Kiro

Top Builders

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

Kiro

Kiro is an AWS-powered agentic coding service designed to revolutionize software development through "spec-driven development." It leverages artificial intelligence to interpret natural language prompts and automatically generate code and tests, significantly accelerating the development lifecycle. Kiro aims to reduce manual coding efforts and improve code quality by ensuring that applications adhere closely to their specifications.

General
Author	AWS
Release Date	2025
Website	https://aws.amazon.com/
Documentation	https://aws.amazon.com/documentation-overview/kiro/
Technology Type	Agentic IDE

Key Features

Spec-Driven Development: Translates high-level natural language specifications into functional code and comprehensive tests.
AI-Powered Code Generation: Utilizes advanced AI models to write code automatically, reducing development time.
Automated Test Creation: Generates relevant test cases alongside code, ensuring immediate validation and higher quality.
AWS Integration: Seamlessly integrates with the broader AWS ecosystem, leveraging cloud infrastructure for scalable development.
Agentic Workflow: Employs AI agents to manage and execute development tasks, from planning to implementation.

Start Building with Kiro

Kiro offers an innovative approach to software development, allowing teams to rapidly build and test applications by focusing on specifications rather than intricate coding details. As an AWS-powered service, it provides the scalability and reliability expected from a leading cloud provider. Developers interested in leveraging AI for accelerated and more reliable coding should explore Kiro's capabilities.

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AWS kiro AI technology Hackathon projects

Discover innovative solutions crafted with AWS kiro AI technology, developed by our community members during our engaging hackathons.

Eido an Autonomous startup builder

Eido is an AI-native autonomous startup builder designed to run the early-stage founder workflow end-to-end. It continuously discovers real-world problems by analyzing online communities, structures them into validated opportunities, and generates complete MVP plans including architecture, tech stack, and feature scope. Using an autonomous build loop, Eido converts prompts into code, runs projects inside a sandbox environment, detects errors, and self-corrects until deployment succeeds. Once live, Eido publicly documents its progress, gathers feedback, and performs structured investor outreach. Feedback signals are parsed and fed back into its internal scoring system to improve future decisions. Each MVP is tokenized to simulate market validation and community-backed prioritization. Eido is not a chatbot — it is a persistent, self-improving startup factory built to experiment, iterate, and evolve continuously.

GoferAI - Semantic VLA RAG Layer

Gofer AI is a human-to-robot learning platform that transforms everyday demonstration videos into robot-executable intelligence. Instead of manually programming robotic behavior through conventional reinforcement learning, users can record a task using a phone or GoPro. Our system extracts keyframes using OpenCV, identifies task phases, objects, and human motion patterns using multimodal Gemini models, and converts demonstrations into structured semantic memory through a Video RAG architecture. These demonstrations are embedded, tagged, and stored for retrieval, allowing the system to reason over prior tasks and reuse knowledge. The extracted trajectories are converted into canonical action representations, then replayed and augmented in simulation using Isaac Lab and Real2Render2Real for scalable data generation. This pipeline enables behavior cloning, demo-initialized reinforcement learning, and diffusion-based policy training. The result is a robot-ready policy capable of sim-to-real transfer. Gofer AI bridges the gap between human intent and autonomous execution—turning video into intelligence.

Rosey Cleaning Service

AUTONOME - Autonomous Venue Cleaning Service English/Spanish - Chat with our AI agent to schedule robot cleaning for your events. HOW IT WORKS: 1. SCAN THE ROOM Ask our AI agent to perform a visual scan of your venue. Using Google Gemini-powered computer vision, our system analyzes every corner, identifying trash, spills, scattered furniture, and areas requiring attention. The scan creates a detailed real-time map. 2. TAKE INVENTORY Request a comprehensive assessment of cleanup requirements. Our agent catalogs all identified issues including trash volume, spill locations and severity, furniture disorganization, and specialized needs. You receive a detailed breakdown of what needs attention. 3. SEND EMAIL REPORT Ask the agent to email you a complete inventory report. This document includes summaries, itemized tasks, time requirements, cost projections, and photographic evidence. Perfect for sharing with your team or getting approval. 4. SCHEDULE CLEANING SERVICE Once satisfied, simply ask to schedule the service. Our agent coordinates deployment of our autonomous robot fleet, selecting optimal timing, determining robot quantities needed, and confirming your appointment. WHAT TO EXPECT: Our specialized robot cleaners arrive at your scheduled time, operating autonomously to restore your venue to pristine condition. Trash collection robots efficiently gather and dispose waste, floor scrubbing units tackle spills, and organization bots reposition furniture. All robots operate safely with collision avoidance and can work overnight without supervision. If robots encounter tasks beyond their capabilities, the system automatically escalates to our human cleaning partner network via Cleanover API, ensuring complete coverage. Experience facility management where technology handles the work and you enjoy a clean, ready-to-use space.

RoboLens

RoboLens is a vision-guided coordination system designed to enable intelligent, system-level autonomy across fleets of autonomous robots. Traditional robotic systems rely heavily on onboard sensors, which limits awareness to local surroundings and often results in isolated decision-making. RoboLens addresses this limitation by introducing an overhead multi-camera infrastructure that functions as a centralized perception layer. These cameras continuously monitor the environment, detecting spills, fallen objects, human presence, congestion, and restricted zone violations in real time. Detections from multiple cameras are fused into a unified world model, providing a consistent and system-wide representation of the environment. An AI-driven reasoning engine evaluates event severity, spatial relationships, safety constraints, and robot availability to dynamically prioritize tasks. A centralized scheduler then assigns or reassigns robots in real time, ensuring efficient task distribution, conflict resolution, and congestion mitigation. By decoupling perception from individual robots and centralizing coordination, RoboLens transforms isolated robotic units into a cohesive, adaptive fleet. Its modular and hardware-agnostic architecture is designed to scale from simulation to real-world warehouse, manufacturing, hospital, and smart facility deployments.

Adapt Navigation

AdaptNav tackles the $27B warehouse automation challenge: creating autonomous robots that safely navigate dynamic environments without constant human supervision. The Problem: Current warehouse robots are either too rigid (predetermined paths) or black-box (unexplainable AI), making them unsuitable for real-world deployment where safety is critical. They fail in chaotic warehouses with moving workers, forklifts, and changing obstacles. Our Solution: AdaptNav introduces the world's first hybrid AI navigation system, combining classical pathfinding reliability with reinforcement learning adaptability: • Global Brain: A* algorithm handles optimal warehouse route planning • Local Intelligence: PPO reinforcement learning manages real-time dynamic obstacle avoidance • Safety Guardian: Multi-layered safety system with hard constraints overriding AI when necessary Key Innovation: Unlike existing solutions, AdaptNav provides explainable AI - operators see exactly why robots make each decision. This transparency plus safety-first design enables production deployment where human lives and equipment are at stake. Proven Results: 80%+ success rate in complex scenarios, <100ms obstacle response time, zero collisions in 1000+ test episodes, 10Hz real-time performance on standard hardware. Technical Excellence: Built on ROS 2, with comprehensive property-based testing, dual-simulation support (Isaac Sim/MuJoCo), cross-platform compatibility, and a modular architecture for easy customization. Impact: AdaptNav bridges research and production with a complete system ready for immediate deployment. Professional implementation with comprehensive documentation represents the future where AI meets reliability and autonomous navigation finally works in the real world.

SANCTUMSIM

SANCTUM-SIM is a simulation-first robotics platform that demonstrates autonomous infrastructure management under real-world constraints. In a simulated geothermal and logistics facility, mobile inspection and maintenance robots operate in dynamic heat zones, energy limits, and failure-prone environments. Unlike scripted demos, each agent continuously evaluates tradeoffs between energy use, time, and operational risk using cost functions and a planner-based decision engine. Every action generates a structured, explainable rationale—showing why a robot rerouted, delayed, escalated, or conserved power. The system introduces sensor noise, randomized failures, and environmental volatility to demonstrate robustness, recovery, and graceful degradation. A browser dashboard visualizes agent states, decisions, and performance metrics in real time. SANCTUM-SIM enables energy operators and infrastructure teams to simulate, validate, and build trust in autonomous systems before real-world deployment.