class Hill_Patel(AI_Researcher):
"""
[INFO] Building open-source AI tools for the community.
[WARN] High compute requirements detected.
"""
def __init__(self):
self.code = "STiFLeR7"
self.specs = {
"role": "AI Researcher & Developer | OSS Contributor",
"focus": ["LLMs", "RAG Systems", "Edge AI", "Quantization"],
"driver": "Deploying Scalable Intelligence"
}
def execute_mission(self):
while True:
self.research()
self.optimize()
self.deploy("Production")![]() |
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|---|---|---|---|---|
| Pull Shark | YOLO | Quickdraw | Pair Extraordinaire | Galaxy Brain |
| Mass PR Merger | Merged without review | Closed issue/PR quickly | Co-authored commits | Answered discussions |
- Published Research: Transforming Urban Solutions for Smart Cities through Crowdsourced Feedback (Mar 2025)
- MCP Mastery: Model Context Protocol - Fractal Analytics
- Professional Certificate: RAG and Agentic AI - Coursera
- Course Completion: Introduction to Neural Networks with PyTorch - Coursera
| PROJECT ID | MISSION BRIEF | CORE TECH |
|---|---|---|
| โก DevPulseAIv2 | [DEV-TOOL] Advanced AI assistant for developer productivity and workflow optimization. |
AI Agents Python LLM |
| ๐ฆ imgshape | [CLI-TOOL] Intelligent dataset analysis framework. Auto-generates reports & pipelines. |
Python PyPI Analysis |
| ๐ฑ Qwen3-iOS | [MOBILE-AI] On-device inference of Qwen3 models optimized for iOS architecture. |
Swift CoreML Quantization |
| ๐ง agentic-rag | [RAG-SYSTEM] Production-grade Agentic RAG for consumer GPUs (RTX 3050/6GB). Graph-based, controllable architecture. |
LangGraph RAG Agents |
| ๐ค Dex | [AI-ASSISTANT] Production-grade personal AI assistant built on MCP. Persistent, memory-aware. |
MCP Python LLM |
| ๐ Antigravity | [TOOL-SERVER] Universal LLM Tool Server - Connect Claude, OpenAI, and Gemini to your workspace. |
MCP LLM Tools |
- Most Vision AI Systems Stop at DetectionโโโI Built One That Takes Action
- Most RAG Systems Fail QuietlyโโโHereโs How I Built a 98%-Accurate Agent on a 6GB GPU
- MedMNIST-EdgeAI: Compressing Medical Imaging Models for Efficient Edge Deployment
- LCM vs. LLM + RAG
- Edge-LLM: Running Qwen2.5โ3B on the Edge with Quantization






