Top Builders

Explore the top contributors showcasing the highest number of app submissions within our community.

Qualcomm

Qualcomm was founded in July 1985 in San Diego by seven engineers, including Irwin Jacobs and Andrew Viterbi, as a wireless communications research firm. Over four decades it has become the defining company in mobile silicon, designing the Snapdragon family of system-on-chips that sit inside the majority of the world's Android smartphones, Copilot+ PCs, XR headsets, and connected automotive systems. Qualcomm operates as a fabless semiconductor company, focusing on chip design and its vast patent portfolio covering CDMA, 3G, 4G LTE, and 5G NR technologies.

General
CompanyQualcomm Incorporated
FoundedJuly 1, 1985 by Irwin Jacobs, Andrew Viterbi, and five colleagues
HeadquartersSan Diego, California, USA
Websitequalcomm.com
DocumentationDeveloper Portal
GitHubgithub.com/qualcomm
TypeSemiconductor and Wireless Technology

Core Products

Snapdragon SoC Platform

Snapdragon is Qualcomm's flagship system-on-chip brand integrating CPU, GPU, NPU (Hexagon), modem, and connectivity in a single package. Snapdragon chips power mobile phones, Windows laptops (Copilot+ PC), AR/VR headsets, automotive cockpits, and IoT devices.

Dragonwing

Qualcomm's commercial and industrial product line, built on Snapdragon silicon, targeting industrial IoT, robotics, and smart edge deployments.

Qualcomm AI Hub

A developer platform at aihub.qualcomm.com offering 175+ pre-optimized AI models, a cloud-based profiling workbench for testing across 50+ device types, and sample applications. Developers can optimize, benchmark, and deploy models on Snapdragon hardware without owning devices.

Connectivity and RF

Qualcomm designs Wi-Fi, Bluetooth, and 5G modem products used across consumer electronics, automotive, and infrastructure.

Developer Resources

Qualcomm provides a full developer toolkit for building AI-accelerated applications on Snapdragon hardware, from low-level NPU SDKs to cloud-hosted device profiling.

Key Features

Heterogeneous AI Engine Qualcomm's AI Engine combines the Hexagon NPU, Adreno GPU, and Oryon/Kryo CPU into a coordinated compute pipeline. This lets applications shift workloads to the most efficient processor at runtime, achieving high performance at low power.

On-Device AI at Scale Qualcomm AI Engine ships in over 4.3 billion devices. The Hexagon NPU supports FP32, FP16, INT16, and INT8 precision, enabling deployment of large language models and image generation on mobile hardware without cloud dependency.

Broad SDK Ecosystem Developer tools include the AI Engine Direct SDK (QNN), the AI Model Efficiency Toolkit (AIMET) for quantization and pruning, the Snapdragon Neural Processing Engine (SNPE), and an ONNX Runtime execution provider plugin.

Use Cases

On-Device Generative AI Run quantized LLMs (Llama, Mistral, Qwen) and image-generation models locally on Snapdragon phones and laptops. AI Hub provides ready-to-deploy .pte and optimized model artifacts for direct integration.

Mobile and Embedded Vision Object detection, depth estimation, scene segmentation, and OCR run at real-time framerates on the Hexagon NPU with Qualcomm's pre-optimized model zoo.

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Qualcomm AI Technologies Hackathon projects

Discover innovative solutions crafted with Qualcomm AI Technologies, developed by our community members during our engaging hackathons.

Toddle AI

Toddle AI

Toddle AI is an on-device Android prototype built to help parents capture and understand toddler walking patterns more clearly and privately. A parent can record a new clip or import an existing side-view walking video, and the app evaluates whether the recording is usable by checking framing, visibility, and whether enough continuous walking steps are present. Once a clip passes the quality gate, Toddle AI runs pose estimation locally on the device using a 33-landmark body model, then applies a deterministic gait-analysis pipeline to detect steps, measure temporal walking features, and generate structured observations. The app focuses on explainable metrics rather than black-box scoring, so each result comes from a measurable part of the walking sequence instead of an unsupported diagnosis. Toddle AI also includes a local AI assistant that helps explain the structured results in parent-friendly language and answer follow-up questions without sending raw video to a server. The current prototype is designed around privacy, transparency, and better home capture guidance. It is a research prototype for educational support and tracking, not a clinical diagnostic tool.

Lantern: Model anything

Lantern: Model anything

Lantern is an AI-powered mobile application that transforms a standard smartphone into a real-time 3D scanner using entirely on-device inference. Users simply point their phone at an object and walk around it for approximately 30 seconds while Lantern estimates depth, tracks camera motion, reconstructs geometry, and generates a dimensionally accurate 3D mesh, all without sending data to the cloud. Built with ExecuTorch, Lantern demonstrates how edge AI can deliver fast, private, and offline 3D reconstruction on consumer smartphones. By eliminating the need for expensive LiDAR hardware, external computers, or cloud processing, Lantern makes high-quality 3D scanning more accessible to engineers, designers, makers, researchers, and students. The generated 3D models can be used for reverse engineering, CAD workflows, rapid prototyping, digital asset creation, manufacturing, and AR/VR applications. Lantern showcases the power of on-device AI by combining computer vision, monocular depth estimation, and real-time 3D reconstruction into a seamless mobile experience. Our goal is to make professional-quality 3D scanning available to anyone with a smartphone while preserving user privacy and enabling low-latency performance through efficient edge inference.

Memory Recall Assistant

Memory Recall Assistant

Memory Recall Assistant is a fully on-device, multimodal AI memory aid designed for people with early-to-mid stage cognitive decline. The app runs continuously in the background, periodically capturing context through the camera and microphone. When the user presses Recall while looking at someone, the app identifies the person using CavaFace face embeddings (w8a16 quantized, ~99.5% accuracy on LFW benchmark), retrieves stored conversation history transcribed by Whisper-Small-Quantized, and generates a warm, natural memory cue using Llama 3.2 1B Instruct - all within seconds and entirely offline. A key design innovation is center crop targeting: rather than identifying the largest or highest-confidence face in frame, the app identifies the face closest to the center of the camera frame - the person the user is actively talking to, not a bystander. This makes identification intentional and natural, and extends directly to Meta AI Glasses where the camera points where the user looks. All sensitive data - face embeddings, conversation transcripts, location context - is stored on-device in a local SQLite knowledge graph and never transmitted anywhere. The system functions fully in airplane mode, demonstrating that privacy-sensitive AI for vulnerable populations is not only possible on mobile hardware - it is better this way. There is no server to breach, no API to call, and no company holding your family's data.

SafeScreen AI

SafeScreen AI

SafeScreen AI is designed as a local first line of defense. It runs directly on a Snapdragon-powered Android device using ExecuTorch and analyzes visual content on-device in real time. When the system detects potentially explicit, abusive, or manipulated media, it can immediately warn, blur, redact, mask, or block the content directly on screen before the user fully engages with it. Our initial focus is on two high-impact use cases: 1. Real-time explicit and harmful visual content protection, especially for young kids, teens, and women who may be targeted by unsafe content, online abuse, harassment, impersonation, or AI-generated explicit media. Many vulnerable users may not have the technical awareness to recognize manipulated or harmful content before it affects them. SafeScreen AI acts as a private safety shield that can blur or redact harmful content in the moment, reducing exposure before harm spreads. 2. Deepfake and manipulated media detection, helping users recognize synthetic or altered images and videos before trusting, sharing, or being harmed by them. This matters because AI-generated abuse is no longer hypothetical. Recent reporting has found AI-generated explicit deepfakes spreading in schools and affecting hundreds of students globally. Research on publicly available deepfake model variants found nearly 35,000 downloadable deepfake-related models, with almost 15 million downloads since late 2022; 96% of the targeted individuals were women. Online harm is also increasingly recognized by governments and advocacy groups as a serious form of non-consensual intimate image abuse. Using ExecuTorch and Snapdragon acceleration, we aim to build a low-latency, privacy-preserving pipeline that captures visual input, runs lightweight models locally, and responds immediately with protective actions such as warning, blurring, redaction, masking, or blocking.

PrivateDoc

PrivateDoc

PrivateDoc is an offline, on-device document intelligence assistant designed for sensitive personal paperwork, running on a Samsung S25 Ultra phone powered by Qualcomm SnapDragon. It combines ML Kit OCR, ExecuTorch, MiniLM semantic retrieval, and an on-device Llama 3.2 1B LLM to scan, classify, index, and answer questions about prescriptions, contracts, bills, IDs, and other documents without requiring an Internet connection. A strict retrieval-augmented generation (RAG) pipeline ensures every response is grounded in retrieved OCR evidence, with supporting text highlighted on the original document to improve transparency and reduce hallucinations. By performing OCR, retrieval, and language generation entirely on the device, PrivateDoc preserves user privacy while delivering fast, reliable, and source-backed document understanding.

EchoWalk: On-Device Guidance for Low-Vision Users

EchoWalk: On-Device Guidance for Low-Vision Users

Imagine walking through an unfamiliar room with your eyes closed. You need to know what is ahead, what is around you, and how to reach the chair someone mentioned — without cloud latency or sending your camera feed anywhere. EchoWalk is built for that moment. On a Galaxy S25 Ultra, one shared camera pipeline feeds a central ModeManager that decides when to warn, when to describe, and when to search — all on the Snapdragon NPU via ExecuTorch and Qualcomm QNN. Safety Radar runs continuously. Depth Anything V2 and YOLOv10 fuse on the Hexagon NPU: not just what is there, but how far and whether it is a trip hazard or a wall you can trail. Spatial audio and haptics place obstacles in space; a VoiceWarningEngine speaks when it matters. A live bounding-box overlay helps sighted helpers follow along in demos. Scene Description is on demand — tap the preview, the Describe button, or long-press Volume Up. A short burst of frames runs through a Places365 classifier and pairs the room label with live YOLO directions: "You appear to be in a living room — couch on your left, TV ahead." Auto-describe announces stable scene changes hands-free. The full SmolVLM-500M stack is integrated and validated through handoff scripts; richer VLM captions are ready for the next aligned build. Find Mode is voice-first. Long-press Volume Down, say "find the bottle," and the app maps your words to everyday object labels. It scans the room, guides you turn by turn, warns about obstacles in your path, highlights the target on screen, and remembers where it last saw it so the next search starts with a hint. Accessibility is front and center: lock-screen access, screen-on at launch, spoken onboarding with a first orientation from live radar, eyes-free volume shortcuts, and double-tap to repeat your last description. No cloud. No upload. Your home never leaves your pocket.