Scientific, personalized, and stress-free learning
A revolutionary web application that transforms how you study, using scientific algorithms to maximize retention and minimize cognitive fatigue.
As Alan "The Gentleman" from the YouTube channel Gentleman Programming, I've always believed that technology should serve to improve our lives, not complicate them. Gentle Learning was born from a personal frustration: why do we continue studying the same way we did 100 years ago when neuroscience has given us much better tools?
Democratize scientific learning. I want anyone, from a high school student to a professional learning new technologies, to have access to the same study techniques used by the world's best researchers.
- 📚 You study for hours but forget everything in days
- 😵 You feel overwhelmed and don't know when to rest
- ⏰ You have no idea what your optimal learning time is
- 🔄 You repeat information without strategy
- 📱 Existing apps are generic and don't adapt to you
This application is based on real scientific research, not trends or opinions. Every implemented algorithm has empirical backing:
- 🔬 SM-2 Spaced Repetition: Automatic review scheduling with 15% more retention than traditional studying
- ⏰ Ultradian Rhythms: 90-120 minute cycles with optimal 52/17 work-break ratio
- 🎯 DeskTime 52/17 Ratio: Evidence-based timing for top performer productivity
- 🧠 Cognitive Load Theory: Sessions limited to 4±1 concepts for optimal memory processing
- 🌅 Chronotype Optimization: Up to 25% better performance with circadian rhythm sync
- 📲 JITAI Framework: Smart notifications timed for peak engagement (6-8am, 10pm-midnight)
- 🌿 Evidence-Based Breaks: 40-second nature scenes for 23% attention improvement
- 🎯 SART Assessment: Sustained attention measurement for personalized session lengths
- 🔄 Adaptive Fatigue Detection: Real-time optimization based on performance feedback
- 🧠 LECTOR Algorithm: LLM-Enhanced Concept-based Test-Oriented Repetition with 90.2% effectiveness
- 🔄 Interleaved Practice: Mixed vs blocked practice optimization with Cohen's d = 0.83
- Arizona State University: 18% increase in pass rates
- University of Mississippi: Reduction from 31% to 14.5% in dropout rates
- LECTOR Algorithm: 90.2% effectiveness in retention
- SART (Sustained Attention to Response Task) to measure your real concentration capacity
- Chronotype Evaluation to find your optimal time of day
- Pattern Analysis of response and reaction times
- Modified SM-2 for personalized spaced repetition
- Ultradian Optimization that respects your natural 90-minute cycles
- Dynamic Adjustment based on your real-time performance
- Accessible Dark Theme - WCAG 2.1 AA compliant
- Smart Notifications that don't bother you
- Visual Progress with scientific metrics
- Responsive to any device
- Angular 20 with Zoneless Change Detection
- Server-Side Rendering for optimal SEO
- CSS Variables for customizable themes
- Local Storage for total privacy
{
"frontend": "Angular 20 + TypeScript",
"runtime": "Bun (faster than Node.js)",
"styling": "SCSS + CSS Variables",
"architecture": "Zoneless + SSR + Standalone Components",
"deployment": "GitHub Pages",
"algorithms": "SM-2, SART, JITAI, Ultradian Cycles",
"accessibility": "WCAG 2.1 AA",
"data": "Local Storage (no tracking)"
}# Install Bun (faster than npm/yarn)
curl -fsSL https://bun.sh/install | bash# Clone the repository
git clone https://github.com/Gentleman-Programming/Gentle-Learning.git
cd Gentle-Learning
# Install dependencies
bun install
# Start development server
bun startbun start # Development server
bun run build # Production build with SSR
bun run build:github-pages # Static build for GitHub Pages
bun run lint # ESLint + Prettier
bun run test # Unit testsfunction calculateOptimalSession(userProfile: UserProfile) {
// Apply evidence-based 52/17 ratio for adults
if (userProfile.age >= 18 && userProfile.age <= 60) {
return {
sessionLength: 52, // minutes - DeskTime top performers
breakDuration: 17, // minutes - optimal 3:1 ratio
maxConcepts: 4 // Cognitive Load Theory limit
};
}
// Age-adjusted for younger/older learners
const attentionSpan = userProfile.age < 18
? Math.min(userProfile.age * 3, 45)
: Math.min(50, ULTRADIAN_CYCLE * 0.8);
return {
sessionLength: attentionSpan,
breakDuration: attentionSpan * 0.22,
maxConcepts: userProfile.age < 18 ? 3 : 4
};
}function calculateSessionQuality(session: StudySession, completionRate: number): number {
let quality = 3; // Start neutral
// Factor 1: Completion rate
if (completionRate >= 90) quality += 1;
else if (completionRate < 50) quality -= 1;
// Factor 2: Fatigue impact (inverse)
quality += (10 - session.performance.selfReportedFatigue) / 10;
// Factor 3: Focus score
if (session.performance.focusScore >= 80) quality += 0.5;
return Math.max(0, Math.min(5, quality));
}
function scheduleNextReview(quality: number, interval: number, easeFactor: number) {
const nextInterval = calculateNextInterval(interval, easeFactor, quality);
const newEaseFactor = updateEaseFactor(easeFactor, quality);
return {
nextReview: getOptimalReviewTime(new Date(), nextInterval),
interval: nextInterval,
easeFactor: newEaseFactor
};
}function getOptimalBreakActivities(duration: number) {
if (duration <= 2) {
// Microbreaks: 40-second nature scenes = 23% attention boost
return [
{ activity: 'View nature scenes', duration: 40, benefit: '23% attention improvement' },
{ activity: 'Deep breathing (4-7-8)', duration: 60, benefit: 'Reduces cognitive load' }
];
}
// Longer breaks: movement = 15% musculoskeletal improvement
return [
{ activity: 'Light physical movement', benefit: '15% musculoskeletal improvement' },
{ activity: 'Nature walk', benefit: '20% working memory boost' }
];
}// LLM-Enhanced Concept-based Test-Oriented Repetition
function calculateLectorInterval(
baseInterval: number,
semanticInterference: number, // 0.8-1.2 based on concept similarity
masteryLevel: number, // 0.5-2.0 based on performance history
repetitionCount: number,
personalFactor: number,
profile: UserProfile
): number {
// LECTOR formula: I(t+1) = H_eff(t) × α_semantic × α_mastery × α_repetition × α_personal
const alphaSemantic = Math.max(0.8, Math.min(1.2, semanticInterference));
const alphaMastery = Math.max(0.5, Math.min(2.0, masteryLevel));
const alphaRepetition = Math.max(0.9, Math.min(1.1, 1.0 + (repetitionCount * 0.02)));
const alphaPersonal = Math.max(0.7, Math.min(1.5, personalFactor));
const lectorInterval = baseInterval * alphaSemantic * alphaMastery * alphaRepetition * alphaPersonal;
return Math.max(1, Math.round(lectorInterval));
}// Optimize topic mixing for maximum learning benefit
function generateInterleavedSchedule(
topics: Array<{ id: string; name: string; difficulty: number; timeRequired: number }>,
totalSessionTime: number,
profile: UserProfile
) {
// Calculate interleaving benefit scores
const topicsWithScores = topics.map(topic => {
let interleavingScore = 1.0;
// Factor 1: Difficulty variation (higher score for mixed difficulty)
const avgDifficulty = topics.reduce((sum, t) => sum + t.difficulty, 0) / topics.length;
interleavingScore += Math.abs(topic.difficulty - avgDifficulty) / 5 * 0.3;
// Factor 2: Mastery level (lower mastery = higher interleaving benefit)
interleavingScore += (1 - (topic.masteryLevel || 0.5)) * 0.3;
return { ...topic, interleavingScore };
});
// Generate optimal A-B-C-A-B-C sequence with 10-20 minute segments
return createOptimalSequence(topicsWithScores, totalSessionTime);
}- SART-based scientific assessment with chronotype evaluation
- SM-2 spaced repetition with automatic review scheduling
- DeskTime 52/17 ratio optimization for adult learners
- Cognitive load theory implementation (4±1 concepts limit)
- JITAI framework for optimal notification timing
- Evidence-based microbreaks with nature scene recommendations
- Adaptive fatigue detection with real-time optimization
- Ultradian rhythm integration for session scheduling
- LECTOR Algorithm - LLM-Enhanced Concept-based Test-Oriented Repetition (90.2% success rate)
- Interleaved Practice - Mixed vs blocked practice implementation (Cohen's d = 0.83)
- Semantic Interference - Content-aware spacing adjustments
- Elegant dark theme with WCAG 2.1 AA compliance
- GitHub Pages deployment with CI/CD pipeline
- Advanced Break Activities - VR nature experiences and guided movement
- Biometric Integration - Heart rate variability for real-time fatigue detection
- Progressive Gamification - Neuroscience-based achievement system
- VR/AR Support - Immersive study environments
- Biometric Integration - Heart rate variability for fatigue detection
- Multi-language - Cultural adaptation of algorithms
- Offline Mode - PWA with synchronization
Contributions are welcome! As an educational developer, I firmly believe in open source and collaborative learning.
- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'feat: add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
- 🧪 More scientific algorithms (FSRS, LECTOR, etc.)
- 🎨 UX/UI improvements
- ♿ Accessibility and internationalization
- 📱 Progressive Web App features
- 🧪 Unit and integration tests
Alan "The Gentleman" Buscaglia
- 🔗 All Links: doras.to/gentleman-programming
This project is licensed under the MIT License - see the LICENSE file for details.
- 🔬 Neuroscience Research Community for years of learning research
🅰️ Angular Team for an incredible framework- 🍞 Bun Team for revolutionizing the JavaScript runtime
- 👥 Gentleman Programming Community for constant feedback
Made with ❤️ by Alan "The Gentleman"
"The best code is the one that helps people learn better"