Chuck’it takes first prize in Arm AI Developer Challenge

The Arm AI Developer Challenge set out to explore what’s possible when AI applications are designed to run locally and efficiently on Arm-based mobile and edge devices. In partnership with Devpost, the competition invited innovators to build AI-powered applications optimised for Arm-based platforms, including smartphones, tablets, and single-board computers such as Raspberry Pi.

Submitted projects demonstrated diverse approaches to on-device AI covering generative AI, computer vision, natural language processing, and geospatial analysis, while accounting for performance-per-watt, memory constraints, and usability.

The challenge was evaluated by Arm engineers and developer evangelists with experience across software. Projects were assessed using four core criteria: technological implementation, user experience, potential impact, and the “wow” factor. After reviewing the 142 submissions, the judges selected six projects that demonstrated strong alignment with the challenge goals.

Han Wei Tan and Varun Bhalero, who jointly created Chuck’it, believe that receiving first prize in the Arm AI Dev challenge allows them to be more confident in the product they are trying to build. They said: “The next step is learning how to optimise on-device AI to squeeze more performance out of what already exists. These future endeavours will involve Arm-based devices in some way, with many systems we use on a day-to-day basis running on Arm.”

Second prize went to DreamMeridian for GeoAI on Pi – a natural-language spatial query engine that runs on a low-power Arm-based Raspberry Pi 5, enabling humanitarian and field workers to find routes, distances, and nearby services from local map data without internet access, cloud compute, or GIS expertise.

Adam Munawar Rahman, who was a solo participant on the GeoAI on Pi project, spoke about the vital role of the Arm compute platform in powering reliable on-device AI that works without connectivity. He said: “Arm will remain central to my work: low cost, low power edge deployment is the only way to reach places where infrastructure fails. For humanitarian and emergency contexts, this changes what’s possible in the field.”

In third place was InstaMeme: A fully on-device, Arm64-optimised iOS app that uses local vision and language models to turn photos into shareable memes instantly, proving that fast, private, and creative AI generation can happen entirely on-device without cloud services.

Mark Foster, who developed the InstaMeme project, was delighted at how a tightly scoped AI model can perform on Arm-powered mobile devices. He said: “Performing AI inference on Arm-based mobile devices allows me to provide an offline experience for my app users. Local models offer several advantages over cloud-based approaches, including lower latency, reduce compute and infrastructure costs, improved privacy, and reliable offline experiences.”

Fourth placed was Jackqr: An on-device study tool that converts poorly scanned PDFs and textbook photos into clean, searchable learning materials with AI-powered simplification and flashcards, all running locally so students can study effectively without reliable connectivity.

Epictetus clinched fifth – a privacy-preserving Android chatbot leveraging AI through Arm KleidiAI, XNNPack and MediaPipe that offers practical guidance, emotional support and everyday advice to users while keeping conversations entirely on smartphone devices.

Placed sixth was Pocket Garden – an AI-powered gardening companion that runs locally on Arm-based Raspberry Pis, using real-time environmental data and a multi-agent system to give new gardeners personalised, location-aware advice that helps them improve yields and stay engaged throughout the growing season.

An Arm spokesperson said: “Seeing this developer community turn Arm-powered platforms into practical AI experiences reinforces why Arm invests in programs like this: to remove friction, share the right tools and examples, and help great projects ship.”

“Together, these projects illustrate how Arm architecture enables scalable, privacy-first, and resilient AI at the edge, helping accelerate adoption of on-device AI patterns that developers can build on and extend.

“So what’s in store for the future with Arm and on-device AI? The Arm AI Developer Challenge reinforces a clear message: AI capabilities are increasingly moving to on device, with the Arm compute platform at the centre of this broad trend. As AI continues to evolve, developers will play a critical role in shaping applications that are not only powerful, but efficient, private, and accessible.”

Across the winning submissions, several consistent themes emerged:

• Prioritising privacy and trust by default, with developers choosing fully offline workflows that keep user data on device without cloud dependencies.

• Performance-per-watt as a core product feature, with developers using techniques such as model quantisation, efficient runtimes, and right-sized models to deliver responsive user experiences on Arm-powered smartphones and Raspberry Pi systems.

• The ability to operate in real-world conditions where connectivity is unreliable or unavailable, with developers demonstrating how on-device AI can remain useful in classrooms, field operations, and other constrained environments.