Hasnain Ahmad@HasnainAhmad

Industrial maintenance teams often receive disconnected sensor readings, sound anomalies and model scores without a clear explanation of what they mean or what evidence is missing. Telltale bridges that gap through an evidence-grounded maintenance decision-support workflow. The platform brings together local machine-learning services for sensor failure-risk estimation, machine-sound anomaly detection and bearing fault classification. A FastAPI backend organizes available results into assessment snapshots, preserving original scores, model provenance and data-quality limitations. A central AI analysis service uses AMD Token Factory for three interpretation tasks: AI Result Explanation, Multi-Model Analysis, and Evidence & Uncertainty Review. Structured template-based workflows support Maintenance Report Generation and Ask Telltale, providing predictable outputs grounded in available assessment data. Our separately developed frontend is being integrated with these APIs to present model indications, missing evidence and maintenance context in one technician-focused experience. Existing approved playbook guidance supports inspection recommendations where applicable. Telltale's distinction is not simply combining models: it separates prediction from explanation, keeps different score types distinct and makes uncertainty visible. Missing evidence is never treated as proof that a machine is healthy. AI-generated interpretations support human review rather than autonomous equipment control. The result is a traceable path from machine signals to maintenance understanding, helping technicians review indications, prioritize inspections and document findings without presenting model predictions as confirmed mechanical diagnoses. Technologies: Python, FastAPI, Pydantic, LightGBM, SHAP, ONNX Runtime, NumPy, pandas, SciPy, React, and AMD Token Factory API.

Problem Traditional reservation systems use basic lists and forms. They often ignore the physical floor, make table combinations difficult, limit staff visibility, and may face concurrent booking conflicts. Composable Floor makes the restaurant floor central to booking and operations. Solution Composable Floor offers a visual 2D/3D floor experience for restaurant discovery, availability, table-group selection, and staff operations. PostgreSQL constraints and atomic booking logic help protect reservation correctness. Key Differentiator Our key differentiator is the combination of a functional 3D floor, composable table groups, and database-level double-booking prevention. The floor shows real table states, while the database remains the source of truth. Frontend Technologies Next.js, React, TypeScript, Tailwind CSS, CSS design tokens, React Three Fiber/Three.js, Zustand, and Zod. Backend Technologies Next.js API routes, Supabase Auth, PostgreSQL, RLS, constraints, triggers, atomic booking logic, advisory locks, Realtime, and polling fallback. Target Users Restaurant guests, hosts, servers, floor managers, restaurant managers, and owners—especially teams needing flexible table combinations and better floor visibility. Future Scope Smart table recommendations, demand heatmaps, QR check-in, email confirmations, advanced floor editing, saved layouts, utilization analytics, restaurant branding, and offline staff support.

Incident Commander tackles the slow, fragmented first-response work after a production bug: engineers must connect a failing test or stack trace to the responsible code change, verify a proposed repair, and document what happened while under pressure. The project is designed for on-call engineers and small development teams, and targets the hackathon's debugging and application-maintenance challenges. In the demo, an engineer enters a service, severity, failing test, and raw error log in one dashboard. Incident Commander presents a three-role investigation: Log Analyst identifies the failing function and symptom; Code Historian links the evidence to a suspect Git commit; Fix Writer proposes a focused patch. The combined report surfaces the source file, test, commit, and plain-language explanation, so the engineer can review the reasoning instead of treating the answer as a black box. In this submitted MVP, these agent findings are loaded from structured project files; the deployed backend does not invoke live Bob or watsonx agents at runtime. The FastAPI backend adds context from the sample runbook and README, runs the original sample tests to confirm the seeded bug, then applies the proposed change only to a temporary copy and runs the tests again. It records the actual test result and cleans up the copy, leaving the original sample app untouched. In the submitted recording, the proposed-fix check fails; the dashboard correctly marks the fix unverified and requires human review. The project does not auto-deploy. The workflow then drafts an incident postmortem containing a timeline, impact, root cause, and prevention steps. Its creative value is the complete, explainable loop from incident evidence to change history, a reviewable patch, an independent test outcome, and reusable documentation—not just a code suggestion. By bringing those steps into one guided workspace, it aims to reduce context switching, help engineers reach a trustworthy first read faster, and keep final operational decisions with the human on-call engineer.

Deal Shield is an AI-powered Contract & Vendor Risk Agent built for small businesses, startups, founders, procurement teams, and operations managers who need faster and clearer business risk assessment. Users can upload a business contract in PDF format and receive an AI-assisted analysis of important clauses, including payment terms, termination, confidentiality, intellectual property, liability, indemnity, renewal, data privacy, governing law, and dispute resolution. Each finding includes a risk classification, plain-English explanation, supporting context, and practical negotiation recommendation. The application also provides vendor intelligence by assessing available vendor information and credibility signals, including reputation, reviews, business details, and news-related indicators where available. Contract risk and vendor risk are combined into an explainable Deal Safety Score to support more informed business decisions. The workflow is simple: Upload Contract → Extract Text → Analyze Clauses → Assess Vendor Risk → Calculate Deal Safety Score → View Recommendations. Deal Shield is built primarily with native.builder using React, TypeScript, Supabase, Supabase Edge Functions, and AI-powered analysis workflows. AI output supports business risk review and is not a substitute for advice from a qualified legal professional.

AI Research Agent is a cutting-edge AI-powered research assistant designed to simplify and accelerate the process of gathering information from multiple online sources. Users can input their research queries, and the agent automatically fetches relevant data, analyzes it, and presents summaries along with interactive visualizations. By integrating tools like Streamlit for the interface, Pandas for data analysis, and Plotly for dynamic charts, this application ensures a smooth, efficient, and user-friendly experience. It is ideal for students, researchers, and professionals who need accurate, structured, and timely insights. Future enhancements include support for more databases, AI-based suggestions, and mobile accessibility

AI Career Copilot is an intelligent, all-in-one platform designed to empower job seekers and professionals at every stage of their career journey. Our tool offers: 🎤 Mock Interview Simulator: Practice behavioral, technical, and situational interviews with AI-generated questions and receive real-time feedback tailored to your target role and experience level. 📄 Resume Scanner: Upload your resume for instant analysis. Get ATS compatibility scores, keyword optimization tips, and actionable suggestions to improve your resume’s impact. 📊 Skill Gap Analysis: Identify missing skills required for your dream job. Compare your current skill set with industry demands and receive personalized learning recommendations. 🗺️ Personalized Roadmaps: Generate 30/60/90 day learning plans based on your career goals, current level, and available time commitment. Stay on track with structured milestones and curated resources. Built with Flask backend and a modern glassmorphism-based frontend, AI Career Copilot combines advanced AI feedback algorithms with an intuitive user experience. Whether you're a developer, data scientist, product manager, or marketing professional, our platform provides the guidance and insights you need to succeed.

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🌎 Hybrid Hackathon | 💻 Online Build: 12-17 October 2026 | On-site phase: 17-18 October 2026 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. The AMD Developer Hackathon: ACT III is a hands-on event for developers, founders, engineers and builders who want to push what’s possible with AI on real infrastructure. 📍 If approved for the on-site experience, you'll also have the opportunity to showcase your project live. Travel and accommodation will not be covered.

🌎 Online Hackathon | 💻 Online Build: Sept 26 - Oct 5, 2026 A virtual, global hackathon. Build from anywhere in the world. Dark Factory is the official hackathon of WeAreDevelopers World Congress North America, presented by BAND, but you don't need to be in San Jose to take part. For one week, you build a software factory in BAND Desktop: a band of coding agents that plans work, implements it and checks its own results. 🎟️ Bonus for sign-ups: you are eligible for a free ticket to WeAreDevelopers World Congress North America in San Jose (Sep 23–25). If you do go, stop by the BAND booth: a surprise is waiting for you.

💻 Online Hackathon | ⏳ Build: 48 hours | 📅 September 25–27, 2026 The official IBM Bob 2.0 hackathon. Build what's next in AI-assisted development, alongside the developers and experts shaping it. ⭐ Get help from experts throughout the event 🤝 Join a team or build solo 💰 Prize pool: $12,000 🧑🏻‍💻 Register before the Kick-Off Stream to start building with Bob