Md Mahmudul Islam
Mahmudul is an AI and Mobile Engineer at Riverborn, building the on-device AI systems and mobile experiences that power our production apps. He works at the intersection of LLM inference, mobile runtimes, and real-world device constraints, turning research-grade models into software that actually runs on a phone.
About Mahmudul
His writing on the Riverborn blog covers the decisions behind that work: how to choose an on-device AI model for mobile and why bigger is not automatically better on a phone, why on-device AI can beat cloud AI on privacy and cost, and why an offline voice stack should give speech-to-text a single engine but give text-to-speech a real trade-off.
He also documented what the team learned from building an offline AI app of its own before building similar systems for clients, and how that experience changes the way Riverborn scopes comparable projects.
Articles by Mahmudul
4 articles
What We Learned Building an Offline AI App
Before we build an AI system for a client, we usually build it for ourselves first. Here's what broke, what we learned, and why it changes how we scope similar work.
Why On-Device AI Beats Cloud AI on Privacy and Cost
Cloud AI processes every message on someone else's server and meters every request. On-device inference breaks both problems. Here's the trade-off, honestly.
Offline Voice AI: Why More Engine Choices Aren't Always Better
Not every part of an offline voice stack needs a choice. Here's why speech-to-text got one engine, and why text-to-speech got a real trade-off instead.
How to Choose an On-Device AI Model for Mobile
There's no single best on-device model. Here's the real lineup we ship, which ones we actually recommend, and why bigger isn't automatically better on a phone.