This post shows you how to configure training jobs on Amazon SageMaker AI to get the most out of Blackwell’s architecture on AWS. You learn how to select batch sizes and sequence lengths that take advantage of Blackwell’s expanded memory, choose the right precision format for your model size (1B to 64B parameters), and apply activation checkpointing strategically. By the end, you have a practical framework for tuning your training configuration and launching distributed training jobs on P6-B2... Read more ›
In this article, we compare a shallow MLP, a strong baseline for tabular data, with a 1D Convolutional Neural Network (CNN), which uses… Read more ›
This is Day 11 of building a neural network from scratch. Yesterday we went over gradient descent: read the slope of the loss at your… Read more ›
NOTE: This is a pre-release feature. The Fil-C 0.679 release does not ship with this feature. To test this feature, you need to build from source. Read more ›
AGENTS.md is the shared source of truth. It maps the whole architecture, the cross-cutting model, and the conventions that reflection depends on. CLAUDE.md / GEMINI.md / the Cursor rule are one-line bridges to it, so nothing is duplicated. Point your agent here first. Read more ›
Modern high performance computing (HPC) applications must target heterogeneous hardware. This requires significant work to ensure domain specific implementations translate to highly performant kernels across a range hardware types and vendors, each requiring bespoke optimization to make use of the specific target architecture. Through the development of a domain specific compiler built with the multi-level intermediate representations (MLIR) project, one can express a high-level, close to the... Read more ›
Sometimes the problem isn’t your optimizer, architecture, or hyperparameters. Read more ›
AI Infrastructure, Transformer Architecture, Memory Systems, GPU Economics, Deep Learning Systems Read more ›
Nvidia’s European AI factory expansion also strengthens the quantum-classical stack. Read more ›
Build your first Image Classification Project with TensorFlow Read more ›
Introduction Part 1 measured the dual GH200 workstation as a memory system. Part 2 used those measurements to explain why DeepSeek V4 Flash can be fast in vLLM when the model layout fits the hardware: keep hot weights in HBM, avoid unnecessary Hopper-to-Hopper traffic, and use MTP only where the acceptance rate pays for the draft work. GLM-5.2 starts at 2.39 output tok/s on this machine and a... Read more ›
Generative AI workloads are rapidly outgrowing the memory and compute budget of single GPUs. For inference developers building media generation pipelines, the challenge is scaling across multiple… Read more ›
Understanding Neurons, Neural Networks, Neural Connections, Activation Functions & More Read more ›
chachaml is a Clojure-native MLOps library developed within the Flexiana ecosystem.It's built for teams that want to run machine learning systems in production without moving their workflows to another language or stack. Read more ›
In this paper, we propose a discrete roto-reflection group equivariant vision transformer with convolutional attention. Roto-reflection equivariant networks preserve the rotational, flip and positional symmetry in feature maps, making them useful for tasks where orientation of the inputs is relevant to the model outputs. In image classification and object detection, most of the studies on roto-reflection equivariant models have focused on using ... Read more ›
This is a guest post by Mateusz Maćkowski and Marek Grzelak, co-maintainers of cot.rs and speakers at Rustikon 2026. You can watch their full talk here. In the very beginning, all we wanted to do was build a JSON API. After doing that a few times in Rust, we noticed a recurring pattern. Every new […] Read more ›
URL Source: Markdown Content: Guan Wang 1,∗,†, Changling Liu 1,∗, Chenyu Wang 2, Cai Zhou 2, Yuhao Sun 1, Yifei Wu 1, Shuai Zhen 1, Luca Scimeca 1, Yasin Abbasi Yadkori 1,† 1 Sapient Intelligence 2 MIT ###### Abstract The current pretraining paradigm for large language models relies on massive compute and internet-scale raw text, creating a significant barrier to foundational research. In contrast, biological systems demonstrate highly sample-efficient learning through multi-timescale p... Read more ›
Impact of Linux Kernel vulnerabilities on B&R products apeterson Jun 23, 2026 Release DateJune 23, 2026 DescriptionSummaryB&R is aware of publicly reported vulnerabilities affecting the Linux kernel versions shipped with the products listed as affected in the advisory. Successful local exploitation of these vulnerabilities could allow an attacker to escalate privileges on the affected system. Public proof-of-concept exploits are available for the vulnerabilities described herein. At the time ... Read more ›
Welcome to the six hundred and fifty-first issue of LLVM Weekly, a weekly newsletter (published every Monday) covering developments in LLVM, Clang, and related projects. LLVM Weekly is brought to you by Alex Bradbury. Subscribe to future issues at and pass it on to anyone else you think may be interested. Please send any tips or feedback via email: asb@asbradbury.org, or Mastodon: @llvmweekly@fosstodon.org / @asb@fosstodon.org, or Bluesky: @llvmweekly.org / @asbradbury.org. Read more ›
Curated news, articles and jobs related to Data Science, AI, & Machine Learning Read more ›