Maham Taqi@maham_taqi_161

Tissue Regeneration & Genetic Factor Navigator is an advanced, AI-driven platform designed to bridge the gap between complex genetic data and actionable tissue regeneration insights. Built for researchers, clinicians, and biotech innovators, our solution addresses the challenges of processing high-dimensional biological data by deploying autonomous AI agents that automate analysis, modeling, and workflow orchestration. AI-Powered Genetic Insights leverages state-of-the-art language models and machine learning pipelines to parse, interpret, and synthesize intricate genetic datasets. Automated Biomedical Workflows reduces manual data handling by streamlining tissue regeneration tracking, cellular modeling, and predictive analytics. Interactive & Explainable Interface translates complex multi-layered bioinformatics into clear, actionable reports and visual dashboards, empowering faster clinical and research decision-making. By integrating modern AI infrastructure into bioinformatics, it accelerates discovery pipelines in regenerative medicine, helping scientists spend less time managing data and more time pioneering life-changing medical solutions.

Field Notes → Ecological Analysis is an AI-powered application designed to solve a persistent bottleneck for field ecologists, conservation researchers, and environmental consultants: the hours spent manually transcribing chaotic, unstructured field notes into digital reports. Instead of forcing users through rigid forms or cumbersome checkboxes, the app features a frictionless free-text input where researchers can simply paste their raw field observations. Utilizing advanced AI models including Gemini and Claude via Native.Builder and Supabase, the backend intelligently parses the prose to extract species names, environmental indicators, and threat mentions. The system automatically synthesizes this data into a clear, categorized UI report featuring an Overview, Species Observed, Environmental Indicators, and Threats/Recommendations. Furthermore, it provides comparative reasoning capabilities, automatically calculating metrics like current turbidity versus historical baselines. Built for speed and efficiency, this tool bridges the gap between field research and rapid compliance reporting for the growing biodiversity credit market.

In the rapidly evolving landscape of oncology, researchers and investors are often hindered by fragmented data. Triple-Negative Breast Cancer (TNBC) research is currently siloed across disparate sources—including PubMed, ClinicalTrials.gov, patent databases, and news outlets—making manual monitoring slow, expensive, and error-prone. TNBC Insight AI solves this by aggregating, analyzing, and delivering real-time intelligence through a single, unified dashboard. Built on a robust, enterprise-grade architecture using React and a Python/Flask API, our platform leverages OpenAI’s NLP to distill complex publications and trial reports into concise, actionable briefs. The system automates the entire intelligence workflow, allowing users to track drug pipelines, monitor trial enrollment, identify funding signals, and visualize patent landscapes in real-time. By automating these tasks, TNBC Insight AI enables teams to move at the speed of science, ensuring that critical competitive moves and emerging research trends are never missed.

BobGuard is an AI-powered security audit assistant built to help developers detect, understand, and remediate vulnerabilities in their codebases faster. The platform supports repository scanning for remote GitHub repositories as well as local project folders, then presents findings through a developer-friendly dashboard with severity grouping, OWASP mapping, and issue-level analysis. Instead of stopping at detection, BobGuard integrates IBM Bob to provide contextual AI remediation for each finding, including a plain-language explanation, why the issue matters, recommended remediation steps, and safer fix guidance. This makes the tool useful not only for identifying risks, but also for helping teams move toward secure fixes with less manual research. In our current implementation, a user can submit a repository, review detected findings in the dashboard, select a vulnerability, and click Ask Bob to get AI-generated remediation guidance in real time. The system has been tested locally end-to-end with successful scan and explanation flows, including rendering vulnerability summaries and Bob-generated fix guidance in the UI. BobGuard is designed as a practical secure development companion for students, developers, and teams who want faster feedback loops between vulnerability detection and safe remediation.

The Autonomous Business Engine is a high-performance, AI-driven bridge designed to automate the entire operational lifecycle of a business, transitioning from initial client contact to final payment. By utilizing ERPNext as the centralized "Brain" for data and AI Agents as the "Hands" for execution, the system creates a production-grade environment that eliminates manual data entry and minimizes human error. The technical architecture is built with Python and FastAPI, serving as a middleware layer that listens for triggers and communicates via REST API. The project features a comprehensive four-phase automation cycle: Autonomous CRM: AI qualifies leads from WhatsApp or email and automatically generates Quotation PDFs. Work Management: Upon quote approval, the AI initializes projects and assigns tasks to team members instantly. Accounts & Invoicing: The system calculates billable items upon task completion and sends final invoices to clients automatically. Reconciliation: An **AI Vision agent** scans payment screenshots to extract transaction details and update the ledger without manual accounting work. This integration drastically increases operational speed, turning processes that typically take days into tasks completed in mere seconds. Ultimately, it provides businesses with a 24/7 professional presence while significantly reducing the need for large administrative or accounting staff

🌎 Online Hackathon | 💻 Dates: 28 August–4 September 2026 | 🏆 $6,000 Prize Pool Build AI trading agents on Alpaca — autonomous agents and trading apps using Alpaca's Trading API, MCP server and CLI. 🌍 Fully online hackathon. Join and build from anywhere in the world.

🌎 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.