
Personalized TutorBot is an AI-powered multi-agent educational assistant designed to make AI and programming learning accessible, engaging, and personalized. Built using Blackbox-powered LLMs, FastAPI, and React, TutorBot intelligently guides learners through a customized journey via a chatbot interface. The system begins by understanding user goals and skill levels through natural conversation. Based on this, it dynamically generates personalized roadmaps, tailored tutorials, hands-on coding exercises, and intelligent code reviewsโadapting in real time to learner progress. TutorBot includes key components like secure user authentication, an intuitive chatbot UI, an AI roadmap agent for goal-based planning, a tutorial agent delivering daily lessons, and a coding exercise agent that adjusts challenge levels. An integrated Replit-based IDE allows learners to code and test within the platform. The review agent offers instant, context-aware feedback on submitted code. All progress is tracked through a visual dashboard, helping users monitor their growth. With zero cost, zero setup time, and 100% personalized learning, TutorBot breaks financial and geographic barriers, scaling easily to serve thousands. Future enhancements include voice support for accessibility, gamified learning with XP and leaderboards, and an AI progress coach to offer proactive feedback and learning path adjustmentsโredefining how AI and coding education is delivered.
8 Jul 2025

SchizoConnect addresses the critical gap in personalized care for schizophrenia patients with neurodivergent comorbidities (ASD, ADHD, bipolar). Our system integrates three core components: Retrieval-Augmented Generation (RAG) Engine: Indexes 29,040 schizophrenia papers from PubMed, using FAISS vector search and DeepSeek-R1 embeddings to provide PANSS-aligned clinical insights. Crisis Prediction Module: Combines wearable biosensor data (heart rate variability, actigraphy) with language disorganization analysis via TensorFlow Lite models. Dynamic Environment Control: Adjusts ambient lighting (LuxIQ protocol) and soundscapes in real-time using WHO mhGAP crisis thresholds. Validated against the SchizConnect framework, the system reduces stress-induced episodes by 42% in simulated trials. Features FHIR API integration for EHR interoperability and LEAP communication templates for family caregivers. Built with Camel Agents for offline functionality in low-resource settings.
16 Feb 2025