Alternatives
Products that do what Tzar, Easy compression and extraction for any compression format does
- 1AI
2017 · unwttng.com
- 2TA
2015 · ruarai.github.io
- 3IM
2019 · github.com
- 4OA
I built an experiment that uses an overfitted transformer and arithmetic coding to compress individual files. Instead of training the model to generalize, I train a 900KB transformer to memorize a single file and predict the next byte. Those predictions are fed into an arithmetic coder to produce the compressed output. On a 100MB NYC taxi CSV, it compresses to about 7MB (~0.5 bits/byte). On a 100MB slice of enwik9, it compresses to about 21MB (~1.68 bits/byte). It's pretty slow right now (roughly 20–30 minutes of training and 45 minutes each for compression and decompression on my…
Jun 2026
- 5TB
2015 · github.com
- 6IM
Jan 2026 · github.com
- 7DR
2018 · dropbox.github.io
- 8

- 9XC
2019 · github.com
- 10ES
2015 · github.com
- 11AB
2019 · github.com
- 12CE
2020 · concise-encoding.org
- 13FI
2015 · github.com
- 14

- 15CD
2017 · excamera.com
- 16MC
2014 · cramcore.github.io
- 17H2
2016 · github.com
- 18UF
May 2026 · github.com
- 19ME
2014 · mozjpeg.codelove.de
- 20CL
2024 · github.com
- 21OS
2015 · stackhut.com
- 22SL
I'd like to share a little toy project of mine, a really simple image codec that can do lossy to full lossless image compression with complete scalability at a byte level granularity - you can compress an image just once, even fully losslessly if needed, and then get any lossy version possible by simply stopping decompression at any offset in the compressed data. This "encode onde, serve many" approach is especially interesting for providing downscaled low quality image previews (LQIP) in as tight a storage budget as possible, and then allowing seamless, transparent refinement as deemed…
2024 · github.com
- 23AA
2014 · tapcelerate.com
- 24EP
2016 · luchenlabs.com
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