Chonky Cat 🐱

메모리 뚱냥이 — an OpenAI Build Week project

Your disk usage, visualized as a cat that gets chonkier as your drive fills up.

Chonky Cat is a desktop pet for macOS, with a lightweight Windows version. It turns an invisible system metric into something you can understand at a glance: the fuller your drive gets, the rounder your cat becomes.

The killer demo feature makes that cat personal. Give Chonky Cat one photo of your pet, and gpt-image-2 creates a six-stage chonk progression that the app automatically converts into a custom 40-frame desktop theme.

Screenshots

The default cat gets rounder as disk usage rises	Built-in themes give each chonk a different style
Disk, RAM, top memory apps, themes, size, and settings on right-click

Highlights

• One pet photo → one custom animated theme: gpt-image-2 preserves your pet's distinctive colors, markings, face, and ears while generating a six-stage horizontal sprite sheet. Chonky Cat segments it and builds the full 40-frame theme automatically.
• The complete chonk chart: disk usage moves your cat through A fine boi → He chomnk → A heckin' chonker → HEFTYCHONK → MEGACHONKER → OH LAWD HE COMIN.
• “🐾 Feeling full?” diagnosis: GPT-5.6 (gpt-5.6-luna) explains why the computer feels slow, recommends safe cleanup targets, estimates reclaimable space, and gives one concise piece of advice. The Korean menu label is “🐾 배불러?”.
• Safety-first cleanup: only allowlisted browser caches, Trash contents, downloads older than 30 days, and Xcode DerivedData can be suggested. Every item requires confirmation and is moved through macOS Trash—never permanently deleted.
• A cat with a personality: choose a sassy, warm, or stoic voice, or describe a custom personality in natural language. The selected voice shapes the diagnosis.
• English and Korean: macOS language is detected automatically, with a manual language override in the context menu.
• Useful at a glance: disk, RAM, swap, and top memory-consuming apps appear in the right-click menu. The cat can be dragged, resized, and rethemed.

Runtime models

• Performance diagnosis: GPT-5.6 (gpt-5.6-luna)
• Custom pet theme generation: gpt-image-2

Built-in themes

Theme	Description
Cute	A soft 3D-toy cat whose eyes get sleepier as it gets rounder
Simple	A clean, minimal illustrated chonk
Madness	A sparkly, wide-eyed chibi cat
Wake-up call	An intentionally derpy reminder to check your drive

Install on macOS

git clone https://github.com/hyeonheebee/memory-cat.git
cd memory-cat
./install_mac.command        # You can also double-click this file

The installer requires Python 3.9 or later. It creates .venv, installs the dependencies, launches Chonky Cat, and configures it to start at login.

To remove it, run:

./uninstall_mac.command

Store OPENAI_API_KEY in a .env file at the project root to enable GPT-5.6 diagnosis and custom pet theme generation.

Install on Windows

See windows/README.txt for the full instructions. In short:

pip install pyside6 psutil
pythonw windows\windows_cat.pyw

To build a standalone executable, run windows\build_exe.bat after installing its listed build dependencies.

Make your own theme 🎨

From one pet photo with gpt-image-2

On macOS, right-click the cat and choose Make a theme from my pet…. After you select a photo and approve sending it to OpenAI, the app generates, imports, and immediately applies the new theme in a background thread.

The same pipeline is available from the command line:

# Install the image-processing dependencies in the project virtual environment
./.venv/bin/python -m pip install -r requirements-dev.txt

# Keep OPENAI_API_KEY in the project root .env file
./.venv/bin/python vision_theme.py my-pet.jpg my-pet --quality medium

vision_theme.py asks gpt-image-2 for six clearly separated versions of the same pet, from slim to extremely round. It then reuses the existing import pipeline to produce frames/my-pet/cat_00.png through cat_39.png, plus preview and raw debug images.

From an existing sprite sheet

If you already have a horizontal image with multiple stages from slim to round, import it directly:

./.venv/bin/python import_theme.py my-sprite-sheet.png my-theme

Themes under frames/<name>/ are discovered automatically. Reopen the right-click Theme menu to select a newly imported theme. Code-generated built-in themes can be rebuilt with python generate_frames.py.

Project structure

desktop_cat.py      macOS desktop app and menus (PyObjC)
brain.py            GPT-5.6 performance diagnosis and safe Trash workflow
personality.py      personality presets and custom prompt compiler
i18n.py             English/Korean UI strings and chonk-stage names
metrics.py          shared disk and memory measurements
vision_theme.py     one pet photo -> gpt-image-2 custom theme
import_theme.py     sprite sheet segmentation and frame generation
generate_frames.py  built-in theme generator
windows/            lightweight Windows app (PySide6)
frames/<theme>/     generated PNG frames for each theme
tests/              mocked, regression, and optional live API tests

Developer demo and test overrides

• MEMORY_CAT_CONFIG=demo_config.json: use a separate config file for demos or tests so personal settings are not read or modified.
• MEMORY_CAT_DEMO_DISK_PERCENT=92: replace measured disk usage with a demo or test value, clamped to 0–100; displayed and diagnostic values stay consistent.

Example:

MEMORY_CAT_CONFIG=demo_config.json \
MEMORY_CAT_DEMO_DISK_PERCENT=92 \
.venv/bin/python desktop_cat.py

How I collaborated with Codex

All application code in this project was written by Codex (GPT-5.6-Codex) in a single continuous session in the ChatGPT desktop app, working directly on this repository. My workflow for every feature:

1. Spec first — I wrote a detailed spec for each module (goals, design decisions, safety constraints, test requirements, done criteria) and handed it to Codex as one prompt.
2. Codex implements — Codex wrote the code, tests, and commits: the GPT-5.6 diagnosis engine (brain.py), the safety-first trash pipeline (safe_trash with an allowlist + macOS Trash only), i18n, the personality system, and the killer feature — vision_theme.py, which turns one photo of your pet into a 40-frame chonk-progression theme via gpt-image-2.
3. Verify against the real API — mocked tests all passed, but my review partner (Claude, which I used for planning, code review, and demo prep — never for the code itself) ran a live API call and caught a real bug: gpt-image-2 rejects the response_format parameter. I reported it back to Codex with the error, Codex verified it against the API reference and shipped the fix with a regression test (assertNotIn("response_format", kwargs)).

Models used at runtime: GPT-5.6 (gpt-5.6-luna) powers the cat's personality-driven performance diagnosis; gpt-image-2 generates the custom pet sprite sheets.

Author and license

Built by Hyeonhee Shim (@hyeonheebee). Code by Codex; planning, review, and demo by Claude. MIT License.