convergence these two worlds and the possibilities it brings.
Every day we’re seeing fantastic advancements in AI, thanks to more data and powerful computers. This may make it seem like the future of AI is all about getting even more data and bigger computers. But I believe a critical and rapidly evolving piece of the puzzle is about bringing the Intelligence of Artificial Intelligence onto the devices where the data originates (eg: our phones, cameras, and IoT devices) and doing the “smarts” using their own computing capabilities.
I stopped using Chrome a long time ago due to privacy concerns, and I’ve been switching between Firefox, Edge, and occasionally Brave. But I never spent the time to fully compare these browsers and analyze which one best suits my needs. Today, I decided to look deeply into these browsers and ended up going down a rabbit hole of researching their privacy features.
Why is this important? Every time we browse a website, we are sharing bits of our digital life with companies we don’t know.
Today I tried something fun - built a Pomodoro timer app mostly by talking to AI instead of typing code myself. And guess what - there is a term for it - vibe coding, coined by Andrej Karpathy 😎.
I have done it a few times before, but this is the first time I am using it to build a full app. I wanted to create something that was useful and worked well, so I chose the Pomodoro timer.
Today I tried Claude Code, the new agentic coding tool announced by Anthropic this morning. Unlike other agentic tools, Claude Code is a CLI tool.
Claude has been my favorite AI coding partner so far. I use it via GitHub Copilot and as standalone through its web interface. I was curious to see how it works in CLI and decided to give it a try.
In this post, I share my first impressions of using Claude Code - how I set it up, what I loved about it, what I didn’t, and how it compares to other similar tools.