This is a submission for the GitHub Copilot CLI Challenge
## What I Built
I built HakoNyans, an experimental image codec focused on practical decode speed and transparent lossless results
across different image types (photo, anime, UI/screen).
For this challenge, I focused on two things:
- A clearer lossless workflow in CLI
- Added a new command:
hakonyans encode-lossless <in.ppm> <out.hkn> [preset: fast|balanced|max]This makes lossless testing reproducible from the terminal without custom scripts.
Reproducible visual and metric snapshots
Prepared a dedicated asset pack for challenge demos:
docs/assets/devchallenge_2026_01_21/Included side-by-side comparisons and both win and lose cases (to keep reporting honest).
What this project means to me:
- I wanted to show not just “best-case screenshots,” but a realistic engineering snapshot:
- where HKN already wins (some natural-photo cases), and where PNG is still much stronger (some structured/UI-like cases).
## Demo
- Repository:
Challenge asset pack:
https://github.com/hakorune/HakoNyans/tree/main/docs/assets/devchallenge_2026_01_21
### Screenshots
Main comparison
Main comparison metrics (Artoria, lossless)
- Source PNG (Artoria):
17,669,320 bytes - Env:
HAKONYANS_THREADS=1 - Command:
./build/hakonyans encode-lossless <in.ppm> <out.hkn> <preset> - Encode time: 2-run median wall time
| Preset | HKN bytes | PNG bytes | PNG/HKN | Encode time |
| balanced | 17,374,550 | 17,669,320 | 1.0170 | 2.94 s |
| max | 16,767,516 | 17,669,320 | 1.0538 | 101.18 s |
PNG/HKN > 1.0 means HKN is smaller.
Lossless win case (nature_01)
Lossless lose case (hd_01)
### Example metrics snapshot (fixed6, max preset)
-
nature_01: HKN 812,567 bytes vs PNG 1,281,481 bytes (PNG/HKN = 1.577) -
nature_02: HKN 999,685 bytes vs PNG 1,446,470 bytes (PNG/HKN = 1.447) -
hd_01: HKN 699,858 bytes vs PNG 8,785 bytes (PNG/HKN = 0.0126)
So currently:
- HKN can beat PNG on some photo-like content.
- PNG still dominates some structured/worst-case images.
- The project is actively improving both sides.
## My Experience with GitHub Copilot CLI
GitHub Copilot CLI was most useful for my fast implementation loop:
- Refactoring large headers into smaller units safely
- Adding a new CLI command (
encode-lossless) while preserving existing behavior - Running repetitive verify loops quickly (
build,ctest, benchmark checks) - Generating and validating challenge-ready visual assets from command pipelines
The biggest impact was speed + consistency.
I could iterate quickly in terminal-first workflows while keeping changes verifiable (tests, checksums, RMSE checks,
benchmark CSVs).
Top comments (1)
Development continues even after the challenge ends.
We beat PNG!
We're still losing to webp.
Webp is strong!