I would like to share my Linux Syscall Monitor project with you. It's a Linux process monitoring tool written in C that uses "ptrace" to observe system calls and generate behavioral reports.
I welcome any feedback or criticism—whether it's about the code .
Someone mentioned a kernel that was malfunctioning, version 7.0.0-28. I was an application manager, what should I do? Delete it or leave it as shown in the picture? My current kernel is 6.8.0-138 and everything is working fine, I've never had any problems since I installed Linux Mint. I'm still new to this, that's why I'm asking these questions. Thank you all.
Hello I have a few questions about Android images... I would like to create QEMU VM with Android or chroot with Android sysroot... Do you have some low level kernel knowledge to tell mi if this thing is actually possible... I don't want to use SDK Emulator with Ranchu, but actually there is a problem beacuse sdk images don't support VIRTIO I think... I downloaded aosp image from Google but to be honest I don't know if they will work...
Actually I need to get some low level knowledge from some expert in this field... I think Google hides knowledge about Android... in theory its just Linux... but in practice I dont know how to make it work... I think its low lovel kernel knowledge...
Why I asked this question on Termux? Beacuse I think this community is pretty low level, Termux architecture requries low level knowledge on Android...
How much of the Linux kernel that gets shipped in production is actually never used?
I'm wondering if release teams building/using a kernel image are often overwhelmed by the number of kernel configuration options, and end up shipping code that will never actually be used by their workload.
Are there any automatic tools out there that can fit a minimal Linux kernel to your runtime needs?
I am two types of kernel user; one is Android whose kernel is Linux and second is Windows whose kernel is Windows NT. I can feel the issues both of these have; both fight for stability when something cause system to get crashed and I feels BSOD and this is so annoying when you are working on something harder which needs system to be stable. This same came with me while training an AI model on my own system and Windows get crashed. So, I thought why not to try to build a kernel when never get this issue, but after some time while building I learnt nothing can be 100% stable but then I thought if this is not possible then why not to create a layer of problems which cause system to crash at system level. I had built this ARKM: Advanced Robotics Kernelling Machine, which is a custom built 12 core 64-bit Microkernel architecture with AMP which means Asymmetric Multiprocessing, ACPI which means Advanced Configuration and Power Interface into the core architecture of ARKM and due to this the stability of kernel became sky high. I had used the AMP architecture as follows:
Its Benefits came as: Deterministic performance, no contention, guaranteed responsiveness (e.g., compositor never lags even if apps crash).
Now if I talk about how ACPI changed the architecture then it also follows the steps:
Now if I talk about the 12 cores architecture it has then there is also a specialty behind it which is also as follows:
I am still building it. I also have a screenshot which if possible then i will embed to this post as visual proof for ARKM.
My Aim is if I had bought a machine, then it must work according to me. I am also creating it under my future Company AROM so that I don't want to rely on any other kernel.
ARKM is just 2 to 10 KM behind the finish line. ARKM will now come with Full-fledge Kernel and with a Server Kernel with POSIX tech Linux have just for attract the user and developers of Linux on ARKM and we are developing our own also this Linux POSIX architecture will act as bridge between present world and future world.
Support me IF you want to dive into the future tech or want to explore it.
Visuals Of ARKM
When testing our eBPF agent, I don’t always get the same experience as our users, especially in performance critical sections. I realize that the benchmark test suite isn’t always enough, because user’s environments can be completely different from our benchmarks.
My goal was to gather eBPF metrics based on the user’s usage and quickly answer questions about why things are slow (improve MTTR). To do this, I wanted:
Here is a post https://naveensrinivasan.com/posts/2026-08-02-measuring-an-ebpf-cache-without-leaving-the-kernel/
I want to hear if others have better ways to measure this.
This is not another AI generated post.
Hello everyone, suppose a process is running with PID 23654, and the OS creates a specific folder for it in the/proc directory. At the moment, how can I change resources such as the network namespace or the cpuset? The final goal I want is to change the network of a running process; suppose I use eth0 and the root namespace; then I create a namespace like ns1 with a specific interface and resource, then move the running process to this namespace to continue the process in an isolated network.
What is the fastest and most efficient way. Thanks for future answers.
Is there documentation that will tell me what these options are?
I am trying to get started with Linux kernel/device-driver development, but the Linux Foundation LFD430 (Developing Linux Device Drivers) course is very expensive to buy. so I am thinking of using its course outline as a roadmap and studying the material on my own.
I have a decent programming and Linux background, but I do not have much experience, and I am very new to kernel and driver development.
So I wanted some advice from the experts who work in this area
How would you get started with learning things, and what resources would you follow, etc.?
I would appreciate recommendations for good books, free courses, labs, YouTube channels, documentation, affordable courses, or practical projects that could roughly follow the LFD430 syllabus.
Also, if you think following the LFD430 outline is not a good way to learn from scratch, then I would love to hear what you would recommend instead.
Thanks!
HydroChroma is a next-generation UI library for Jetpack Compose that fuses Fluid Physics, Glassmorphism, and Device Sensors into a living interface that ripples, distorts, and refracts based on user interaction.
minSdk = 33 (Android 13 / Tiramisu)
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Built on top of Android Liquid Glass and Capsule by kyant0.
Practice with the underlying library: LiquidGlass Documentation
I am using redmi note 8 (ginkgo) running on evolution x android 16 rom. I recently installed kali nethunter with custom kernel for my device, but the problem is that my nethunter app has a bluetooth arsenal option but it is throwing errors during installation. I also tried running hciconfig -a in terminal but I printed nothing so I checked my kernel version and it is '4.14.356-openla-rc1-Ikteach-g98dd6390243b-dirty'. The first solution that came into my mind was flashing kernel manually but I don't know any clear instructions and requirements required to meet.
A few days ago I submitted an RFC to LKML for NSD (Neural Storage Driver) — a Linux kernel module that learns I/O access patterns (Markov chain + stride prediction) and prefetches data before it's requested, instead of relying on static heuristics. Benchmarks showed 18-19% wall-time reduction on SQLite scans and +22.6% sequential throughput improvement, validated with interleaved ON/OFF methodology.
The core idea: predict → prefetch → measure → adapt. Learn the workload's behavior, act ahead of the bottleneck, verify against real results, refine the strategy.
I came across OpenAI's writeup on GPT-5.6 Sol optimizing its own production GPU inference kernels (Triton/Gluon) and this stood out:
"Even when individual operations are fast, excess memory movement, synchronization, and inefficient data layouts can leave GPUs idle. To avoid this, GPT‑5.6 Sol found work that could be precomputed, avoided, or parallelized."
Different layer of the stack entirely (storage I/O vs. GPU compute), different scale (kernel module vs. frontier LLM serving infra), but structurally the same optimization family: identify where the resource sits idle waiting on unpredictability, front-load the work you can predict, measure against ground truth, iterate.
Not claiming any connection between the two projects — just find it validating that this "predictive resource utilization" pattern keeps showing up as the right lever to pull, from a hobbyist kernel module all the way up to frontier model serving infrastructure. Curious if others here have seen this principle recur in other unexpected corners of systems work.
NSD repo: github.com/nsdprojectdev/NSD