triangle chenxinlong

What do Etcd, Consul, and Zookeeper do?
- Service Registration:
  - Host, port number, and sometimes authentication credentials, protocols, versions numbers, and/or environment details.
- Service Discovery:
  - Ability for client application to query the central registry to learn of service location.
- Consistent and durable general-purpose K/V store across distributed system.
  - Some solutions support this better than others.
  - Based on Paxos or some derivative (i.e. Raft) algorithm to quickly converge to a consistent state.
Centralized locking can be based on this K/V store.

How to ssh to a remote server without typing your password

Save yourself a few keystrokes. Follow the steps below:

Run this Bash script on your laptop:
```
#!/usr/bin/env bash
```

The hostname of your remote server.

🤗Huggingface Model Downloader

Considering the lack of multi-threaded download support in the official huggingface-cli, and the inadequate error handling in hf_transfer, this command-line tool smartly utilizes wget or aria2 for LFS files and git clone for the rest.

Features

⏯️ Resume from breakpoint: You can re-run it or Ctrl+C anytime.
🚀 Multi-threaded Download: Utilize multiple threads to speed up the download process.
🚫 File Exclusion: Use --exclude or --include to skip or specify files, save time for models with duplicate formats (e.g., .bin and .safetensors).
🔐 Auth Support: For gated models that require Huggingface login, use --hf_username and --hf_token to authenticate.
🪞 Mirror Site Support: Set up with HF_ENDPOINT environment variable.

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

	package chainsaw

	import (
	"reflect"
	"testing"
	)

	/*
	goos: darwin
	goarch: arm64

	# install

	```
	brew install esolitos/ipa/sshpass
	```

	according to https://github.com/kevinburke/sshpass/issues/3

	# use

	#!/bin/bash
	for d in /sys/kernel/iommu_groups//devices/; do
	n=${d#/iommu_groups/}; n=${n%%/*}
	printf 'IOMMU Group %s ' "$n"
	lspci -nns "${d##*/}"
	done

	# 所有 syscall 函数可以在 strace 源码中的 linux/<platform>/syscallent.h 里找到，比如 x86_64

	https://github.com/strace/strace/blob/master/linux/x86_64/syscallent.h