Top Builders

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

GitHub

GitHub is a cloud-based developer platform built on Git, launched publicly in April 2008 by Tom Preston-Werner, Chris Wanstrath, P.J. Hyett, and Scott Chacon. Acquired by Microsoft in 2018 for $7.5 billion, it has grown into the central hub of open-source software and professional software development, hosting over 100 million developers worldwide. Beyond code hosting, GitHub now offers a full suite of tools spanning CI/CD automation, security analysis, cloud development environments, and AI-powered coding assistance through GitHub Copilot.

General
CompanyGitHub, Inc.
FoundedApril 2008 by Tom Preston-Werner, Chris Wanstrath, P.J. Hyett, Scott Chacon
Acquired byMicrosoft (October 2018, $7.5B)
HeadquartersSan Francisco, California, USA
Websitegithub.com
Documentationdocs.github.com
GitHubgithub.com/github
TypeDeveloper Platform

Core Products

GitHub Repositories and Code Hosting

The foundation of GitHub: Git-based version control with pull requests, code review workflows, branch protections, and merge strategies. Supports public, private, and internal repositories at any scale.

GitHub Copilot

An AI-powered coding assistant providing inline completions, chat, autonomous agent mode, and cloud-based coding agents. Available across VS Code, JetBrains, Xcode, Eclipse, Neovim, and GitHub.com itself.

GitHub Actions

A CI/CD and workflow automation platform built into every repository. Write YAML-defined workflows triggered by events (push, PR, schedule) and run them on GitHub-hosted or self-hosted runners.

GitHub Advanced Security

Security tools including secret scanning, code scanning (CodeQL static analysis), and Dependabot for dependency vulnerability management. Available on GitHub Enterprise Cloud and as add-ons for Teams.

GitHub Codespaces

Cloud-hosted development environments that spin up a full VS Code workspace in seconds, pre-configured from a devcontainer.json in the repository.

GitHub Spark

A natural-language app builder (public preview) that generates and deploys full-stack web applications from a prompt, integrated with Copilot.

Developer Resources

GitHub's developer ecosystem spans official SDKs, REST and GraphQL APIs, GitHub Apps, OAuth Apps, and Actions marketplace extensions.

Key Features

Integrated AI with Copilot GitHub Copilot is embedded natively across GitHub.com, the CLI, and major IDEs. It supports multi-model selection (OpenAI, Anthropic Claude, Google Gemini), autonomous agent mode, and MCP server integrations.

Actions-native CI/CD GitHub Actions offers thousands of pre-built actions in the Marketplace, matrix builds, reusable workflows, and first-class integration with every GitHub event.

Security-first by default Dependabot monitors dependencies for CVEs and auto-opens fix PRs. Secret scanning alerts on accidentally committed credentials. CodeQL catches security vulnerabilities before they ship.

Use Cases

Open-Source Collaboration GitHub hosts the majority of the world's public open-source projects, providing issue tracking, discussions, wikis, GitHub Pages, and Sponsors for maintainer funding.

Enterprise Software Delivery GitHub Enterprise Cloud adds SSO, audit logs, compliance controls, IP allowlists, and dedicated support, making it the platform of choice for regulated industries and large engineering organizations.

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Github AI Technologies Hackathon projects

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

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.

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.

Project Hermes

Project Hermes

Hermes is an Android accessibility and translation assistant designed for real-time communication across text, camera, and voice workflows. The app is built in Kotlin with Android native UI components, CameraX for live camera preview, ML Kit Text Recognition for on-device OCR, Android SpeechRecognizer for fast speech-to-text, and ExecuTorch/QNN integration for experimenting with local AI inference using whisper and Qwen2.5-0.5B and Qwen3-7B and on Qualcomm-powered devices. Its camera mode lets a user point the phone at signs, notes, labels, forms, menus, or other printed text, recognize the text on-device, identify the source language, and translate it into English. Its voice mode supports quick speech transcription so spoken input can be converted into readable text during live interaction. The project is especially useful in real-world situations such as travel, hospitals, classrooms, public offices, accessibility support, and multilingual conversations where users need quick understanding without switching between multiple apps. Hermes also includes groundwork for local model execution using Whisper-style ASR assets, Qwen translation models, Android native libraries, and Qualcomm acceleration paths, making it a practical prototype for privacy-aware, low-latency mobile AI translation.

Beacon

Beacon

When the river crests and the towers go dark, a hundred people end up stranded in a school gym with no signal and no way to call for help. A volunteer nurse faces a growing line of the sick and injured with no one to consult. A teacher manages sixty frightened kids alone. A family doesn't know if their water is safe to drink. Every one of them is holding a phone with a powerful on-device NPU, but cloud AI dies the instant the network does, and no single phone has the memory or compute to run a frontier-grade LLM by itself. Beacon is built around this constraint from the start: the model is pre-sharded before disaster strikes, not after. Users opt in ahead of time, downloading a layer-wise slice of a large language model's weights onto their device, a contiguous block of transformer layers sized to that phone's available memory and NPU class. These shards sit dormant on the device, costing nothing until they're needed. When the network goes down, phones nearby connect over a peer-to-peer hotspot network: one phone hosts, others join directly, with no router or internet infrastructure required. Beacon assembles an inference cluster from whichever pre-loaded layer shards happen to be present in the room, sequencing them in the correct layer order for a forward pass. The hotspot link only needs to negotiate which layers are available, route activations between phones in sequence, and reroute around a phone that drops out or runs out of battery. The heavy lifting, distribution, was done in advance, when everyone still had a connection. The result is a cluster that can assemble in seconds during an emergency, because the only real-time job is discovery and coordination, not download. The nurse gets triage guidance. The teacher gets crisis-management support. The family gets a real answer about their water. The help didn't arrive; it was already pre-positioned in their pockets, just waiting to be switched on.

SnapOn: On-Device Context-Aware Multimodal AI

SnapOn: On-Device Context-Aware Multimodal AI

SnapOn is an Android-based, offline-first multimodal AI assistant that understands what the user says and what the user sees. By combining speech, vision, and on-device reasoning, SnapOn provides fast, privacy-preserving assistance without any cloud dependency. Rather than a general-purpose chatbot, SnapOn is designed for real-world situations, identifying people and objects, summarizing documents, recognizing products and labels, and answering spoken questions about the current scene. The interaction is natural and hands-free. Hold the mic button, speak your question or say "remember this," and SnapOn captures the best camera frame, transcribes your voice using Whisper, and generates a grounded answer using SmolVLM-500M-Instruct running on the Snapdragon Hexagon NPU via ExecuTorch. What makes SnapOn unique is its personal memory layer. Say "remember this is my medication Metformin" and SnapOn saves a visual fingerprint using CLIP embeddings alongside your exact words. Next time you point the camera at the same object or person, SnapOn recognizes it passively and surfaces your saved context automatically, no button press needed. Use cases include identifying people and objects in view, summarizing documents and text in the scene, recognizing products, signs, and labels, answering spoken questions, and saving personal context for future reference. The stack includes SmolVLM-500M-Instruct, OpenAI CLIP ViT-B/32, Whisper-tiny, FAISS, SQLite, CameraX, AudioRecord, and Android TTS. On-device compilation targets SM8750 via ExecuTorch and Qualcomm QNN backend. Built for the ExecuTorch Hackathon with a strong emphasis on NPU utilization, real-world usability, and complete privacy.

Electric Safe

Electric Safe

Electric Safe is a fully on-device Android application that guides industrial electricians through the Lockout/Tagout (LOTO) safety procedure using a Vision-Language Model (VLM) running on the Qualcomm Hexagon NPU via ExecuTorch and QNN. How it works — 5-screen state machine: Start — Dark industrial UI with a single "Start Session" button and a model selector (SmolVLM 500M / InternVL3 1B). Add Documents — Import equipment manuals via the SAF PDF picker or use the bundled PowerFlex 753 VFD manual. PDFs are processed on-device using PDFBox with regex-based fault code extraction. Live AI Session — Push-to-talk speech input (offline, EXTRA_PREFER_OFFLINE) and on-device TTS. Upload a photo of the VFD fault display; the VLM reads the fault code (e.g. F071 OC1) and matches it to the manual's meaning. A live NPU: X ms latency readout shows real inference time. Guided LOTO — Four camera-gated verification steps: Breaker B-201 OFF, Breaker B-205 OFF, Lock & Tag applied, MCC Cabinet open. Each step requires the VLM to verify the correct breaker identity and state (OFF/ON) with a minimum 0.70 confidence threshold. Identity mismatches trigger a red "WORK BLOCKED" card with expected vs detected values. Permit — Generates a timestamped LOTO evidence PDF on-device using android.graphics.pdf.PdfDocument, embeds captured photos, and allows sharing via FileProvider. Architecture: Single-activity MVVM with state-machine navigation (no NavController). The CaptureDetectionSource interface abstracts detection — mock mode (clickable demo) and real VLM inference drive the exact same code path. Model files are side-loaded via adb push to external storage (industrial MDM pattern), falling back to bundled assets. Privacy: No INTERNET permission declared. Camera capture, VLM inference, PDF generation, speech recognition, and TTS all run locally on the Snapdragon Galaxy S25 Ultra. Nothing leaves the device.