Workloads

Projects, experiments, and field work.

Chronological record of projects, experiments, and milestones. Featured work highlights public-safe systems and learning tracks that best represent my current direction. Some private, client, internship, or sensitive work is intentionally summarized only.

Featured work

4 projects

tools // systems

AEGISACTIVE

Apr 2026

Portable Windows Diagnostic Framework

Stateless forensic system diagnostics — no install, no footprint, no trust

The Problem

Diagnosing Windows system health in field environments requires installing tools that may not be permitted, may not exist, or may themselves be compromised.

The Solution

Stateless PowerShell diagnostic framework. Captures baseline snapshots, compares drift, flags anomalies. Single-file. No install. No registry writes. No external dependencies. Runs on any Windows system.

Result

v1.0 released. Validated on external environments. Used as a diagnostic tool during internship work.

PowerShell 5.1+Windows WMICIMBaseline diffing

automation // systems

Mr. RobotoMAINTAINED

Jan 2026 → Jul 2026

Portable Media Downloader

Portable media download utility powered by yt-dlp and FFmpeg.

The Problem

Downloading YouTube videos and other media had become tiring. I kept jumping from website to website just to find something that worked. As a cybersecurity student, I also had to pay attention to domain names, redirects, ads, and other risks on shady download sites. Some sites were full of ads, some looked unsafe, and some locked file formats or longer videos behind paywalls. At some point, the process stopped feeling worth the hassle. I wanted a safer, repeatable, terminal-based workflow I could control.

The Solution

I started building Mr. Roboto as an AI-assisted utility, inspired by Mr. Robot. The "robot" idea fit the goal: a small tool that could handle repetitive media-download work from the terminal. Mr. Roboto is a portable media downloader powered by yt-dlp and FFmpeg. Windows is the stable platform, supported through Batch and PowerShell launchers. Linux support is available in beta through a native Bash launcher, roboto.sh. The project focuses on making media downloads more repeatable, reducing reliance on random download websites, and giving users a clearer local workflow with logs, dependency checks, and platform-specific launchers.

Result

Mr. Roboto is in active personal use and has evolved from a Windows-first utility into a maintained portable downloader. The current project state keeps Windows stable while introducing Linux beta support through a native Bash launcher. The repo now has clearer setup guidance, testing notes, changelog entries, and reporting instructions for Linux users. The project also became a practical exercise in open-source maintenance: reviewing contributor work, limiting risky changes, improving documentation, and turning user feedback into safer platform support.

PowerShellBashBatchyt-dlpFFmpegWindowsLinux beta

ai // exploration

ai-athenaEXPLORING

Apr 2026 → Aug 2026

Local AI Experiments

Experiments with local models, retrieval, tool use, and privacy-aware AI assistants

The Problem

Cloud-hosted AI services raise privacy, cost, and dependency concerns. Exploring what is possible with local inference and retrieval on consumer hardware.

The Solution

Running local language models, building RAG pipelines, and experimenting with agent tool use. Focused on privacy-aware, offline-capable AI workflows.

Result

Ongoing learning and R&D lab track on Athena. Informing how I think about AI-assisted security tooling and local intelligence.

OllamaLM StudioPythonCUDAOpen WebUIRAG

tools // production

vulaiACTIVE

Mar 2026

vulai — LinkedIn Optimization Framework

No-BS open-source prompt framework that kills corporate buzzwords and rebuilds LinkedIn profiles into high-signal, data-driven presence.

The Problem

LinkedIn profiles are saturated with generic corporate language that obscures real skills and repels serious recruiters and collaborators.

The Solution

Open-source prompt framework that systematically rewrites LinkedIn sections — headline, about, experience — into high-signal, data-backed language. No code. No API. Distributed as a usable framework via GitHub.

Result

Full release cycle. Distributed across LinkedIn and WhatsApp groups. Community adoption.

Prompt EngineeringMarkdownGitHub

Full timeline

2024 → now