Building Real-World AI Systems

I'm Giancarlo Vizhnay, a software engineer working on AI agents, local-first AI, automation, backend infrastructure, and scientific research tools.

My goal is to build systems that go beyond demonstrations and can eventually operate reliably in real-world environments.

What I'm Working On

Aegis Vision — Computer Vision and Spatial Intelligence

Aegis Vision explores real-time object detection, tracking, spatial measurement, camera calibration, and interactive 3D visualization.

The challenge is connecting computer vision with reliable real-world measurements.

Neuroscience Evidence Lab — Scientific Research Verification

This project focuses on retrieving scientific literature, validating research metadata, and checking identifiers such as DOI and PMID.

I'm exploring how automated research systems can distinguish verified scientific records from mismatched or unreliable references.

SerpentOS — Quantitative Decision Engineering

SerpentOS explores deterministic decision-making, API-based analysis, automated evaluation, and system reliability.

Local-First AI — Running Models on Personal Hardware

I'm also experimenting with local language models, agent orchestration, and model optimization.

The objective is to understand how much AI capability can run locally while maintaining control over infrastructure, execution, and data.

My Engineering Philosophy

I primarily work with Python, TypeScript, FastAPI, React, PostgreSQL, and related infrastructure tools.

My priorities are:

• Reliable and maintainable architecture
• Reproducible experiments
• Security and controlled execution
• Measurable performance
• Automated testing
• Practical deployment

I believe building reliable AI software requires more than connecting an application to a language model.

What I'll Share Here

I'll document engineering decisions, technical experiments, architecture, debugging, performance results, and lessons from building these projects.

Some experiments will succeed. Others will reveal limitations worth understanding.

My intention is to share the actual engineering process.

You can follow my projects on GitHub.