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Building a Linux GPU Driver for the M4 Mac Mini in One Month

Two developers built a fully OpenGL ES 3.0 compliant Linux GPU driver for the M4 Mac Mini in one month via clean-room reverse engineering.

Niklas and the author reverse engineered Apple's AGX GPU firmware ABI and user-space components in about a month, a process that normally takes years, producing an OpenGL ES 3.0 conformant driver fast enough to run Minecraft at 200fps on an M4 Mac Mini. The work was done transparently using hypervisor traces without examining Apple binaries, following clean-room practices, and included a custom shader compiler, command stream builder, and a full Linux kernel driver for the firmware ABI. The A18 Pro firmware ABI proved significantly more complex than the M1's, with 1.5x as many structs and twice as many pointers. All experiments and provenance evidence were published in public agx-re repositories.

Hacker News · AI · 21h agoResearchHN 40↑ · 5 comments

Google Patches Pixel Modem Zero-Day Exploited in Targeted Attacks

Google patched Pixel modem zero-day CVE-2026-58704 (CVSS 8.0), exploited in limited targeted attacks, enabling adjacent privilege escalation without user interaction.

Google's September 2026 Pixel security update fixes CVE-2026-58704, a CVSS 8.0 permission bypass caused by a logic error in the cellular modem, allowing remote (proximal/adjacent) elevation of privilege with no user interaction or additional privileges. Google confirms indications of limited, targeted exploitation in the wild but provides no attribution, target count, or attack objectives. The modem location is significant because it operates below much of the Android application security model. The bulletin also patches multiple critical RCE flaws in IMS, libpixelimsmedia, VPU, modem, telephone and BigOcean components, with the 2026-09-05 patch level protecting devices.

Security Affairsupdated · 2h agofirst · 3h agoExploit / PoC in the wild 7 sourcesCVE-2026-58704

Inside NVIDIA’s cuDNN Graph API: Fusion, Autotuning, and Plan Reuse with cuDNN Frontend

MarkTechPost tutorial walks through NVIDIA's cuDNN Frontend graph API, covering kernel fusion, autotuning, plan reuse, and CUDA graph capture on Colab GPUs.

The tutorial explains how to express GPU computations as operation graphs via the cuDNN Frontend graph API, running the five-step build pipeline of validate, build operation graph, create execution plans, check support, and build plans. It progresses from a single fused convolution with bias and ReLU to autotuning across engine configs, FP8-style epilogues, attention, plan serialization, dynamic shapes, and CUDA graph capture. Each kernel is benchmarked against a PyTorch reference on a single Colab GPU to verify correctness and measure cost. The piece also covers practical setup issues like making libcudnn.so visible to the frontend's dynamic loader.

MarkTechPost · 19h agoAI tools & infra

NVIDIA Vera Rubin NVL72 Delivers Leading Performance in MLPerf Inference v6.1 Debut

NVIDIA's Vera Rubin NVL72 debuts in MLPerf Inference v6.1 with up to 3.7x higher throughput than GB300 NVL72 and 99% scaling efficiency at 288 GPUs.

In its first MLPerf Inference preview submission, NVIDIA's Vera Rubin NVL72 achieved up to 3.7x higher throughput than GB300 NVL72 on Qwen3-VL and 2.5x on DeepSeek-R1. A 288-GPU GB300 NVL72 submission across four racks reached 99% scaling efficiency on the DeepSeek-R1 offline benchmark. Software optimizations delivered up to 1.6x gains over v6.0, leveraging TensorRT-LLM, vLLM, Dynamo, disaggregated serving, and NVFP4 precision.

NVIDIA Blog · 2h agoAI industry 2 sources