AI Engineering
Practical AI projects, experiments, tutorials, and applied research.
System 1 vs LLM: Does Every AI Decision Really Need a Large Language Model?
Does every AI decision need the power of a general-purpose LLM? We tested Jev 1.13 as a System 1 decision model against an OpenAI LLM for a banking model-routing problem. The experiment revealed substantially lower observed…
Build an AI-Powered Glacier Monitoring System: What Satellites Revealed Before the 2026 Langtang Lirung Collapse
Can AI see a glacier disaster coming?
Satellite and climate observations before the 2026 Langtang Lirung event revealed exceptionally warm, melt-favourable conditions—but an unsupervised AI model did not find a unique monthly warning signal.
That “negative” result teaches…
Build an AI-Powered Financial Digital Twin: Simulate a $100 Billion Bank with Python
What happens when a bank’s cloud region fails?
It may not remain an IT problem.
Cloud failure → payment disruption → customer impact → deposit withdrawals → liquidity stress.
I explored how Python, NetworkX, SimPy, Monte Carlo simulation, financial…
Build an AI Signature Verification and Fraud Detection System
What if a signature matches perfectly because a fraudster copied the genuine signature?
That’s why signature verification needs more than one neural network.
This project combines:
YOLO detection → Siamese embeddings → structural analysis → document forensics → evidence…
AI Signature Extraction and Manual-Signing Indicator Analysis
Can AI tell whether a document contains a handwritten signature?
Finding the signature is only the beginning.
In this project, we combine YOLO, Segment Anything, OpenCV, feature extraction, quality gates, and human review to build an AI-assisted signature…
Build an AI Deepfake Image Detection System: A Beginner’s Guide to Multi-Evidence Digital Forensics
Learn how to build an AI-powered deepfake image detection system that combines computer vision, image forensics, metadata analysis, and explainable AI to detect manipulated images with confidence.
AI Engineering brings together practical AI projects, experiments, tutorials, and applied research notes on building real-world AI systems. The goal is to make AI engineering easier to understand through concrete examples, beginner-friendly explanations, and practical implementation notes.