Claude Code

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Claude Code

Claude Code is an advanced command-line interface (CLI) tool developed by Anthropic, designed to empower its AI model, Claude, with direct code interaction capabilities. This tool allows developers to leverage Claude for agentic coding tasks, including refactoring, debugging, and managing code within the terminal environment. It integrates Claude's powerful language understanding with practical development workflows, bringing AI assistance directly to the codebase.

General
Author	Anthropic
Release Date	2024
Website	https://code.claude.com/
Documentation	https://code.claude.com/docs/en/overview
Technology Type	AI Coding Assistant

Key Features

Agentic Coding: Enables Claude to perform complex coding tasks autonomously, guided by natural language instructions.
Terminal Integration: Works directly within the command line, providing a seamless experience for developers.
Code Refactoring: Assists in improving code quality, structure, and efficiency.
Debugging Support: Helps identify and resolve issues in the codebase.
Code Management: Facilitates various code-related operations, enhancing developer productivity.
Natural Language Interaction: Developers can interact with Claude using plain language prompts for coding tasks.

Start Building with Claude Code

Claude Code offers a powerful way to integrate Anthropic's Claude AI directly into your coding workflow. By providing agentic capabilities from the terminal, it streamlines refactoring, debugging, and general code management. Developers can leverage this tool to accelerate development, improve code quality, and benefit from AI assistance in real-time.

👉 Claude Code CLI Guide 👉 Claude Code Quickstart

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Anthropic Claude Code AI technology Hackathon projects

Discover innovative solutions crafted with Anthropic Claude Code AI technology, developed by our community members during our engaging hackathons.

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.

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.

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.

SixthSense: Haptic Vision for the Blind

SixthSense is a wearable that helps blind and low-vision people sense obstacles around them and find a clear path. A phone is mounted on the chest and watches the way ahead. On-device models turn what the camera sees into a simple readout: how near obstacles are in the left, center, and right zones, what objects are present, and whether the path is clear. That readout drives a vibration belt worn at the waist, which buzzes on the side of the nearest obstacle so the user can feel which way to move. The point is that knowing something is close is not enough. A basic vibrating cane buzzes whenever anything is near, so in a crowd it buzzes constantly without telling you where the gap is. SixthSense reads each zone separately and steers the user toward open space, so it stays useful in busy areas. The user can also ask what is ahead and hear a short spoken answer, or point the camera at a sign and have its text read aloud. The vision runs on the phone. YOLOv11n detects objects and tags each to a left, center, or right zone. Depth-Anything-V2 estimates how near things are, which sets how hard the belt buzzes. Qwen2.5-0.5B answers spoken questions about the scene, and ML Kit reads text on demand. The models run through ExecuTorch as compiled files on the phone, offline, on a Qualcomm Snapdragon 8 Elite, with the option to run on the Hexagon NPU. The phone sends a small directional packet over Bluetooth to an ESP32, which drives the belt motors. Cost is the main reason it exists. Smart canes and glasses run from about $850 to over $2,000, and only one in ten people who need assistive technology can get it, dropping to about five percent in lower-income countries. SixthSense uses a phone the user already has and a sub-$20 belt, with room to reach about $50 at scale, putting this within reach of people who are priced out today.

Lodestar — Offline AI Survival Copilot

GPS denial is no longer rare: Poland logged 2,732 jamming incidents in one month in early 2025, and an EU Commission President's plane lost GPS near Bulgaria and landed on paper maps. When navigation fails, everything built on top tends to fail at once including medical guidance, since most first-aid apps assume connectivity that may not exist when it matters most. Lodestar is an offline, on-device AI survival copilot built for that moment. It runs on Snapdragon hardware via ExecuTorch, requesting no INTERNET permission at all, across three capabilities: TREAT — describe an injury by voice or text and get a severity-ranked, source-cited first-aid response. Severity comes from a deterministic safety-tree engine, not the language model, so the system can't be talked into downgrading a critical call by ambiguous phrasing. The model explains and cites a TCCC/MARCH corpus but cannot override the verdict underneath. We tested negation handling ("hasn't stopped" vs. "has stopped now"), the failure mode that matters in the field — and caught and fixed a real bug here during testing. ORIENT — true north without a satellite. By day, a solar compass derives heading from the sun's position, verified against documented sunrise, sunset, and solar-noon directions. By night, on-device star plate-solving matches a photographed sky against a catalog. A status strip shows the active position source — GPS_TRUSTED, DEAD_RECKONING, SOLAR_FIX, or STAR_FIX — and flips in real time if GPS is spoofed, freezing to the last trusted fix. COMMUNICATE — medic-casualty translation plus a one-tap SOS card from the TREAT conversation. Every model-backed capability sits behind one interface, so the app was built and tested against a stub before the real models landed swapping to production is a one-line change. We're upfront about what's tested today (safety tree, solar math, spoof detection all pass automated tests) versus what's in progress (corpus coverage, runtime integration)

Sub Rosa: On-Device AI Legal Intake

Sub Rosa is an Android app that turns a lawyer's first client meeting into clear intake notes. It runs entirely on the phone and never goes online. It records the conversation and transcribes it on the device with Whisper. A small language model, Qwen3-1.7B running with ExecuTorch on the Qualcomm Hexagon NPU, then produces three things: the facts, each shown next to the exact words from the transcript that back it up; the missing items, the standard intake points the client did not mention, found by comparing the talk against a checklist; and a few neutral follow-up questions to ask. The model only reads and sorts what it is given. It never states the law or gives advice. The legal knowledge lives in checklists written by people, not in the model's weights, so a small model stays accurate with no training step. Privacy is built in. The app has no internet permission, so the phone blocks any connection, and an on-screen panel shows nothing was sent. Audio is never saved, and all client data is encrypted on the device with AES-256-GCM.