class Hill_Patel(AI_Architect):
"""
[INFO] Architecting bridge between Research and Production.
[WARN] High compute requirements detected.
"""
def __init__(self):
self.code = "STiFLeR7"
self.specs = {
"role": "AI Engineer & Full-Stack Architect",
"focus": ["LLMs", "RAG Systems", "Edge AI", "Quantization"],
"driver": "Deploying Scalable Intelligence"
}
def execute_mission(self):
while True:
self.research()
self.optimize()
self.deploy("Production")[SYSTEM MESSAGE]: ALL SYSTEMS OPERATIONAL. SKILLS DEPLOYED.
| PROJECT ID | MISSION BRIEF | CORE TECH |
|---|---|---|
| β‘ DevPulseAIv2 | [DEV-TOOL] Advanced AI assistant for developer productivity and workflow optimization. |
AI Agents Python LLM |
| π¦ imgshape | [CLI-TOOL] Intelligent dataset analysis framework. Auto-generates reports & pipelines. |
Python PyPI Analysis |
| π± Qwen3-iOS | [MOBILE-AI] On-device inference of Qwen3 models optimized for iOS architecture. |
Swift CoreML Quantization |
| π¦Ύ SpecCraft-AI | [PLATFORM] Analyst-Grade AI Spec Generation Platform with specialized UI. |
Next.js AI-Agents RAG |
| π TTGv1-Docker | [ENTERPRISE] Scalable scheduling engine solving complex constraint problems. |
Docker OR-Tools Redis |
| π FastFare-v1-GCP | [SaaS] AI-Logistics assistant. Automated RAG pipeline with vector search. |
GCP RAG FastAPI |
- MedMNIST-EdgeAI: Compressing Medical Imaging Models for Efficient Edge Deployment
- LCM vs. LLM + RAG
- Edge-LLM: Running Qwen2.5β3B on the Edge with Quantization
