Technical disclosure and PoC for CVE-2026-84543, a macOS SMB kernel vulnerability
PTXBench: an open-source environment for kernel agents with architecture-specifc CUDA/PTX
Curated directory of Android root apps, Magisk, KernelSU, LSPosed modules, system tools, privacy utilities, and no-root Shizuku apps.
A langgraph based workflow with a C++ CUDA harness to optimize CUDA kernels
MiniMax-H3 33B audio+video DiT accelerated 1.54x on a single NVIDIA DGX Spark (GB10, sm_121): NVIDIA Sol-Engine FirstBlockCache + batched VAE decode ported to single-GPU ComfyUI, Sol-Attn patched for compute 12.1, kijai Triton INT8/TMA kernels. Benchmarks, charts, one-shot installer, agent recipe.
Recover usable memory on DGX Spark with a 64 KiB kernel: an opt-in NVIDIA UVM page-table packing patch, build tools, and measured validation.
Experimental Linux bring-up research for the M4 MacBook Air (T8132/J713): device-tree patches, kernel experiments, and documented findings. Not an installer.
Temporary ADB root tool for Xiaomi devices (kernel 5.10.236).
AMD gfx12 (RX 9070/9070 XT) Memory-Efficient Sage Attention for MiniMax H3. Matches KJNodes' Nε‘ patch with native HIP kernel.
QEMU harness for AI agents: boot aarch64/arm/riscv64/x86_64 kernels in seconds, exec via guest agent, fast rootfs rebuilds, IoT emulation
MiniMax H3 (video + audio) on TensorFold 0.6.5 with an H3 family, Mac Studio M5 Ultra: 5 s 864x480 clip in ~50 s, 40 s at 768x448 β the fastest H3 we have measured on Apple Silicon; int8 tensor-unit kernels + Turbo adapter; one-shot + GHCR carrier
Ubuntu Desktop for OnePlus Pad 2 Pro: kernel integration, device packages and installation tools
Linux/pc98: Linux for NEC PC-9800 series, integrated repository of kernels, toolchains, and Debian/pc98 port
Code and reproducibility artifacts for reconstructing megakernel persistence with CUDA PDL
iOS kernel exploit toolkit | DarkSword R/W | SSV Bypass | XPF offset verification
Official implementation of "VKSR: Scalable Kernel Surface Reconstruction Using Vecchiaβs Approximation" (ECCV'26)
Efficient Triton Kernels for LLM Training
A Quirky Assortment of CuTe Kernels
A Python-embedded DSL that makes it easy to write fast, scalable ML kernels with minimal boilerplate.
Dist-MoE implements Speed-of-Light distributed Mixture-of-Experts forward and backward kernels. They are highly optimized and fused comm-compute kernels for training and inference on SM100+ GPUs