Algsoch is an advanced AI-powered study companion Android application that brings cutting-edge AI capabilities directly to your device. It's built using RunAnywhere SDK to enable 100% offline AI inference, ensuring your data stays completely private while providing instant, intelligent responses.
Version: 1.0.0
Platform: Android 10+
Size: ~45 MB (lightweight)
License: Open Source
Traditional study assistants and tutoring apps have several limitations:
- ❌ Require constant internet connection
- ❌ Upload sensitive data to cloud servers
- ❌ Slow responses due to network latency
- ❌ Limited to rigid question-answer formats
- ❌ Expensive subscriptions
Algsoch solves all of these:
- ✅ 100% offline - works anywhere, anytime
- ✅ Zero cloud uploads - complete privacy
- ✅ Sub-second responses - instant help
- ✅ Adaptive learning modes - learn your way
- ✅ Completely free - no hidden costs
Algsoch adapts to your learning style through 7 different response modes:
| Mode | Use Case | Example |
|---|---|---|
| Direct 💬 | Quick, concise answers | "What is photosynthesis?" → Direct answer |
| Answer ✓ | Focused, well-structured responses | "Explain this concept" → Structured answer |
| Explain 📖 | Deep, educational breakdowns | "Help me understand calculus" → Step-by-step explanation |
| Notes 📝 | Study notes in bullet format | "Summarize for exam" → Formatted study notes |
| Direction 🧭 | Problem-solving guidance | "How do I solve this?" → Approach guidance |
| Creative 💡 | Analogies and real-world examples | "Make it relatable" → Creative explanations |
| Theory 🔬 | Advanced conceptual deep-dives | "Theoretical aspects" → In-depth theory |
- Upload photos of handwritten notes
- Capture diagrams, equations, charts
- Get instant explanations via SmolVLM (Vision AI)
- Works completely offline
- All conversations stored on your device
- Review past learning sessions anytime
- Search through your study history
- Completely encrypted and private
- Sub-second latency
- No network delays
- Instant model inference
- Smooth user experience
- All AI runs on your device
- Zero data uploads to servers
- No tracking or analytics
- Your conversations are yours alone
app/src/main/
├── java/com/algsoch/
│ ├── MainActivity.kt # App entry point
│ ├── data/
│ │ ├── models/
│ │ │ ├── Message.kt # Chat message entity
│ │ │ ├── ChatSession.kt # Chat session data
│ │ │ └── UserPreferences.kt # User settings
│ │ └── repository/
│ │ ├── ChatRepository.kt # Chat data management
│ │ └── ModelRepository.kt # Model management
│ ├── domain/
│ │ ├── ai/
│ │ │ ├── PromptBuilder.kt # Builds prompts for 7 modes
│ │ │ ├── ResponseParser.kt # Parses AI responses
│ │ │ └── SystemPrompts.kt # 7 system prompt templates
│ │ └── models/
│ │ ├── LearningMode.kt # Mode enum
│ │ └── StructuredResponse.kt # Response structure
│ ├── services/
│ │ ├── ModelService.kt # Model registration & management
│ │ ├── AIInferenceService.kt # AI inference engine
│ │ ├── ModelDownloadManager.kt # Model download handling
│ │ └── AIChatService.kt # Chat operations
│ ├── ui/
│ │ ├── screens/
│ │ │ ├── HomeScreen.kt # Mode selection
│ │ │ ├── ChatScreen.kt # Chat interface
│ │ │ └── SettingsScreen.kt # User settings
│ │ ├── components/
│ │ │ ├── MessageBubble.kt # Chat message display
│ │ │ ├── ModeSelector.kt # Mode selection UI
│ │ │ └── ModelLoadingScreen.kt # Model download UI
│ │ └── theme/
│ │ └── AlgsochTheme.kt # Material 3 theming
│ └── viewmodel/
│ └── AlgsochViewModel.kt # MVVM state management
└── resources/
├── values/
│ ├── strings.xml # String resources
│ └── colors.xml # Color definitions
└── drawable/ # App icons & assets
| Layer | Technology | Purpose |
|---|---|---|
| UI Framework | Jetpack Compose | Modern declarative UI |
| Design System | Material Design 3 | Material 3 components |
| Navigation | Navigation Compose | Screen navigation |
| State Management | ViewModel + StateFlow | Lifecycle-aware state |
| Async Operations | Coroutines + Flow | Async tasks & streams |
| Local Database | JSON File Storage | Chat history storage |
| AI Engine | RunAnywhere SDK | On-device inference |
| Language | Kotlin | Modern, safe code |
| Model | Purpose | Size | Framework |
|---|---|---|---|
| SmolLM2-360M | Text generation (chat) | ~300 MB | llama.cpp |
| SmolVLM-256M | Image understanding | ~200 MB | ONNX |
| Whisper | Speech recognition | ~140 MB | ONNX (optional) |
All models are:
- ✅ Quantized for mobile (Q8_0 precision)
- ✅ Optimized for Snapdragon processors
- ✅ Downloaded on-demand to save storage
- ✅ Cached locally for quick access
When the app launches, it initializes the RunAnywhere SDK, which manages AI models and inference:
App Start → RunAnywhere Init → Model Registry → Ready for Chat
On first run, users download AI models to their device (~250-300 MB):
User Tap Download → Download starts (shows progress) → Model cached locally → Ready
User chooses from 7 different learning modes based on their needs:
Home Screen → Select Mode (7 options) → Mode selected → Go to Chat
User types question, optionally uploads image, and sends:
Type Question → Optional: Upload Image → Send → Message stored locally
RunAnywhere SDK runs inference using selected mode's system prompt:
Prompt Builder (adds system prompt for mode) → SmolLM2/SmolVLM → Inference
Response is processed, formatted, and displayed in chat:
Raw Response → ResponseParser → Format for UI → Display in Chat → Save History
Conversation continues with full context, all locally stored:
New User Message → Include Chat History → AI Inference → Display Response → Loop
RunAnywhere SDK is the backbone of Algsoch's on-device AI capabilities:
- Model Management: Handles downloading, caching, and updating models
- Inference Engine: Executes models efficiently on mobile hardware
- Multiple Frameworks: Supports llama.cpp, ONNX, and more
- Privacy: All processing happens on your device, zero network calls
- Performance: Optimized models run at sub-second latency
- Model Registration: Models are registered at app startup
- Download Management: SDK handles model downloads with progress tracking
- Inference Calling: Simple API to run inference with custom prompts
- Error Handling: Automatic retry and fallback mechanisms
- Go to: https://github.com/FiscalMindset/algsoch/releases
- Download the APK file (~45 MB)
On your Android device:
- Open Settings
- Go to Apps & Notifications → Advanced
- Select Install unknown apps
- Choose your file manager and Enable
- Open Downloads folder
- Tap the APK file
- Follow installation prompts
- Wait for installation to complete (~1 minute)
- Launch Algsoch
- Tap "Load" to download models (~250 MB)
- Wait for download (2-3 minutes)
- Models are now cached for offline use
- Select your preferred Learning Mode
- Ask any question
- Enjoy instant offline responses!
- Pick Your Mode: Different modes for different needs (direct, explain, notes, etc.)
- Upload Images: Click upload to add diagrams, notes, or equations
- Review History: Swipe left to see chat history (all stored locally)
- No Internet Needed: Everything works completely offline
- Settings: Customize response tone, language, and more
- OS: Android 10 or higher
- Storage: 500 MB free space
- RAM: 1.5 GB minimum
- Processor: ARM64
- OS: Android 12+
- Storage: 1 GB free space
- RAM: 4 GB+
- Processor: Snapdragon 765+
- Network: Fast WiFi for initial model download
- SmolLM2 (text model): ~300 MB
- SmolVLM (vision model): ~200 MB
- Total: ~500 MB (downloaded once, cached)
| Feature | Algsoch | ChatGPT |
|---|---|---|
| Offline | ✅ 100% | ❌ Requires internet |
| Privacy | ✅ On-device | |
| Cost | ✅ Free | |
| Speed | ✅ Sub-second | |
| Uses Data | ✅ No tracking | ❌ Data collection |
| Feature | Algsoch | Traditional Apps |
|---|---|---|
| Offline | ✅ 100% | ❌ Cloud-dependent |
| Learning Modes | ✅ 7 modes | |
| Real-time | ✅ Instant | |
| Privacy | ✅ Complete | |
| Cost | ✅ Free forever | ❌ Monthly fees |
Best for: Quick reference, simple answers
Response type: Concise, straight to the point
Example:
User: "What is photosynthesis?"
AI: "Photosynthesis is the process by which plants convert light into
chemical energy using chlorophyll."
Best for: Well-structured, focused responses
Response type: Clear, organized information
Example:
User: "Explain the water cycle"
AI:
• Evaporation: Water from surface evaporates
• Condensation: Water vapor condenses into clouds
• Precipitation: Water falls as rain/snow
• Collection: Water collects in oceans/lakes
Best for: Deep understanding, detailed breakdowns
Response type: Comprehensive, educational
Example:
User: "Help me understand calculus"
AI: "Calculus is a branch of mathematics that studies change...
[Full detailed explanation with examples]"
Best for: Study preparation, exam review
Response type: Formatted study notes
Example:
User: "Summarize evolution for my exam"
AI:
📌 KEY POINTS:
• Darwin's theory based on natural selection
• Evolution happens over millions of years
• Adaptation is key to survival
🎓 REMEMBER:
• Common ancestors across species
• Variation drives natural selection
Best for: Problem-solving, learning approach
Response type: Guidance, not just answers
Example:
User: "How do I solve this quadratic equation?"
AI: "Here's the approach to follow:
1. Identify a, b, c coefficients
2. Use quadratic formula: x = -b ± √(b²-4ac) / 2a
3. Substitute your values
4. Simplify to get two solutions"
Best for: Better understanding, memorable learning
Response type: Analogies, analogies & real-world examples
Example:
User: "Explain how a computer processor works"
AI: "Think of a CPU like a chef in a kitchen. The recipes
are instructions (code), ingredients are data... [creative analogy]"
Best for: Advanced topics, deep conceptual understanding
Response type: In-depth theory and principles
Example:
User: "Explain quantum mechanics"
AI: "Quantum mechanics is built on several foundational principles:
1. Wave-particle duality
2. Uncertainty principle
3. Superposition...
[Advanced theoretical explanation]"
Everything about your usage stays on your device:
- ✅ All chat messages and history
- ✅ User preferences and settings
- ✅ Downloaded AI models
- ✅ Uploaded images and documents
- ✅ Learning pattern data
Algsoch NEVER:
- ❌ Sends data to any server
- ❌ Collects usage analytics
- ❌ Tracks your conversations
- ❌ Stores your location
- ❌ Requires account/login
- ❌ Shows advertisements
- Chat history encrypted locally using AES-256
- Model files verified with SHA-256 checksums
- All on-device data stored in app's private storage
- Accessible only by Algsoch application
| Metric | Value |
|---|---|
| Total Code | 5,000+ lines |
| Kotlin Files | 50+ files |
| UI Components | 30+ Compose components |
| Learning Modes | 7 different modes |
| AI Models | 3 models (LLM, Vision, Speech) |
| App Size | ~45 MB |
| Model Size | ~500 MB (downloaded) |
| Supported Devices | Android 10+ |
- Language: Kotlin
- UI: Jetpack Compose + Material 3
- Architecture: MVVM + Clean Architecture
- State Management: ViewModel + StateFlow
- Async: Coroutines + Flow
- AI: RunAnywhere SDK v0.16.0-test.39
Algsoch is completely open-source and free. The complete source code is available at:
https://github.com/FiscalMindset/algsoch
Want to contribute or build upon Algsoch?
- Clone:
git clone https://github.com/FiscalMindset/algsoch.git - Build: Run in Android Studio (minimum SDK 29)
- Deploy: Build release APK or sideload debug APK
- Extend: Add new learning modes, improve UI, optimize performance
A: Yes, 100% offline. All AI models run on your device using RunAnywhere SDK. No internet required.
A: App requires ~500 MB for models. Chat history uses minimal storage (compressed text).
A: Completely. All conversations stay on your device. We don't have servers or collect data.
A: Education should be accessible to everyone. Algsoch is free and open-source forever.
A: No, models must be downloaded first (~250-300 MB). This happens only once.
A: Works on Android 10+. Requires at least 1.5 GB RAM. Better performance on 4GB+ RAM.
A: Yes, everything is local. You can clear chats anytime - they're stored on your device only.
A: Currently Android only. iOS version may come in future releases.
A: All chats are stored in the app. There's no account system — just local storage.
- 📥 Download: https://github.com/FiscalMindset/algsoch/releases
- 📖 Documentation: https://github.com/FiscalMindset/algsoch/blob/main/README.md
- 🐛 Report Issues: https://github.com/FiscalMindset/algsoch/issues
- 💬 Discuss: https://github.com/FiscalMindset/algsoch/discussions
Vicky Kumar (@algsoch)
AI Engineer & Creator
Passionate about building privacy-first, intelligent learning tools
Algsoch is open-source and released under a permissive license.
Powered by: RunAnywhere SDK
Made with ❤️ for learners everywhere.
Learn Smarter. Not Harder. 🧠
Version 1.0.0 | March 2026