hopechong

Time flies! Can't believe it's been 15 years already. Congrats on publishing your new book!

We ran hundreds of benchmarks to tune storage systems for distributed training

Hi HN! We built GPU Compass to make the cloud GPU landscape browsable. The data comes from SkyPilot Catalog, open-source, auto-updated every 7 hours from 20+ cloud APIs. Feedback welcome.

Coding agents that read papers before writing code find optimizations that code-only agents miss.

We added a literature review phase to Karpathy’s autoresearch loop and pointed it at llama.cpp. The agent autonomously read arxiv papers, studied competing forks and spun up VMs to run parallel experiments.

We've been seeing a lot of people run OpenClaw directly on their main machine, which is a bad idea for a few reasons: it needs broad system access, it's noisy on resources, and if something goes wrong you want a clean blast radius. The obvious answer is "just isolate it," but isolation has real friction. You need to provision a machine, handle SSH keys, configure security groups, and remember to tear things down so you're not leaking money. This post walks through the three realistic options:

Docker – lowest friction, but shares your kernel and has limits depending on what OpenClaw needs to do Dedicated hardware – best isolation, but you're paying 24/7 and it takes time to set up Cloud VM – the sweet spot for most people: true isolation, pay-per-use, tear it down when you're done

For the cloud VM path, we show how to launch a hardened OpenClaw environment on AWS, GCP, Azure, or any other cloud with a single command, handling provisioning, SSH, and auto-teardown for you.

Zookeeper-less trend. Cool Stuff.