Gurkirat Singh

A global mouse tracker that hooks low-level Windows APIs to intercept cursor telemetry across all applications. Renders real-time trajectory paths on a live JavaFX dashboard and displays hovered application names. Designed for UX heatmaps and productivity analysis.

Architecture

Windows OS
  │  WH_MOUSE_LL hook (jnativehook)
  ▼
App.java ── Worker thread (40ms pulse)
  │  ├─ Win32: GetForegroundWindow, WindowFromPoint (JNA)
  │  ├─ Weighted-pulse CSV logging (5px deadzone + dwell time)
  │  └─ Platform.runLater → UI updates
  ▼
TrackerUI.java ── JavaFX Application thread
  │  ├─ Live X/Y, active/hovered window titles
  │  ├─ Resizable path-trail canvas (multi-monitor normalized)
  │  └─ Dark dashboard theme
  ▼
logs/mouse_log_<ts>.csv

Key Features

• Weighted-pulse logging — writes CSV rows only on ≥5px movement or window change; accumulates dwell time during idle to minimize disk I/O
• Multi-monitor normalization — computes virtual desktop bounds from all monitors, maps raw hook coords to canvas-relative percentages
• Thread isolation — hook message pump on dedicated worker thread; all UI mutations marshaled via Platform.runLater()
• Resizable live canvas — path trail binds to window dimensions, auto-clears on resize

Debugging Highlights

• Resolved path-trail boundary violations where rendered points drew outside the canvas viewport
• Fixed layout overflow in the stats panel causing clipped window titles at narrow widths
• Added zero-guards and size-change listeners to prevent null-pointer crashes during window resize

A fully local, two-stage AI pipeline that extracts Japanese text from raw comic/manga pages and translates it to natural English. Zero cloud APIs, zero Tesseract, zero manual preprocessing — just raw images in, English dialogue out.

Architecture

images/*.png
  │  preprocess.py: load, downscale, base64-encode
  ▼
ocr.py ── POST /api/chat → Qwen3-VL (vision model)
  │  Returns raw Japanese text from speech bubbles
  ▼
translate.py ── POST /api/chat → TranslateGemma (LLM)
  │  Furigana stripping → contextual English translation
  ▼
outputs/batch_translation.txt

Key Features

• Two-stage local AI — vision model reads the image, separate LLM translates; both run on-device via Ollama
• Zero preprocessing — raw images go direct to the vision model; no grayscale conversion, thresholding, or denoising
• Vertical text support — vision models natively understand right-to-left Japanese reading order
• Crash-resilient batch mode — streaming per-page writes survive mid-batch failures; restart picks up from scratch with no duplicates
• Furigana stripping — regex-based removal of kana reading annotations before translation to prevent tokenization noise
• 563 lines of Python — no framework overhead, single-file modules, trivially auditable

A lightweight Go CLI that auto-detects any project's stack and generates a production-ready Dockerfile — then builds and runs the container with one command. No Docker knowledge required.

Architecture

deploybox init <dir>
  │  Scan: pom.xml→Java, requirements.txt→Python, package.json→Node, ...
  │  Load: deploy.yaml overrides (name, port, env, volumes, command)
  │  Generate: multi-stage Dockerfile with layer caching
  ▼
Dockerfile

deploybox up <dir>
  │  docker build -t deploybox-<name>
  │  docker run -d --rm (or docker compose up for depends_on)
  ▼
Container running ✓

Key Features

• Auto-detection — scans directory for pom.xml, requirements.txt, package.json, Cargo.toml, or go.mod and picks the right base image
• Multi-stage builds — every Dockerfile uses layer caching; dependencies installed before source so code changes skip full rebuilds
• Optional deploy.yaml — override name, port, environment variables, volume mounts, and startup command without touching the Dockerfile
• Multi-service support — depends_on in deploy.yaml generates docker-compose.yml to wire containers together
• host.docker.internal — containers reach host services (Ollama, databases) through Docker Desktop's built-in DNS
• Cross-project consistency — all generated Dockerfiles follow identical conventions; audit one, trust all

Verified Against

• OCR Translate (Python) — built image, ran batch translation, connected to Windows Ollama via host.docker.internal
• MouseFlow Tracker (Java/Maven) — built multi-stage image, resolved JNA + JavaFX + JNativeHook, compiled successfully