Ravi Manghwar@ravim_nlp

Target Audience & Market Validation RadarAI is built for U.S. brick-and-mortar small business owners and solo-founders in the service and hospitality sectors (coffee shops, bakeries, car washes, salons, and auto repairs).Official SBA reports confirm that over 82% of U.S. small businesses are solo-founders with zero paid employees. Reddit sentiment research validates that these owners are completely trapped in daily operational tasks like sales, inventory, and staffing, leaving them with zero time or capacity to manually track local market fluctuations or competitor pricing trends. The Problem Small business owners lack the time for daily market research. When competitors alter working hours, adjust pricing, or secure new reviews, surrounding businesses remain blind to the shift until weeks too late. This critical visibility gap causes immediate customer attrition, margin erosion, and thousands in lost revenue. These busy entrepreneurs don't need complex data dashboards—they need immediate, stress-free market clarity. Product Solution & User Journey RadarAI is an automated revenue shield with a 10-second setup requiring only Google Maps links—no passwords or bank data.The live version provides an instant-value sandbox of Marfa, Texas. The user journey is seamless: the owner hits "Explore" on the landing page, views the interactive map, zooms into their neighborhood, and clicks "Refresh Data." RadarAI handles the rest, turning public web data into instant actionable insights. Infrastructure, AI Logic & ScalingBuilt via Native.Builder, RadarAI uses scraping and an OpenAI core to parse real-time map data into a three-step plan: Fact, Opportunity, and Prediction with Zero-Data Retention. Validated and ready to scale, the SaaS pipeline expands from niche tracking to scenario simulation, storefront sync, and global multi-sector deployment.

The solution is a hackathon onboarding ecosystem that helps participants move from registration to a working team setup with minimal friction by using a hackathon-specific onboarding page as the main source of truth for setup checklists, team assignment or team-creation instructions, submission rules, teammates, and mentor information, while an AI assistant only steps in when the page is unclear or when users need troubleshooting for issues like missing credits, promo codes, team confusion, or mentor lookup; the flow starts by identifying the participant’s hackathon and routing them to the correct page, then guides them through creating an AMD Developer Cloud account, claiming cloud credits, creating a Fireworks.ai account, redeeming promo credits, generating an API key, connecting that key to the AI builder, and linking GitHub so teams can access a boilerplate repository and collaborate quickly; at the team layer, it helps users find their team, team leader, teammates, Discord handles, GitHub usernames, team channels, repository links, and mentor details when available, and if data is missing or inconsistent it explains the limitation and points users back to the onboarding page or organizer instructions; the architecture should be model agnostic, token efficient, secure, observable, rate-limited, and evaluated for faithfulness, relevance, coherence, groundedness, and toxicity so the system remains reliable throughout the hackathon.

The War Room is an autonomous, multi-agent incident response platform designed to accelerate cloud service recovery and reduce Mean Time to Resolution (MTTR). When outages or spikes occur, instead of engineers manually checking disparate monitoring systems, The War Room coordinates a synchronized swarm of specialized AI agents. Directed by an Incident Commander (orchestrated with LangGraph), these agents include Metrics (Telemetry), Logs (Code Audits), Change (CI/CD Deployments), and Runbooks (Procedure Retriever) specialists. Unified via the event-driven Band SDK, these agents perform parallel diagnostics, exchange findings over deliberation channels, and submit structured evidence. A mathematical consensus engine computes dynamic confidence scores by weighing domain findings and analyzing deliberation sentiment. If confidence meets security gates, the platform generates a step-by-step interactive remediation plan (e.g., database rollbacks, traffic caching, container scaling). Finally, it compiles a complete markdown postmortem report and automatically commits it to Git via simulated Git-Ops, creating a traceable GitHub postmortem record.

RepoQuest is an AI-powered developer onboarding and first-PR simulator. When a user enters a GitHub repository, RepoQuest analyzes the project structure and turns the codebase into a guided learning journey. Instead of only generating static documentation, it creates a repository overview, architecture map, onboarding missions, mentor-style hints, suggested tests, contribution difficulty scoring, and a first PR readiness report. The goal is to help interns, new engineers, open-source contributors, and hackathon teammates move from “I don’t understand this repo” to “I know where to contribute.” IBM Bob IDE is used as the core development partner for understanding code, refining the product workflow, generating mission logic, improving documentation, and preparing first PR guidance. IBM watsonx.ai with Granite enhances AI-generated outputs, while watsonx Orchestrate supports the workflow as a conversational onboarding coach. RepoQuest reduces onboarding friction, saves mentor time, and helps developers make meaningful contributions with more confidence.

🌎 Online Hackathon | 📅 August 3–10, 2026| 💻 Powered by native.builder The official native.builder hackathon, hosted by NativelyAI and lablab.ai. Join founders, developers, designers, and builders to create real AI-native applications powered by native.builder: from idea to deployed product, fully in the cloud. 💻 This hackathon is fully online. No travel required.

To participate in the hackathon, simply click Sign up with AMD. If you are not already a member of the AMD AI Developer Program (ADP), you will need to create an account. ADP members can access AI tutorials, experts, and community support. In addition, new members can claim a free month of DeepLearning.ai pro, $100 in AMD GPU cloud credits, and $50 in Fireworks AI API credits. All hackathon participants will receive on hackathon launch additional compute and API credits for their projects. Ready to build what's next on AMD? Sign up with AMD below to secure your spot.

📅 June 12–19, 2026 • June 12: Kick-off Stream • June 12–19: Online Build Phase • June 19: Project Submissions Ends 🌍 Fully online hackathon. Join and build from anywhere in the world. 🤝 Build practical enterprise workflows where AI agents communicate, coordinate, exchange context, and complete work together. 🧠 Powered by Band and Codeband, built for collaborative AI systems and agent orchestration. 🏆 Prize Pool: $10,000+ 🧑‍💻 Apply now to build the next generation of enterprise multi-agent systems.