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A high-performance Hex Editor with Yara-X support in C#

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A high-performance Hex Editor with Yara-X support in C#

I'm integrating the Yara-x rules engine into my C# hex editor. I'm working to maximize the performance and efficiency of the integration. I'd like to ask your opinion about this. I personally made this decision to expand the functionality of my hex editor by adding Yara-x support. This allows me to search for signatures in binary files in more detail. I think viewing the entire byte grid can help in malware research. I implemented this using memory mapping files. I also divided the scanning methods into modes: small files are mapped completely, while large files are scanned in 16MB chunks with a small 64KB overlay to prevent a situation where half the signature is in one chunk and half is in another. I also used smarter memory management for performance with large files. Documentation is in the readme. But in short, this is an implementation that doesn't overload the garbage collector in C# and handles unsafe pointers and raw memory addresses. What's important is that I now have protection against bad rules that, for example, search for any byte, overloading the scanner. Such rules won't work, and the scanner will stop scanning so that the scanner doesn't crash with an error. I can't say right now that this tool could be better than the others, because it's currently in development and I still have room for improvement, but it would be cool to hear people's opinions or accept other people's ideas for improving the tool. (The native version with Yarax is not yet available in current releases, but the source code is available and you can compile or read it yourself.)

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Indie HackersFits the IH revenue-focused audience · Strong signals: maximize · Missing: supports, reddit linkedin, podcasting
81%81% 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: using, code · Missing: mac, agents, macos
78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
55%55% 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: personal, scanner · Missing: mobile apps, ios, entrepreneurs
53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
38%38% 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 · Strong signals: smart · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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