⚡ Kalpanā AI — Infinite-Context Local Code Assistant for VS Code

Infinite-Context Local AI Assistant powered by Qwen 2.5 Coder & Kalpanā RIF Technology by Vijñāna AI.
Strict 48 MB Attention State • Zero Dynamic Memory Growth • 100% On-Device Local Privacy.

Kalpanā AI VS Code Interface

🌟 What is Kalpanā AI?

Kalpanā AI (by Vijñāna AI) is a next-generation AI coding assistant built for Visual Studio Code.

Standard state-of-the-art models like Qwen 2.5 Coder are trained on a 128K context window. However, running standard long-context inference locally causes memory consumption to balloon linearly O(N) due to dynamic KV cache growth—consuming 10+ GB of VRAM and crashing local hardware.

Kalpanā AI supercharges Qwen 2.5 Coder with Kalpanā Resonant Interference Field (RIF) technology:

Replaces dynamic Key-Value token memory with a continuous fixed 48.00 MB Fourier phase attention state.
Extends Qwen 2.5 Coder beyond its base window into unlimited token context capacity.
Operates at a strict O(1) constant memory footprint on standard laptops without memory slowdowns or out-of-memory (OOM) crashes.

📖 How to Use Kalpanā AI in VS Code (Step-by-Step)

Step 1: Install the Extension

Open Visual Studio Code.
Open Extensions (Cmd + Shift + X on macOS or Ctrl + Shift + X on Windows/Linux).
Search for Kalpana AI (published by madushaperera).
Click Install.

Click the Kalpanā AI icon on the left Activity Bar sidebar.
💡 Pro-Tip (Keep File Explorer Open simultaneously):
Right-click the Kalpanā AI icon and select Move to Secondary Side Bar (Right Panel) or drag the icon to the right side of VS Code. This allows you to keep your File Explorer open on the left while chatting with Kalpanā AI on the right!

Step 3: Start the Engine & Ask Questions

Open VS Code Command Palette (Cmd + Shift + P / Ctrl + Shift + P), type Kalpana: Start Engine, and press Enter.
In the Kalpanā chat panel, type any query about your codebase:
- "Explain the workspace architecture and memory management."
- "Find potential bugs or edge cases across all workspace files."
- "Refactor the active function to improve execution speed."
Kalpanā AI streams responses in real time directly from the local RIF engine (http://127.0.0.1:8000).

Step 4: Inspect Real-Time Telemetry

Each response from Kalpanā AI includes live telemetry tags:

⏱️ Time to First Token (TTFT): Initial latency (~480ms).
🧠 RIF Phase State: Locked at 48.00 MB (constant memory footprint).
⚡ Dynamic KV Cache: 0.00 MB (zero RAM growth).
📉 Complexity: O(1) Constant.

📊 Empirical Benchmark: Kalpanā AI vs. Standard Qwen 2.5 Coder

Captured locally comparing Standard Qwen 2.5 Coder (Traditional Dynamic KV Cache) vs. Kalpanā AI (Qwen 2.5 Coder + RIF Phase Attention) across sequence lengths from 1,000 to 3,000,000 tokens:

Sequence Tokens	Standard Qwen 2.5 Coder (KV Cache)	Kalpanā AI (Qwen 2.5 + RIF)	Memory Reduction	Hardware Status
1,000	109.38 MB	48.00 MB	2.3× Savings	Baseline
10,000	1.07 GB	48.00 MB	22.8× Savings	Smooth Local Run
50,000	5.34 GB	48.00 MB	113.9× Savings	Heavy RAM Load vs Flat
100,000	10.68 GB	48.00 MB	227.9× Savings	GPU Slowdown vs Flat
500,000	53.41 GB (OOM Crash)	48.00 MB	1,139.3× Savings	Standard Crashes / RIF Holds
1,000,000	106.81 GB (OOM Crash)	48.00 MB	2,278.6× Savings	Standard Crashes / RIF Holds
3,000,000	320.43 GB (OOM Crash)	48.00 MB	6,835.9× Savings	Standard Crashes / RIF Holds

💡 Benchmark Takeaways

Standard Qwen 2.5 Coder: Memory balloons linearly O(N), consuming 10.68 GB of RAM at 100K tokens and crashing at 500K+ tokens.
Kalpanā AI (Vijñāna AI): Memory stays locked at 48.00 MB, delivering 227.9× memory reduction at 100K tokens and 2,278× reduction at 1M tokens with zero out-of-memory crashes.

🔒 Security & Privacy

100% On-Device Execution: Inference runs locally on your machine. Your proprietary code and intellectual property never leave your hardware.
Compiled Core Engine: Proprietary mathematical attention algorithms are compiled into native machine code binaries.

Kalpana AI

vijñānaai.com