Projects & Open Source
Selected projects and contributions showing how I build AI, backend, security, and open source systems.
TruthLens: An AI-Assisted Credibility Assessment Platform
Strategic Vision: Complementary Intelligence. Built as a Decision Support System (DSS) that augments human judgment rather than replacing it. It delivers multi-dimensional, bilingual (Arabic & English) analysis synthesizing linguistic patterns, contextual cues, and source credibility against high-velocity misinformation.
Original Concept + End-to-End Technical Owner
Proposed the original TruthLens concept and led the core technical implementation across backend architecture, AI/NLP orchestration, Celery/Redis asynchronous processing, ChromaDB-backed retrieval, Docker-based local deployment, frontend integration support, technical documentation, and privacy-focused PII handling.
Completed as a team graduation project at Al-Balqa Applied University (BAU), supervised by Eng. Nawal Al-Zabin, and defended in January 2026.
Multi-Layer AI System
- • Local LLM reasoning and ML classifiers.
- • NLP, OCR, retrieval, and security screening.
- • Layered analysis for source context, bias, toxicity, OSINT signals, and report generation.
Local AI Optimization
Optimized deep-analysis waiting from roughly 8 minutes to about 5–6 minutes in local RTX 4050 6GB VRAM testing. The optimization combined Celery/Redis background tasks, composite prompting, asynchronous coordination, and model-availability improvements under consumer-grade hardware limits.
Local benchmark; hardware-dependent.
Structured AI Output
Worked on composite prompt design, JSON-oriented output handling, validation logic, cleanup steps, and fallback-oriented processing to make local LLM outputs easier to parse, explain, and present.
Local Privacy vs Cloud Speed
Chose a local-first AI direction to prioritize privacy, cost control, and independence from third-party LLM APIs. The latency trade-off was handled with background processing, progress-update design, and asynchronous workflows.
Technical report and architecture documentation.
Docker-based local deployment workflow.
PWA interface, real-time feedback, visual analysis, and exportable reports.
Bilingual Arabic/English credibility analysis.
Academic portfolio project; TruthLens is an AI-assisted credibility assessment and decision-support system, not a production fact-checking authority.
Full System Demonstration
OCR, multilingual NLP, RAG context, source checks, and AI-assisted reasoning.
Decision-support system for credibility analysis, not an official fact-checking authority.
Demo Chapters
- • 0:00 - 1:15: Interface Tour & Input Methods
- • 1:15 - 3:00: Asynchronous Job Lifecycle & Real-time Progress
- • 3:00 - 4:45: NLP & Sentiment Layers Inspection
- • 4:45 - 6:00: Report Generation & Credibility Insights
What this Demo Demonstrates
- • Interactive PWA UI: Real-time feedback and responsive workspace layout.
- • Backend Workflow: FastAPI gateway, Celery background tasks, and Redis queuing.
- • Structured AI Reports: Validation parsing of multi-model LLM outputs into readable insights.
Multi-Model Consensus
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01
Qwen 3 (8B): Primary Reasoning Engine for complex semantic context and logical coherence analysis.
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02
Phi-3 Mini (3.8B): Self-Healing Agent ensuring structural data integrity and JSON validation/repair.
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03
Custom Logistic Regression: High-performance clickbait detection model deployed via Hugging Face.
Advanced NLP Layer
Deep integration of CAMeL-BERT for Arabic linguistic nuances and Twitter-RoBERTa for English social sentiment. The NLP layer uses SpaCy NER to detect named entities such as people, organizations, locations, and referenced topics, then enriches those entities through the Wikipedia API to provide additional context during credibility analysis. It is augmented with Detoxify for toxicity and hate-speech signals, Tesseract OCR for visual text extraction, and FastText for fast language detection.
Performance Arch
Engineered on a Python 3 / FastAPI / Uvicorn stack where the API gateway validates requests, dispatches long-running analysis jobs, and returns a task ID without blocking the user interface. Celery and Redis handle background processing, task queues, result caching, and progress updates, while Server-Sent Events and Firestore listeners support real-time frontend feedback. Local RTX 4050 6GB VRAM testing informed the hardware-constrained optimization strategy, including model persistence, composite prompting, and worker lifecycle controls.
Data Intelligence
Advanced Adaptive Scraping pipelines using Newspaper3k, Trafilatura, DuckDuckGo Search, and ScraperAPI fallback layers for extracting article content and supporting context. Integrated RAG via ChromaDB for context injection, entity-aware retrieval, and comparison against supporting evidence. The system also considers source context, publication timing, recency cues, citation quality, domain signals, and available external evidence when framing credibility analysis beyond the text alone.
Defense & Privacy
Implements Proactive Threat Defense through VirusTotal API integration for automated URL scanning, alongside privacy-first preprocessing that redacts emails and phone numbers before analysis. The system is designed around content-based evaluation, local processing where feasible, and responsible AI disclaimers so users receive decision-support guidance rather than an absolute fact-checking verdict.
Future Roadmap
System architecture confirms full Structural Readiness for Google News Fact Check API integration. Future iterations focus on browser-extension analysis, trusted news feeds, trend monitoring, educational media-literacy prompts, and expanded cross-platform misinformation monitoring while preserving the platform’s decision-support positioning.
OpenSSF cve-bin-tool
PhishGuard Pro
securityAI-powered phishing and scam email analysis with threat probability, indicators, and explainable guidance.