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A portable (OS X/Linux/Windows), simple zip library written in C

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A portable (OS X/Linux/Windows), simple zip library written in C

... Some day, I was looking for zip library written in C for my project, but I could not find anything simple enough and lightweight. Everything what I tried required 'crazy mental gymnastics' to integrate or had some limitations or was too heavy. I hate frameworks, factories and adding new dependencies. If I must to install all those dependencies and link new library, I'm getting almost sick. I wanted something powerfull and small enough, so I could add just a few files and compile them into my project. And finally I found miniz. Miniz is a lossless, high performance data compression library in a single source file. I only needed simple interface to append buffers or files to the current zip-entry. Thanks to this feature I'm able to merge many files/buffers and compress them on-the-fly. It was the reason, why I decided to write zip module ( https://github.com/kuba--/zip ) on top of the miniz. It required a little bit hacking and wrapping some functions, but I kept simplicity. So, you can grab these 3 files and compile them into your project. I hope that interface is also extremely simple, so you will not have any problems to understand it.

Visit productView on Hacker NewsMar 15, 2017
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Product HuntOn track for Day 1 leaderboard · Strong signals: new, single · Missing: mac, agents, macos
73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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