
3
2
Pakistan
1 year of experience
I'm a Computer Science student from Pakistan with knowledge of C++, Java, and Python. I have completed a Python for Machine Learning course on Coursera and I'm passionate about AI and software development. I'm continuously improving my skills through coding practice and personal projects. I'm excited to collaborate, learn, and build impactful AI applications in hackathons.

BridgeForge solves a common software development problem called API contract drift. This happens when a backend developer changes an API field, response, or endpoint, but the documentation and other dependent parts of the project still use the old contract. A small change can then cause confusion, broken client code, failed tests, and outdated API documentation. Our prototype shows this with a simple product API. The backend originally returns a field called title. We then rename it to productName. BridgeForge watches the backend route files for changes. When it detects the contract change, it automatically invokes IBM Bob Shell. Bob reads the current backend code as the source of truth and updates two dependent documentation files: docs/API.md and docs/openapi.json. The main users are software development teams, especially backend developers, frontend developers, API maintainers, and technical writers. They do not need to manually ask the AI to check every change. They work normally on the backend, and BridgeForge starts the synchronization process when a monitored API file changes. A React dashboard also shows the contract drift, the affected area, before-and-after differences, and the synchronization workflow. What makes BridgeForge different is that it uses AI proactively instead of waiting for a developer to open a chat and ask for help. The current prototype focuses on API documentation because it is a clear and reliable example of contract drift. The same architecture can later be extended to frontend bindings, tests, SDKs, and other downstream files. BridgeForge reduces repetitive manual work and helps development teams keep API documentation aligned with the actual backend implementation.
27 Sep 2026

Aethel Bio is an enterprise precision oncology platform built 100% on Natively AI, bridging Next-Generation Sequencing (NGS) pathology data with active clinical trials in real time. THE PROBLEM Late-stage cancer patients are out of time, but traditional trial matching is bogged down by 50-page unstructured pathology PDFs. Over 80% of eligible patients are rejected due to rigid inclusion cutoffs without waiver evaluation, while pharma sponsors lose millions opening trial sites blind to regional patient demand. OUR SOLUTION & ARCHITECTURE Orchestrated on Natively AI, Aethel Bio transforms genomic reports into actionable clinical pathways: • AI Pathology Parser: Parses PDF, DOCX, and TXT files in under 3 seconds, extracting mutations (EGFR, KRAS, BRCA1), staging, and organ panels. • Protocol Waiver Sensitivity Slider: Interactive tolerance slider letting clinicians test "what-if" scenarios for borderline lab function (e.g., eGFR 54 vs 60 mL/min) without altering official EHRs. • Dual-View Exporters: Generates technical Physician Dossiers with PI referral drafts alongside plain-language Patient Guides with care-team checklists. • Sponsor Feasibility Analytics: Maps unmatched patients (<50% match score) into a de-identified market registry, giving pharma sponsors density maps and CSV/JSON exports. • 1-Click Guest Sandbox: Instant access for oncologists, and patients with zero sign-up friction. TECHNOLOGY Built on Natively AI using React/Next.js, TypeScript, Tailwind CSS, and client-side HIPAA sanitization. Natively AI orchestrates our Supabase (PostgreSQL) DB and secure ClinicalTrials.gov REST API v2 sync. IMPACT Aethel Bio slashes trial discovery time by 99%—turning days of research into 3 clicks, empowering patients, and optimizing clinical trial enrollment.
10 Aug 2026