AWS

Top Builders

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

AWS

Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform, offering over 200 fully featured services from data centers globally. Since its launch in 2006, AWS has provided a highly reliable, scalable, low-cost infrastructure platform in the cloud that powers hundreds of thousands of businesses in 190 countries worldwide. AWS is a leader in cloud computing, offering services that span compute, storage, databases, networking, analytics, machine learning, artificial intelligence, Internet of Things (IoT), security, and enterprise applications.

General
Company	Amazon Web Services, Inc.
Founded	2006
Website	https://aws.amazon.com/
Documentation	https://docs.aws.amazon.com/
Technology Type	Cloud Provider

Start Building with AWS Products

AWS provides a vast array of services that enable developers and businesses to build sophisticated, scalable applications in the cloud. From foundational services like compute and storage to advanced machine learning and AI capabilities, AWS offers the tools needed to innovate and grow.

AWS SageMaker

AWS SageMaker is a fully managed machine learning service that enables developers to quickly and easily build, train, and deploy machine learning models at scale. You can find more information on our AWS SageMaker tech page.

AWS Kiro

Kiro is an AWS-powered agentic coding service that uses "spec-driven development" to turn prompts into code and tests. You can find more information on our AWS Kiro tech page.

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AWS AI Technologies Hackathon projects

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

AIVE-Artificial Intelligence Venture Engine

AIVE (Artificial Intelligence Venture Engine) is a Cognitive Discovery Operating System that transforms unstructured information into structured knowledge, evidence-backed insights, and actionable opportunities. Instead of only summarizing documents, AIVE builds an internal understanding of the information it receives and reasons across multiple knowledge sources. The platform ingests research papers, patents, technical documents, reports, datasets, presentations, websites, and other digital content. It extracts concepts, entities, relationships, evidence, and patterns, organizing them into an interconnected knowledge structure that enables deeper analysis and intelligent reasoning. Users can upload their own knowledge, explore it through an AI copilot, ask complex questions, and receive responses supported by evidence with traceability to the original sources. Rather than relying on predefined workflows or keyword matching, AIVE is designed to adapt dynamically to new domains and continuously expand its understanding as additional information is introduced. Beyond search and summarization, AIVE identifies research gaps, hidden relationships, emerging trends, contradictions, technology transfer possibilities, commercial opportunities, and innovation pathways. It generates research-grade reports, interactive knowledge graphs, visualizations, comparison tables, timelines, and decision-support outputs to help users understand complex information and make informed decisions. Built on knowledge engineering, graph-based reasoning, retrieval systems, and large language models, AIVE provides a unified workspace for researchers, engineers, enterprises, and innovators. Its vision is to become a trusted cognitive partner that accelerates scientific discovery, product innovation, strategic decision-making, and enterprise knowledge management through transparent, explainable, and evidence-driven intelligence.

CrosslaneAI

CrosslaneAI is an agentic migration platform that autonomously ports CUDA codebases to ROCm/HIP, so teams can run their GPU workloads on AMD hardware without rewriting a single kernel by hand. For over fifteen years, the entire GPU computing world has been written in CUDA. That accumulated codebase — not the silicon itself — is the real reason teams stay locked to a single vendor. AMD's MI300X is genuinely competitive today, offering 192GB of HBM3 and far better price-per-hour economics, yet most organizations can't make the switch. The migration is manual, slow, error-prone, and expensive enough that it's typically handed to specialist consultants charging weeks of engineering time. PortForge productizes that migration and collapses it from weeks into minutes.

Probably runs on Samsung S25

Hybrid Router is an intelligent AI inference system that dynamically selects the best execution path for each user query instead of relying on a single large language model. The system first analyzes the incoming prompt to identify the task type, estimate its complexity, and determine whether it can be solved deterministically or requires generative AI. For structured tasks such as mathematical calculations, JSON validation, regular expression verification, and date/time operations, the router invokes specialized deterministic tools that produce fast, accurate, and reproducible results without consuming LLM tokens. For more complex natural language and coding tasks, the router attempts local inference using OpenVINO-optimized models running on Intel hardware, reducing latency and API costs. If the local model is unlikely to provide a sufficiently reliable answer or the task exceeds its capabilities, the system automatically falls back to Fireworks AI models for high-quality remote inference. The routing decisions are driven by task classification, confidence estimation, and configurable thresholds, allowing the system to balance accuracy, response time, and operational cost. The architecture is modular, making it easy to add new tools, local models, or routing strategies in the future. The project also includes benchmarking and evaluation components that measure routing accuracy, latency, model utilization, and fallback frequency to continuously improve routing performance. By combining deterministic tools, local inference, and cloud-based language models into a single adaptive pipeline, Hybrid Router delivers efficient, scalable, and cost-aware AI inference while maintaining high response quality across a wide range of tasks.

Preflight AI

A single typo in a Terraform file can cost an enterprise millions of dollars in downtime. Today, catching these errors manually in a Pull Request is nearly impossible because human reviewers cannot easily visualize the cascading "blast radius" of complex cloud architectures. Preflight AI is a DevSecOps platform and Chaos Engine that mathematically simulates infrastructure failures before the code is ever deployed. It acts as an automated Principal Cloud Architect. To build a product capable of this at an enterprise scale, standard AI pipelines aren't enough. We engineered a highly optimized Dual-Model Routing Architecture: For rapid logic synthesis, compliance, and security checks, we route workloads to the Fireworks AI API (DeepSeek-V4-Pro). To simulate hundreds of cascading failures simultaneously, we needed brute-force compute. We built a custom Monte Carlo Chaos Engine and offloaded it to a self-hosted Llama-3-70B model running on an AMD MI300X Developer Cloud GPU. The massive 192GB of VRAM allows us to process high-volume parallel simulations that would choke standard infrastructure. Developers can visualize this topological graph in our React Web UI, or they can install our custom GitHub Action to get automated, AI-synthesized architectural fixes posted directly to their Pull Requests. Preflight AI is a highly monetizable B2B SaaS platform built specifically to leverage the raw throughput of AMD hardware alongside the speed of Fireworks AI.

Nexus — Engineering Trust Layer

AI coding assistants can generate software quickly, but responsibility still belongs to human engineers. Nexus was built to make AI-assisted software verification transparent, reproducible, and trustworthy. Nexus is an Engineering Trust Layer that separates every software decision into three stages: executable evidence, AI reviewer analysis, and human approval. Instead of asking developers to trust an AI, Nexus requires every decision to be backed by evidence that can be inspected and replayed. The demonstration begins with an AI-generated code submission. Nexus freezes the change, executes automated verification using pytest and Hypothesis property-based testing, and records reproducible evidence, including generated counterexamples when verification fails. A Fireworks AI reviewer analyzes the evidence and provides an independent opinion, but the AI never makes the final decision. Human reviewers remain responsible for approving or rejecting changes. Every decision is stored in an append-only audit trail through the Decision Workspace API backed by SQLite. Replay Mode demonstrates a contract-validated verification session, while Live Mode performs real backend reads and writes. If any dependency is unavailable, Nexus follows a Fail Honest policy by reporting the failure instead of fabricating results. For the AMD Developer Hackathon, we implemented an AMD execution adapter for AMD Developer Cloud. Cloud activation was requested during the hackathon but was still pending when this submission was prepared. Rather than simulate AMD execution, we honestly disclose this limitation. The adapter is implemented and ready, but we make no unsupported claims about MI300X execution. Nexus includes a demonstration console, evidence pipeline, live Fireworks reviewer integration, append-only audit storage, comprehensive documentation, and a fully passing automated test suite, providing a trustworthy foundation for AI-assisted software engineering.

BoundaryForge

BoundaryForge AI is a pre-deployment AI risk evaluation system for regulated financial use cases. It helps teams assess whether an AI model is safe enough to move toward deployment by testing security resilience, governance behavior, and trustworthiness under realistic regulatory pressure. The system uses document-grounded adversarial prompts generated with Gemma 4 via Fireworks AI. The attack corpus is grounded in financial-sector regulatory themes from RBI Cyber Security, RBI KYC, FCA AI Update, PRA SS1/23, and the EU AI Act. BoundaryForge then executes these prompts against selected target models, including OpenAI and Anthropic Claude models, and scores the responses using independent Claude/GPT judges. BoundaryForge evaluates prompt injection, refusal bypass, role escalation, governance-bypass enablement, source-fidelity gaps, and hallucination risk. It separates attacker, target, and judge roles to reduce self-evaluation bias and create a more realistic audit workflow. The project also includes AMD ROCm validation proof for the model-generation workflow and uses a frozen attack corpus for repeatable evaluation. After each run, BoundaryForge produces surfaced findings, execution-state counts, risk metrics, a one-page AI risk certificate, SHA256 fingerprinting, and downloadable full audit JSON evidence. BoundaryForge does not claim legal compliance certification. It provides AI risk awareness, adversarial evaluation evidence, and deployment decision support for high-stakes financial AI systems.