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Proxmox VE tips

Just a somewhat chaotic list of Proxmox VE related tips/notes.

Please note that unless you see a shebang (#!/...) these code blocks are usually meant to be copy & pasted directly into the shell. Some of the steps will not work if you run part of them in a script and copy paste other ones as they rely on variables set before.
The { and } surrounding some scripts are meant to avoid cluttering your shell history with individual commands, etc. You can ignore them if you manually copy paste the individual commands.

Table of contents

@K1ethoang
K1ethoang / Active StarUml version 6 for Window | MacOS | Linux.md
Last active June 10, 2026 18:00
Active StarUml version 6 for Window | MacOS | Linux
@christopher-hopper
christopher-hopper / README.md
Last active June 10, 2026 18:00
macOS Zscaler stop | disable Zscaler on mac

Disable macOS Zscaler

The following script can be used to disable Zscaler on macOS. Zscaler is corporate spyware and security software that controls access to Internet resources and spoofs TLS certificates to allow inspection of encrypted communications on centrally managed corporate computers.

This script will not uninstall the Zscaler software.

Getting Help

Getting help with FreeBSD has been... interesting.

The best place to start is often the FreeBSD forums, where the question you may have has very often been answered in full. But this comes with some important caveats:

  • The forums explicitly state that if you're using a derivative (like GhostBSD) then the forum rules say to just not post there. This is on one hand understandable: derivatives in general can make wild changes (I've been around long enough to remember early Ubuntu users trying to get help from long-time Debian users.... even early Ubuntu had made major changes to the point where Debian users genuinely couldn't help). And certainly there are some FreeBSD derivatives that have made major changes, like helloSystem and ravynOS, not to mention whatever sourcery NomadBSD has done to filesystem mounting. On the other hand, GhostBSD seems (famous last words) to have made rather minimal changes

AWS ElastiCache Consumption Estimate with ECSTATS2

This tutorial shows how to run the ecstats2 script, which accesses AWS CloudWatch metrics to estimate ElastiCache Redis usage without connecting directly to Redis.


🔐 Security

@am17an
am17an / mtp-bench.py
Last active June 10, 2026 17:58
MTP benchmark
#!/usr/bin/env python3
import argparse, json, sys, time
from urllib import request
PROMPTS = [
{"name": "code_python", "prompt": "Write a Python function that returns the n-th Fibonacci number using memoization. Include a docstring."},
{"name": "code_cpp", "prompt": "Write a C++ template function `clamp(x, lo, hi)` that returns x clamped to [lo, hi]. No std::clamp."},
{"name": "explain_concept", "prompt": "Explain how speculative decoding works in large language model inference, in three short paragraphs."},
{"name": "summarize", "prompt": "Summarize in two sentences: The Industrial Revolution began in Britain in the late 18th century, transforming manufacturing through mechanization, steam power, and the factory system. It spread to continental Europe and North America during the 19th century."},
{"name": "qa_factual", "prompt": "Q: What are the four fundamental forces of physics?\nA:"},
@jav-stream
jav-stream / index.md
Created May 15, 2026 07:07
สตรีมมิ่งออนไลน์ - JUFE-622 ฉันเป็นพนักงานที่มีจุดอ่อนเห ...
ชื่อ JUFE-622 ฉันเป็นพนักงานที่มีจุดอ่อนเหมือนสาวบัญชีซึ่งฉันไม่ต้องการให้ใครรู้ ฉันก็เลยยัดเธอเข้าไปในหีโกนที่อวบอ้วนของเธอในขณะที่เธอเอาแต่มองฉันในแง่ร้า
หมวดหมู่
NSFW
เนื้อหาสำหรับผู้ใหญ่
18+
วันที่ 2026-05-15
คำสำคัญ
สตรีมมิ่งออนไลน์
mp4
วิดีโอ
AV
JUFE-622.mp4

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.