How we exploited CodeRabbit: From simple PR to RCE and write access on 1M repos
CodeRabbit's vulnerability allowed for remote code execution (RCE) and unauthorized access to 1 million repositories, exposing sensitive API tokens and secrets, which could lead to significant data breaches and supply chain attacks.
How to Scale Your Model: How to Think About GPUs
GPUs, particularly NVIDIA's H100 and B200, excel in matrix multiplication with specialized cores and high memory bandwidth, making them versatile for large language models (LLMs) despite their origins in graphics processing. Each H100 GPU boasts 990 bf16 TFLOPs/s and 3.35 TB/s memory bandwidth, while the B200 offers 2250 bf16 TFLOPs/s and 9 TB/s bandwidth, enhancing performance for ML tasks.
Positron, a New Data Science IDE
Positron is a free, next-generation IDE for data science that integrates Python and R, allowing seamless transitions from ideation to application, built on insights from over 14 years of RStudio development.
Learning about GPUs through measuring memory bandwidth
Measuring memory bandwidth in GPUs reveals significant performance differences across architectures, with the Qualcomm Adreno 740 achieving a bandwidth of over 130 GiB/s when using textures instead of buffers, highlighting the importance of resource selection in software optimization.
MapLibre Tile: A next generation geospatial format optimized for rendering
The MapLibre Tile (MLT) format is engineered to surpass the Mapbox Vector Tile (MVT) standard, achieving up to three times better compression ratios and three times faster decoding speeds, making it ideal for modern geospatial data demands.
Show HN: Luminal – Open-source, search-based GPU compiler
Luminal is a deep learning library that leverages search-based compilation to enhance performance, aiming to be the fastest ML framework across devices, with a focus on simplicity and minimalism in its architecture.
Monoid-Augmented FIFOs, Deamortised
Monoid-augmented FIFOs enable efficient windowed aggregation in streaming analytics, allowing for constant-time updates and queries while maintaining aggregates like top-K values without requiring inverses, as demonstrated in the provided Python code.
[R] azzurra-voice, a new State-of-the-Art Italian Text-to-Speech model
azzurra-voice is a cutting-edge Text-to-Speech model for Italian, designed to deliver a natural and expressive auditory experience by leveraging tens of thousands of hours of diverse speech data.
Into the Omniverse: How OpenUSD and Digital Twins Are Powering Industrial and Physical AI
OpenUSD and digital twins are revolutionizing industrial and physical AI by enabling the rapid creation of physically accurate simulations, which enhance the training of AI agents and autonomous systems.
MindJourney enables AI to explore simulated 3D worlds to improve spatial interpretation
MindJourney empowers AI to navigate 3D environments by simulating movement and generating multiple perspectives, enhancing spatial reasoning capabilities beyond static images.
[R] Virtuous Machines: Towards Artificial General Science
A generalizable scientific method has been integrated into AI, enabling it to navigate the entire scientific process, from ideation to manuscript production, yielding new insights in cognitive science as detailed in the arXiv paper.
[D] Beyond the cloud: SLMs, local AI, agentic constellations, biology and a high value direction for AI progress
Pushing for models under 1 billion parameters can drive innovation in AI, as it compels researchers to focus on fundamental algorithms rather than relying on sheer scale for performance improvements.
Chunks as Arms: Multi-Armed Bandit-Guided Sampling for Long-Context LLM Preference Optimization
LongMab-PO introduces a Multi-Armed Bandit (MAB) strategy to optimize long-context LLMs by selecting the most informative chunks for generating diverse and high-quality responses.
Input Time Scaling
Input Time Scaling introduces a novel approach to enhance Large Language Models (LLMs) by focusing on optimizing query inputs during both training and testing, revealing that low-quality datasets can yield high performance contrary to traditional beliefs.
ZenFlow: Stall-Free Offloading Engine for LLM Training
ZenFlow is a stall-free offloading engine for LLM training that achieves over 85% reduction in GPU stalls and up to 5× speedup by prioritizing important gradient updates on the GPU while deferring less critical ones to the CPU asynchronously.