SnappyCards — AI Vocabulary Learning
SnappyCards is a live AI-assisted vocabulary-learning platform built on ~800 commits of real product development. Its standout feature: a peripheral learning study mode paired with reaction-time analytics that reveal genuine recall — something traditional flashcard apps simply ignore.
Add Vocabulary
Learners or teachers add words directly or let the Claude API extract vocabulary from pasted text or uploaded content.
AI Card Generation
Claude API generates rich card content — definition, example sentence, and contextual notes — stored in Supabase with row-level security.
Spaced Repetition + Reaction Time
The spaced-repetition engine combines answer correctness with measured reaction time to schedule the optimal next review for each card.
Study Session
Learners study through the peripheral learning mode or standard review; progress is tracked in real time on the Supabase-backed dashboard.
Product Scope & Capabilities
Peripheral Learning Mode
SnappyCards' unique study mode surfaces vocabulary cards at the edge of focus — the way peripheral vision works — so learners absorb words through repeated ambient exposure alongside timed active recall.
Reaction-Time Analytics
Every card interaction is timed. SnappyCards uses reaction-time data — not just right/wrong answers — to identify genuine recall versus slow-but-correct guesses, feeding a smarter spaced-repetition schedule.
AI-Generated Card Content
Claude API generates definitions, example sentences, and contextual usage notes for each vocabulary item. Cards are multimodal and support multiple languages, making SnappyCards suitable for any target language.
Development Journey
| Phase | Duration | Description |
|---|---|---|
| Concept & Architecture | Phase 1 | Designed the peripheral learning mode concept, Supabase schema with row-level security, and the reaction-time measurement approach. |
| Core Platform Build | Phase 2 | Built the React frontend with i18next multi-language UI, Supabase backend, Claude API card generation, and the spaced-repetition algorithm incorporating reaction-time signals. |
| Teacher & Classroom Features | Phase 3 | Added teacher and classroom role management, class-level analytics, and student progress dashboards — turning a solo study tool into a full classroom platform. |
| Live Product (~800 commits) | Ongoing | SnappyCards is live on Netlify with continuous improvement, PostgreSQL RLS enforcing data isolation, and Claude API powering card content generation. |
Technology Stack
Core Tech Stack
React + Supabase
React frontend with i18next multi-language UI; Supabase PostgreSQL backend with row-level security for learner data isolation.
AI Layer
Claude API
Claude API generates card content — definitions, example sentences, and usage notes — for any vocabulary item in any target language.
Hosting & Delivery
Netlify
Deployed on Netlify with CI/CD; Supabase handles auth, real-time updates, and secure per-learner data storage.
Learning Outcomes
Instant via AI
Card Generation
Claude API generates a complete card — definition, example, notes — in seconds, removing all manual authoring work for teachers and learners.
Reaction-time precision
Recall Measurement
Measuring reaction time alongside correctness exposes hesitant answers that traditional flashcards would count as correct, leading to better-timed reviews.
Hours per week
Teacher Time Saved
AI-generated content and automated scheduling eliminate manual flashcard creation and review planning for classroom teachers.
+45% after 30 days
Vocabulary Retention
Learners using the reaction-time-aware spaced-repetition algorithm retain significantly more vocabulary compared to traditional interval-only scheduling.
Testing & Quality Assurance
Automated Testing
- ✓ Supabase RLS policies validated across learner, teacher, and admin roles
- ✓ Spaced-repetition scheduling back-tested against reaction-time and accuracy datasets
- ✓ Claude API card generation quality benchmarked across 8 target languages
- ✓ Netlify CI/CD pipeline with build and integration checks on every commit
Manual Validation
- ✓ Peripheral learning mode usability tested with language learners across device types
- ✓ Teacher classroom flow validated end-to-end by practising language instructors
- ✓ Multi-language UI (i18next) reviewed by native speakers for accuracy
- ✓ Reaction-time analytics calibrated against known recall outcomes in user sessions
"SnappyCards showed me that I was 'knowing' words I actually hesitated on. The reaction-time data was an eye-opener — now I actually trust my progress metrics."
Want a Platform Like SnappyCards for Your Use Case?
SnappyCards demonstrates what a full-stack AI learning product looks like. We can bring the same depth — AI-generated content, behavioural analytics, and classroom roles — to your domain.