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Hexi – Modern header-only network binary serialisation for C++

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Hexi – Modern header-only network binary serialisation for C++

Over the last few years, I've needed an easy way to quickly serialise and deserialise various network protocols safely and efficiently. Most of the libraries that existed at the time were either quite heavy, had less than stellar performance, or were an abstraction level above what I was looking for. I decided to put together my own class to do the job, starting with an easy, low-overhead way to move bytes in and out of arbitrary buffers. Along the way, it picked up useful bits and pieces, such as buffer structures and allocators that made the byte shuffling faster, often being able to do it with zero allocations and zero copies. Safety features came along to make sure that malicious packet data or mistakes in the code wouldn't result in segfaults or vulnerabilities. It's become useful enough to me that I've packaged it up in its own standalone library on the chance that it might be useful to others. It has zero dependencies other than the standard library and has been designed for quick integration into any project within minutes, or seconds with a copy paste of the amalgamated header. It can be used in production code but it's also ideal for for those that want to quickly hack away at binary data with minimal fuss.

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118points
45comments
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Indie HackersFits the IH revenue-focused audience · Strong signals: mistakes, efficiently · Missing: supports, reddit linkedin, podcasting
85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: code · Missing: mac, agents, macos
73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, ide, io · Missing: https docs, excited, just released
67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal
53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: efficient · Missing: plus, platform, intuitive
43%43% 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
17%17% 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.

Correct prediction on native model

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