Linux Kernel 6.14 is Released: This is What's New for Compute Express Link (CXL)

Linux Kernel 6.14 is Released: This is What's New for Compute Express Link (CXL)

The Linux Kernel 6.14 release brings several improvements and additions related to Compute Express Link (CXL) technology.

Here is the detailed list of all commits merged into the 6.14 Kernel for CXL and DAX. This list was generated by the Linux Kernel CXL Feature Tracker .

Understanding STREAM: Benchmarking Memory Bandwidth for DRAM and CXL

Understanding STREAM: Benchmarking Memory Bandwidth for DRAM and CXL

In today’s Artificial Intelligence (AI), Machine Learning (ML), and high-performance computing (HPC) landscape, memory bandwidth is a critical factor in determining overall system performance. As workloads grow increasingly data-intensive, traditional DRAM-only setups are often insufficient, prompting the rise of new memory expansion technologies like Compute Express Link (CXL). To evaluate memory bandwidth across DRAM and CXL devices, we use a modified industry-standard tool called STREAM.

In this blog, we’ll explore what STREAM is, how it works, why it’s commonly used for benchmarking memory bandwidth, and how a modified version of STREAM can be used to measure performance in heterogeneous memory environments, including DRAM and CXL.

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Is Thinking Mode Affecting Your Agentic Workflows?

Is Thinking Mode Affecting Your Agentic Workflows?

I jumped on the trend of running local LLMs and agents and was having a lot of fun until my agents kept failing, timing out, and just stopping without any obvious reason. I tried PaperClip + ZeroClaw, PaperClip + Hermes-Agent, and Hermes-Agent + Hermes-Workspace with Qwen 3.6 and Gemma 4 models (various sizes and quantization levels). All of them failed in the same way at some point in the workflow with almost nothing reported in the logs to indicate what was happening. Some tasks completed without any problem, but most did not, often leaving me to wonder what was going on. After many hours of debugging and reading many forums, I finally found that this was a model serving configuration trap that catches many people the first time they self-host a reasoning model.

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Into the 3MF Specification Wilderness: Reading 1000+ Pages of Specifications

Into the 3MF Specification Wilderness: Reading 1000+ Pages of Specifications

Series: Building lib3mf-rs

This post is part of a 5-part series on building a comprehensive 3MF library in Rust:

  1. Part 1: My Journey Building a 3MF Native Rust Library from Scratch
  2. Part 2: The Library Landscape - Why Build Another One?
  3. Part 3: Into the 3MF Specification Wilderness - Reading 1000+ Pages of Specifications
  4. Part 4: Design for Developers - Features, Flags, and the CLI
  5. Part 5: Reflections and What’s Next - Lessons from Building lib3mf-rs

“How hard can it be? It’s just a file format.”

That’s what I thought before I started reading the 3MF specifications. After reading, re-reading, and getting AI to help summarize and dig deeper into the interpretations and understandings, I was ready to begin.

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