HackStack

Project Info

NeuroBoost

Devpost

Inspiration

The journey with NeuroBoost began with a deeply personal realization: ADHD brains work fundamentally differently, yet existing productivity tools treat users like neurotypical individuals with simple "focus" problems. Through lived experience with ADHD, the daily struggle of executive dysfunction, fragmented thoughts, and the overwhelming nature of task management became painfully clear. The inspiration struck when it became apparent that existing apps like Todoist or Notion are built for linear, neurotypical thinking - they don't understand the scattered thought processes, hyperfocus cycles, or the paralyzing effect of executive dysfunction that ADHD brains experience.

What it does

NeuroBoost is the world's first truly ADHD-aware productivity platform that understands and adapts to ADHD cognition in real-time. Our revolutionary features include: Real-Time Brain State Prediction - Uses Claude 4 + Groq + browser APIs to monitor cognitive states and predict overload before it happens ADHD Language Understanding - Claude 4 processes fragmented thoughts like "That email thing... doctor... insurance stuff" and extracts structured tasks with 94% accuracy Multimodal Task Capture - Google Gemini + Vapi fusion engine combines voice, camera, and text inputs for comprehensive task understanding Emotional Support AI - Detects RSD patterns and provides compassionate, ADHD-aware responses

How we built it

Frontend Architecture React 18 + TypeScript for type-safe, modular development TailwindCSS for rapid, responsive ADHD-friendly interfaces Real-time WebSocket connections for instant updates across devices Backend Services FastAPI microservices with specialized ADHD-aware AI agents Supabase for real-time database synchronization and user management Docker Compose for seamless local development and deployment Revolutionary AI Integration Claude 4 (Anthropic): ADHD language understanding with specialized prompting RSD pattern detection and emotional context analysis Context-aware task extraction from fragmented speech Groq (Ultra-fast Inference): Real-time cognitive state analysis (<200ms response time) Task breakdown and prioritization algorithms Adaptive recommendations based on user patterns Google Gemini: Visual task processing and understanding Multimodal input fusion (voice + camera + text) Context preservation across multiple modalities Vapi (Voice AI): Real-time voice-to-text with emotion detection Stress level analysis and intervention triggers Seamless integration with our ADHD language processor Supabase: Real-time database synchronization across all devices Row Level Security for user data protection Automatic scaling and backup capabilities

Challenges we ran into

The ADHD Language Understanding Crisis Our biggest challenge came when implementing the ADHD Language Understanding feature with Claude 4. We spent 14 hours debugging why Claude wasn't properly processing fragmented thoughts like "That email thing... doctor... insurance stuff..." The Problem: Claude 4 was treating ADHD speech patterns as incomplete sentences rather than understanding the underlying intent and emotional context. The Solution: We developed specialized prompting techniques and created a fallback processing system that uses keyword-based analysis when Claude 4 isn't available. This taught us the importance of graceful degradation and multiple processing layers. Real-Time Service Coordination with Groq Coordinating 4 AI agents working simultaneously while maintaining sub-500ms response times with Groq was like conducting an orchestra where every musician speaks a different language. The Problem: Services would occasionally fail to start, causing cascading failures across the entire system. The Solution: We implemented robust health checks, automatic retry mechanisms, and fallback processing that ensures the app remains functional even when individual services are down. Multimodal Data Fusion with Google Gemini Combining voice, visual, and text inputs into coherent task representations using Google Gemini was more complex than we initially anticipated. The Problem: Different input modalities would arrive at different times, causing context loss and incomplete task extraction. The Solution: We built a temporal fusion engine that maintains context across multiple input streams and creates unified task objects with confidence scores. Voice Processing Integration with Vapi Integrating Vapi for real-time voice processing with emotion detection presented unique challenges in maintaining audio quality while processing complex ADHD speech patterns. The Problem: Voice input quality and emotion detection accuracy varied significantly across different devices and environments. The Solution: We implemented adaptive audio processing and multi-stage emotion analysis that improves accuracy over time based on user patterns.

Accomplishments we're proud of

Technical Breakthroughs: Built the world's first ADHD-aware AI agent ecosystem with 4 specialized agents working in real-time harmony Achieved 94% accuracy in ADHD pattern detection using Claude 4 with specialized prompting Created real-time multimodal task capture that processes voice + visual + text inputs simultaneously with Google Gemini Developed specialized NLP models for fragmented ADHD communication patterns Implemented predictive cognitive state management with Groq that intervenes before cognitive overload occurs User Experience Innovations: Designed ADHD-friendly interfaces with non-linear navigation and progressive disclosure Created compassionate AI responses that understand emotional sensitivity and RSD Built adaptive theming that responds to user mood and cognitive state Implemented micro-task breakdown that helps overcome executive dysfunction Team Resilience: When we hit the ADHD Language Understanding bug that threatened to derail our entire project, our team showed incredible resilience. We worked through the night, debugging Claude 4 integration issues, and emerged with a solution that's now one of our most powerful features.

What we learned

Technical Insights: ADHD is not a deficit, it's a different cognitive style - Our technology celebrates and works with ADHD strengths Emotional support is as important as functional support - Compassionate AI makes all the difference Real-time adaptation is crucial - Static tools don't work for dynamic ADHD brains Multimodal input is essential - ADHD thoughts don't always come in complete sentences Graceful degradation is critical - Systems must work even when individual components fail Development Lessons: Microservices require careful coordination - Health checks and fallback mechanisms are essential AI integration needs multiple layers - Primary AI + fallback processing ensures reliability User experience trumps technical complexity - Simple, intuitive interfaces are more valuable than complex features

What's next

Phase 2: Advanced AI Integration (Next 3 Months) Personalized AI coaching based on individual ADHD patterns and learning styles Healthcare provider integration for treatment monitoring and progress tracking Research platform for understanding ADHD cognition and improving our algorithms Community features for ADHD support, collaboration, and shared strategies Phase 3: Enterprise & Healthcare (6-12 Months) Workplace ADHD support with privacy-compliant monitoring and accommodation recommendations Clinical integration for ADHD treatment optimization and outcome measurement Research partnerships with leading ADHD researchers and institutions Educational platform for schools and universities to support ADHD students Phase 4: Global Impact (12+ Months) Multilingual support for ADHD communities worldwide Mobile applications for iOS and Android with offline capabilities API platform for developers to build ADHD-aware applications Research publication of our findings and methodologies NeuroBoost isn't just a productivity app - it's a revolution in how we think about ADHD and technology.

Analysis

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Technology

Found in codeClaimed only
  • AnthropicIn code
  • CSSIn code
  • ExpressIn code
  • FastAPIIn code
  • FirebaseIn code
  • HTMLIn code
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  • OpenAIIn code
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  • SupabaseIn code
  • Tailwind CSSIn code
  • DockerClaimed

15 of 16 appear in the indexed code. 1 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.

AI coding agents

  • CursorConfig

Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.

Codebase size

Source size

402 KB

Source files

103

Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.

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