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]: Click "SCAN" to analyze module components.
[+] SCAN MODULE: NEURAL_CORE (AI & ML)
STATUS: OPTIMIZED
[+] SCAN MODULE: DEPLOYMENT_GRID (Cloud & DevOps)
STATUS: SCALABLE
[+] SCAN MODULE: BACKEND_MAINFRAME (Server-Side)
STATUS: HIGH_THROUGHPUT
[+] SCAN MODULE: VISUAL_INTERFACE (Frontend)
STATUS: RESPONSIVE
| PROJECT ID | MISSION BRIEF | CORE TECH |
|---|---|---|
| 📦 imgshape |
[CLI-TOOL] Intelligent dataset analysis framework. Auto-generates reports & exports pipelines.
(>4.5k Downloads) |
Python PyPI Analysis
|
| 🏢 FastFare |
[SaaS] AI-Logistics assistant. Automated RAG pipeline with vector search for real-time queries.
|
RAG Next.js FastAPI
|
| 🏭 TTGv1 |
[ENTERPRISE] Scalable scheduling engine solving complex constraint satisfaction problems.
|
Docker OR-Tools Redis
|
| 🔬 MedMNIST-Edge |
[RESEARCH] (Under Review) Medical model compression via Knowledge Distillation for mobile edge.
|
EdgeAI Vision Distillation
|
- MedMNIST-EdgeAI: Compressing Medical Imaging Models for Efficient Edge Deployment
- LCM vs. LLM + RAG
- Edge-LLM: Running Qwen2.5–3B on the Edge with Quantization