researchredditApril 10, 2026 at 05:51 PM

60% MatMul Performance Bug in cuBLAS on RTX 5090

RTX 5090 上 cuBLAS 的 60% MatMul 效能漏洞

cuBLAS is dispatching an inefficient kernel for every batched FP32 workload, from 256×256 to 8192×8192×8, only using ~40% of available compute on RTX GPUs. Tested on RTX 5090 with CUDA 13.2.51, cuBLAS 13.3.0, and driver 595.58.03—previous versions perform even worse. The author wrote a simple but efficient kernel and compared it against cuBLAS across various workloads, demonstrating significant performance gaps.

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Keywords

performance bugMatMulbatched operationsFP32GPU optimizationkernel efficiencycompute utilization

ResearchRead91d

Google's AI Development Tool Antigravity Has a Critical Flaw That Lets Hackers Run Commands Remotely

Security researchers at Pillar Security just revealed a serious vulnerability in Google's Antigravity AI agent development tool. Attackers can exploit a prompt injection weakness to bypass the sandbox protection and execute malicious commands from anywhere. The good news? Google's already patched it after being notified.

This isn't just about Google — any AI agent tool that processes external prompts could have the same sandbox-escaping weakness lurking underneath

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ResearchRead99d

AMD's AI Director Just Proved Claude Isn't Ready for Real Engineering Work

AMD's AI director analyzed 6,852 Claude Code sessions and found something alarming: Claude is getting worse at complex engineering tasks, not better. The data is brutal—thinking depth dropped 67%, the model stopped reading code before editing it (dropping from 6.6 reads to just 2.0), and it started blindly modifying files it never even looked at. Stop-hook violations jumped from zero to 10 per day. Turns out Anthropic quietly downgraded the default effort level from "high" to "medium" and introduced "adaptive thinking" that apparently isn't thinking hard enough. If you're using Claude for anything mission-critical, this is a wake-up call.

The model is literally making blind edits to files it never read, which means your 'AI pair programmer' might be randomly breaking working code

ResearchRead92d

How Google DeepMind is researching the next frontier of AI for Gemini — Raia Hadsell, VP of Research

Google DeepMind's VP of Research Raia Hadsell discusses the cutting-edge research driving the next generation of Gemini AI. The article explores what's coming next for Google's flagship AI model and the research directions that will shape its future capabilities.

Researchers crack Gemini's SynthID detection—here's what that means for AI-generated content

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ResearchRead102d

Researchers crack Gemini's SynthID detection—here's what that means for AI-generated content

Google's Gemini has a built-in watermark called SynthID that's supposed to flag AI-generated images so you can tell them apart from real ones. Sounds good in theory, right? Well, researchers just figured out how to fool it. They found ways to strip out or manipulate the watermark, which basically means bad actors could now pass off AI images as real without getting caught. It's like Google put a lock on the door, but someone just found the master key. This matters because as AI image generation gets better and better, we're going to need reliable ways to spot fakes—especially with deepfakes and misinformation becoming a real problem. The fact that SynthID can be reverse-engineered this easily is a pretty big wake-up call for the whole industry.

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