🕉️ Sanskritam Programming Language (SPL)

A Semantic-First, Order-Independent, AI-Native Programming Language

Sanskritam Programming Language (SPL) is a next-generation general-purpose programming language inspired by Sanskrit linguistic principles, where meaning is independent of token order. SPL is designed to be human-friendly, AI-native, and high-performance, combining semantic reasoning with modern compiler technology.

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📜 Table of Contents

• 🚀 Key Features
  • 🧠 Semantic-First Design
  • 🔀 Order-Independent Execution
  • 🌐 Dual Script Support
  • 🤖 AI-Native Language
  • ⚡ High Performance
• 📐 High-Level Architecture
• 🧩 Example Code
  • 🔹 Arithmetic Function (Order-Independent)
  • 🤖 AI-Native Transformer Definition
  • 🏃 Training Loop
• 📊 Research Highlights
• 🧪 Current Status
• 🛠️ Roadmap
• 👤 Author
• 🌌 Vision
• ⭐ Contributing

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🚀 Key Features

🧠 Semantic-First Design

• Program meaning is independent of syntax order
• Execution is driven by semantic dependency graphs
• Inspired by Sanskrit Karaka (semantic role) theory

🔀 Order-Independent Execution

• Tokens, clauses, and statements can be reordered
• Compiler resolves execution order dynamically
• Improves robustness and reasoning for AI systems

🌐 Dual Script Support

• Write code in:
  • Devanagari script
  • Roman (Latin) script
• Both scripts compile to the same semantic representation

🤖 AI-Native Language

• First-class support for:
  • Tensors
  • Attention
  • Transformers
  • Training & inference pipelines
• Tokenization optimized for Transformer models
• Reduced token entropy and positional dependency

⚡ High Performance

• Core implemented in C/C++
• LLVM-based backend
• Near C++ runtime performance

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📐 High-Level Architecture

SPL Source Code (Devanagari / Roman)
↓
Multi-Script Lexer
↓
Semantic Normalization Engine
↓
Semantic Dependency Graph (IR)
↓
C/C++ / LLVM Backend
↓
Executable / AI Runtime

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🧩 Example Code

🔹 Arithmetic Function (Order-Independent)

Same meaning, different order:

कार्य जोड़
इनपुट a b
प्रतिफल a + b

प्रतिफल a + b
इनपुट b a
कार्य जोड़

Both compile to the same semantic graph.

🤖 AI-Native Transformer Definition

मॉडल भाषा_मॉडल
परत Embedding
परत Transformer × 12
परत Linear
परत Softmax

🏃 Training Loop

प्रशिक्षण भाषा_मॉडल
डेटा पाठ_संग्रह
हानि CrossEntropy
अनुकूलक Adam
युग 10

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📊 Research Highlights

Feature	Traditional Languages	SPL
Order-Independent Semantics	❌	✅
AI-Native Constructs	❌	✅
Low Token Entropy	❌	✅
Semantic Graph Execution	❌	✅
Dual Script Support	❌	✅

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🧪 Current Status

• ✅ Language design finalized
• ✅ Formal grammar (EBNF)
• ✅ Semantic execution model
• 🔧 Compiler prototype (in progress)
• 🔧 LLVM backend (planned)

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🛠️ Roadmap

• SPL Interpreter (C++ / LLVM)
• IDE & LSP support
• Formal verification engine
• Neural IR backend
• Quantum & neuromorphic extensions
• Open-source community release

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👤 Author

Devanand Yadav Founder & Researcher, BrahmandX Innovations

• Email: devanand@brahmandx.com
• GitHub: git-devanand
• LinkedIn: devanandyadav

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🌌 Vision

From syntax-driven code to meaning-driven computation.

SPL aims to become a universal semantic programming layer for:

• Artificial Intelligence
• Autonomous systems
• Robotics
• Scientific computing
• Space exploration

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⭐ Contributing

Contributions, discussions, and research collaborations are welcome. Please open an issue or submit a pull request.

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