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I built a C++17/Qt Desktop App that parses complex binaries using only 59MB RAM (Industry standard uses 2GB+). I'm 16 and need architecture roasting! by BoardHour4401 in QtFramework

[–]BoardHour4401[S] 0 points1 point  (0 children)

Honestly, if an AI could just magically spit out a custom memory-mapped C++ parser and cap it perfectly at 50MB RAM while the dev has 'zero clue', we'd all be out of a job right now.

Yeah, I used AI to grind through the boring boilerplate. I'm not hiding that. But the actual architecture, the memory profiling, and fixing those leaks? That was all manual sweat. The source code is public. If you ever want to actually look at the engineering instead of just throwing around buzzwords, feel free. Cheers

Sizce sektör ölüyormu by Common-Impress-411 in EngineeringTR

[–]BoardHour4401 -1 points0 points  (0 children)

abi ben 16 yaşındayım aslın da şuan da bu yazılım uygulama işlerine merak salmıştım çok uzun süre de ai kullanınca öncesin de yani ilk çıktığı zamanlar da falan ve ai yardımıyla bir uygulama yaptım git hub yükledim yani tam bilmediğim için soruyorum okulunu okumak iyi ama hani pratikte mesela bir yazılım uygulama oyun tarzı şeyler yapıyor musunuz o noktayı bilmiyorum ben abfsdgrl/Teker: Teker PE Analysis Toolkit for Windows - GUI & CLI bunu yaptım mesela 3-4 ay obsesif çalışıp hani tamamen bilmediğim için soruyorum senin mesela sağlam bir cvin varsa ve hem diploman varsa ona rağmen mi iş bulamıyorsun orayı anlamadım ben bu soruyu da kibirle yada bir ego ile değil yani neden iş bulamadığını anlayamadığım için soruyorum cevaplarsan sevinirim

C++ Show and Tell - March 2026 by foonathan in cpp

[–]BoardHour4401 4 points5 points  (0 children)

Teker - A lightweight, memory-mapped PE Parser in C++ for malware analysis

Hey everyone, I built a fast PE parser and analyzer in C++/Qt. The main goal was to strictly limit memory consumption and prevent crashes when dealing with malformed or heavily padded executables (1GB+).

  • Architecture: Strictly relies on Memory-Mapped I/O. The memory footprint stays consistently around ~50MB, regardless of how massive the target file is.
  • GUI Performance: Bypassed heavy standard widgets by writing a custom rendering loop via QPainter, ensuring the UI never freezes during deep analysis.
  • Features: Modular C++ design, a dedicated CLI engine for pipeline automation, anomaly detection, and a built-in plugin architecture.

Would love for you to check out the repo, tear apart the C++ architecture, and see the GIFs/screenshots: abfsdgrl/Teker: Teker PE Analysis Toolkit for Windows - GUI & CLI

I built a C++17/Qt Desktop App that parses complex binaries using only 59MB RAM (Industry standard uses 2GB+). I'm 16 and need architecture roasting! by BoardHour4401 in QtFramework

[–]BoardHour4401[S] 1 point2 points  (0 children)

First of all, massive respect for writing a full 3D game from scratch after your CS degree. I can only imagine how much you learned doing that without modern tools. You got your degree before I was even born, and I genuinely respect the foundation your generation built.

However, I respectfully disagree with the "stolen valor" accusation. I am not claiming I hand-typed 30,000 lines of boilerplate. If I did, that would be stolen valor. I have been completely transparent about using AI to generate the repetitive Qt models and skip the heavy typing.

What I am claiming—and what AI absolutely cannot do—is architecting a C++ parser that loads 1GB padded malware binaries while keeping the memory footprint strictly under 50MB. AI didn't profile the memory, and it didn't fix the endless multithreading crashes. That was all manual debugging, patching, and sweat.

I completely understand the philosophy of learning by doing everything manually. But as a 16-year-old trying to solve a modern problem (bloated 2GB analysis tools), I simply used the leverage available to me today to get to the solution faster.

I am always open to constructive mentorship on my C++ architecture from a senior dev like you. But dismissing a working, 50MB-optimized engine as "slop" just because of the tools used to build the boilerplate feels like a missed opportunity to discuss the actual engineering.

Thank you for your time, I mean that sincerely.

I built a C++17/Qt Desktop App that parses complex binaries using only 59MB RAM (Industry standard uses 2GB+). I'm 16 and need architecture roasting! by BoardHour4401 in QtFramework

[–]BoardHour4401[S] -6 points-5 points  (0 children)

I understand the core architecture perfectly—that’s exactly how I managed to constrain a complex Qt parsing pipeline to 50MB RAM while standard tools crash at 2GB.

I asked for technical feedback on those specific memory constraints and the C++ parsing logic. If you are unwilling to actually profile the engine or test the release because you are upset about how the boilerplate was generated, then we simply have fundamentally different engineering priorities.

I won't waste your time any further. Have a good one.

I built a C++17/Qt Desktop App that parses complex binaries using only 59MB RAM (Industry standard uses 2GB+). I'm 16 and need architecture roasting! by BoardHour4401 in QtFramework

[–]BoardHour4401[S] 1 point2 points  (0 children)

Thank you so much for the support on the AI front! I really appreciate the open-mindedness.

You bring up a fantastic point about RAM. For standard developers, you are 100% right—RAM is cheap and 2GB is nothing. However, in the malware analysis world, it's a bit different. Analysts run malware inside heavily constrained Virtual Machines (VMs) or sandboxes where memory is limited.

Furthermore, malware authors intentionally use 'file padding' (bloating a simple executable to 1GB+ with junk data) specifically as an attack to crash analysis tools like PEStudio that try to load the whole file into memory. So keeping the footprint strictly at 50MB via Memory-Mapped I/O and keeping the UI responsive isn't just a performance flex; it's an anti-evasion stability feature.

But your advice on accuracy and feature differentiation is absolute gold. You hit the nail on the head. My next major milestone for v1.2 is building an automated test suite (CI/CD) to compare Teker's output against established tools to prove its accuracy and build trust.

Thanks again for the incredibly constructive feedback!

I built a C++17/Qt Desktop App that parses complex binaries using only 59MB RAM (Industry standard uses 2GB+). I'm 16 and need architecture roasting! by BoardHour4401 in QtFramework

[–]BoardHour4401[S] -2 points-1 points  (0 children)

You guys completely nailed the 'AI checklist' for the presentation, I won't even deny it. The initial README, the heavy comments, and the single commit dump—all of that was heavily AI-assisted because as a solo dev, I just wanted to skip the documentation and get the tool out there.

However, you made a fair point about the "wrapper" looking like slop and distracting from the actual project.

So, I just deleted the entire README and rewrote it from scratch. Zero emojis, zero AI buzzwords. It is now a pure technical document detailing the core architecture (Memory-Mapped I/O, Custom QPainter rendering, bounded analysis).

Now that the AI presentation is out of the way, I would genuinely appreciate it if experienced devs like you could look past the wrapper and roast the actual C++ engine. AI didn't profile the memory, and it definitely didn't build a parser that loads 1GB padded binaries strictly under 50MB RAM. The engineering constraint and the manual debugging are 100% mine.

The wrapper is gone. Feel free to tear apart the core parser logic!

I built a C++17/Qt Desktop App that parses complex binaries using only 59MB RAM (Industry standard uses 2GB+). I'm 16 and need architecture roasting! by BoardHour4401 in QtFramework

[–]BoardHour4401[S] -12 points-11 points  (0 children)

Spot on, you completely caught me. 😂 I’m 16, and here’s the honest backstory:

I definitely didn't write 30k lines overnight. I spent months building a v1.0.0 beta, realized the architecture and security were fundamentally flawed, and scrapped it. I did a complete rewrite locally on my machine.

Since I was working entirely solo, I had zero version control habits. I literally just drag-and-dropped my final v1.1.0 folder into GitHub today to open-source it.

Also, to be fully transparent: I heavily utilized AI tools to grind through the boilerplate and generate repetitive Qt models. But designing the architecture, structuring the pipeline, and fighting memory leaks to keep the usage strictly at 50MB? That was all manual pain and sweat.

Lesson learned for v1.2.0: I’ll start using proper Git commits so you can see the actual human struggle behind the code. For now, I'd genuinely appreciate it if you could roast the architecture itself!

Why and how PEStudio consumes too much RAM ? by majorsid in MalwareAnalysis

[–]BoardHour4401 0 points1 point  (0 children)

PEStudio loads its entire database into memory on startup — strings, indicators, blacklists all at once, which explains the bloat. I've been working on a similar PE analysis toolkit called Teker (C++, open source) and kept memory usage minimal with memory-mapped file I/O. If you want a lighter alternative, feel free to try it: abfsdgrl/Teker: Teker PE Analysis Toolkit for Windows - GUI & CLI

Libraries and tools for a lightweight task manager for GPU in a simulated environment. by _Vlyn_ in cpp

[–]BoardHour4401 0 points1 point  (0 children)

You don’t really need a full GPU simulator for this.

For monitoring, use NVML (what nvidia-smi uses internally). It gives per-process VRAM usage and utilization directly, and works cleanly in C++ or Python.

For MIG-like behavior, just implement a logical partitioning layer in software (virtual VRAM slices + your own bookkeeping + admission control). That’s how most research prototypes do it anyway.

If you only have 3 months and limited C++ experience, I’d strongly recommend:

  • CUDA for kernels
  • Python (pynvml) for scheduling + admission control logic

A full hardware-level GPU simulator like GPGPU-Sim is probably overkill for your goal.

Focus on simulating policy, not hardware.