DREZ
DREZ

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PROJECTSCONTACT
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A LITTLE ABOUT ME

Yo, I’m Drez. I build slick websites, automate boring stuff, and break things (for science). My passion lies in creating seamless digital experiences and solving complex problems with clean, efficient code.

PROJECT: NETTRACK

A deep dive into my flagship server security platform.

NetTrack dashboard
Visit Live Site
What it is

NetTrack is a Linux-first server security platform designed to act like the ultimate guard dog for your servers. It prevents DDoS attacks, quarantines malware, and traces the origin of attacks — all while running locally on your servers.

How it works
  • Local Deployment: Runs fully on client servers — no cloud dependency for core protection.
  • AI Traffic Analysis: Learns normal server traffic patterns to spot zero-day anomalies instantly.
  • Automated Incident Response: Suspicious files are quarantined automatically and alerts are sent to your dashboard.
  • Cloud-Managed Updates (Optional): Our encrypted cloud infrastructure pushes updates and patches, so all servers stay up-to-date and secure.
  • Compliance-Ready: Modes for HIPAA, GDPR, and financial standards.
  • Anti-Piracy & Security: Impossible to decompile or bypass — your servers stay protected.
Why it’s different / better
  • Local-first design keeps control in the hands of clients.
  • AI-driven detection goes beyond simple signature-based protection.
  • Modular pricing for everyone from homelabbers to enterprises.
  • Cloud servers let our team update, monitor, and patch clients without exposing sensitive data.
Target Users
  • Homelab enthusiasts
  • Indie developers and small gaming servers
  • SaaS, e-commerce, and mid-size businesses
  • Large enterprises needing multi-server security
Why it matters
  • DDoS and malware attacks are exploding — businesses need fast, reliable, and local protection.
  • Most solutions rely entirely on the cloud, creating blind spots or latency — NetTrack fixes that.
  • Investors get in early on a product that scales from small personal servers to enterprise deployments.

PROJECT: DYNAMIC DOCK

A deep dive into my macOS Dynamic Island-style utility.

Dynamic Dock screenshot
View on GitHub
What it is

Dynamic Dock is a macOS app inspired by the iPhone Dynamic Island. It sits under the camera notch and provides live notifications for battery, screen recording, AirPods connection, volume changes, and supports plugins for third-party widgets.

Features
  • Dynamic Dock Pill: Smooth animations for notifications.
  • Battery & Charging Detection: Shows charging status and battery percentage.
  • Screen Recording Detection: Real-time detection using ScreenCaptureKit.
  • CoreAudio Integration: Detects volume changes and AirPods connections.
  • Plugin System: Extend functionality with JS plugins located in ~/Library/Application Support/DynamicDock/Plugins/.
  • Album Art: Displays album art for now playing tracks (Spotify/Music).
  • Single-file Swift Implementation: Easy to compile and run.
Plugins
  • batteryWarning.js – Warns when battery < 20%
  • recordingNotifier.js – Pops pill when screen recording starts
  • funPill.js – Random funny messages every few minutes
Installation

1️⃣ Easiest: Download Prebuilt App

  • Go to the Releases page on GitHub
  • Download the latest .app file
  • Move it to /Applications
  • Open it — you might need to right-click → Open the first time because of Gatekeeper
  • Done ✅

2️⃣ Build It Yourself (Dev Mode)

  • Clone the repo: git clone https://github.com/Benji-Bitter/Dynamic-Dock.git
  • cd DynamicDock
  • Open in Xcode: open DynamicDock.xcodeproj
  • Build and run (⌘ + R in Xcode)
  • Or from the terminal: xcodebuild -project DynamicDock.xcodeproj -scheme DynamicDock -configuration Release
  • Once built, your .app will be in ./build/Release/ — move it to /Applications manually.
Tags / Keywords
macOS
Dynamic Dock
Dynamic Island
SwiftUI
Plugins
ScreenCaptureKit
CoreAudio
Battery
AirPods
Volume
Notification Pill

PROJECT: EASYQUANTUM

A quantum-inspired engine for classical hardware.

EasyQuantum abstract visualization
View Project
What is EasyQuantum?

Think of EasyQuantum like a super-fast “brain booster” for your computer. It makes your normal PC act a bit like a quantum computer by using smart tricks: doing lots of things at once (parallelism), using your graphics card (GPU) for math, and running math in clever ways inspired by quantum ideas.

Real quantum computers can explore many possibilities at the same time (called superposition) and then “interfere” to make good answers show up stronger. EasyQuantum copies the vibe of that using normal hardware by splitting big problems into millions of tiny math pieces, crunching them in parallel on all CPU cores and the GPU, and using special math “patterns” (like interference and amplification) to make the right answers stand out.

What It's NOT

It is not a real quantum computer. It simulates quantum-like behaviors on a classic computer. That means it won’t break encryption or do true quantum magic, but it gets you many of the “parallel search and combine” benefits fast.

How it Works (Under the Hood)

Superposition (copycat)

It treats possibilities as numbers in a big list (tensors) and normalizes them so they behave like 'amplitudes'.

Interference

It shifts the 'phase' of amplitudes, then combines them so helpful paths add up and unhelpful paths cancel out.

Amplitude Amplification

A strategy (inspired by Grover’s algorithm) that boosts scores of promising options over a few rounds.

Auto-Precision

It changes number accuracy on the fly: high precision (float64) on CPU for sensitive math, lower precision (float16/float32) on GPU for speed.

Smart Scheduling

It picks CPU or GPU depending on the job size and what your machine supports.

Caching & Hot Kernels

Reuses memory buffers to avoid re-allocating giant arrays and uses optional C++ pieces (via pybind11) for very fast inner loops.

How to Use It (Examples)

Show help:

dist/easyq.exe --help
easyq --help

Quick math:

easyq eval --expr "sin(0.5)+2"

Benchmark CPU vs GPU:

easyq bench --size 20000000

Simple particle sim:

easyq particles --n 1000000

Call the C++ kernel:

easyq kernels
What's Inside the Folders
  • core/quantum_engine.py: Superposition, interference, precision/device control, phase evolution.
  • core/math_solver.py: Amplitude amplification, fast sums, least squares (CPU/GPU).
  • core/ai_core.py: Profiles runs and chooses smarter strategies automatically.
  • core/simulator.py: Particle world with forces, damping, and energy stats.
  • cpp/: Optional C++ speedup kernels bridged to Python.
  • scripts/: Build and packaging helpers (.exe, .deb).
  • cli.py: The command-line tool you can run.
Why It's Fast
  • Uses all CPU cores at once.
  • Uses the GPU (which is built to smash huge arrays of numbers).
  • Runs operations in big batches (vectorization) instead of tiny loops.
  • Avoids slow memory moves and reuses buffers.
  • Adjusts precision for the best speed/accuracy tradeoff.
What It's Good For
  • Heavy number crunching
  • Physics-like simulations
  • Search/optimization
  • AI preprocessing
Why It Matters
  • You don’t need a quantum computer to get quantum-like acceleration patterns.
  • It gives developers and students a powerful playground to “think quantum” using everyday hardware.
Limitations & Future Upgrades
  • Speed depends on your GPU and memory.
  • Some tasks aren’t a good fit for parallel math.
  • Future: more C++ kernels, more solvers (differential equations, PDEs), better auto-tuning, and a nice React dashboard.

SKILLS

React / Next.js

Building modern, fast, and scalable web applications.

Python

Automating tasks, backend services, and data analysis.

Firebase

Leveraging Google's backend services for rapid development.

Node.js

Crafting efficient and powerful server-side logic.

Databases

Working with SQL and NoSQL databases like PostgreSQL and MongoDB.

UI/UX Design

Designing intuitive and visually appealing user interfaces.