... BetterStack, and there was nothing. So whatever killed the pod didn t let it run any more JavaScript on the way out. The convenient theory The convenient theory was OOM. Node.js services hit the cgroup limit, the kernel reaps the process with SIGKILL, the dashboards spike, the pod restarts. We ve seen it before, and it s the first thing anyone reaches for. We looked, and container memory stayed comfortably below the limit on every restart we examined ... ... When OOM stopped fitting, we walked back to kubectl and looked at what it actually said about the dead containers. Last State: Terminated Reason: Error Exit Code: 137 Exit code 137 is 128 + 9, which is SIGKILL, so the kernel

... everyone I just launched kernelIQ an open-source Linux diagnostic assistant built to help investigate system issues in a more natural way. Instead of only showing raw metrics, kernelIQ

... here: git subtree push --prefix="/path/to/sdk" "sdk_name" "$BRANCH" Comment from Solal Zanovello(@solal_aegis): This is a game-changer for AI context management. I m currently 17, building Aegis Omni-Core (a high-performance no_std Rust kernel), and I hit the same wall with submodules. When you're working on low-level SIMD optimizations (AVX2), the AI (Cursor/Claude) needs to see the link between the core kernel

... during inference) by up to 7.3 . What's new in this release: User-selectable Compute Mode in Settings AI Optimization. Pick Auto, CPU, or GPU (CUDA). Real integration with the turboquant-gpu library. The GPU backend runs cuTile fused kernels for the full 2-bit / 3-bit KV compression path. The CPU backend uses a PyTorch fallback with physical-core thread pinning and MKLDNN acceleration. Live-measured compression ratios in the UI. No more static lookup tables ... ... actual compression your backend produced on the last request. Graceful fallback everywhere. Missing CUDA? Falls back to CPU. Missing cuTile kernels

... everyone! I think writing GPU code is going to be something that's going to become ubiquitous even though today it's a tough and time consuming process. Claude Code and Cursor agents have gotten great at writing GPU kernels once they know what to target. What's missing is the tooling to get them there. We're building that tooling at https://ncompass.tech . Our agent analyzes the profile and gives back a list of bottlenecks and ways to solve ... ... them. Once that's known, you can feed that to Claude Code / Cursor and they will implement the code. Using Claude Code and the nCompass agent, we implemented a Hopper GEMM kernel