denji

Moved to git repository: https://github.com/denji/nginx-tuning

NGINX Tuning For Best Performance

For this configuration you can use web server you like, i decided, because i work mostly with it to use nginx.

Generally, properly configured nginx can handle up to 400K to 500K requests per second (clustered), most what i saw is 50K to 80K (non-clustered) requests per second and 30% CPU load, course, this was 2 x Intel Xeon with HyperThreading enabled, but it can work without problem on slower machines.

You must understand that this config is used in testing environment and not in production so you will need to find a way to implement most of those features best possible for your servers.

to-your-now

The Perfect Setup for Google Antigravity IDE on Windows (2026 Edition)

If you are using Google’s Antigravity IDE on Windows to power autonomous AI agents, you’ve likely encountered terminal hanging, broken token parsing, and endless execution loops. Agents natively default to Unix-style thinking, struggling with pwsh, file path escapes, and the IDE's internal CLI safeguards.

This guide provides the Absolute AI System Prompt (gemini.md) and the Optimal IDE Settings (Allow/Deny lists) to transform your Windows environment into a flawlessly stable workspace for any LLM agent.

---

rickyah

name: Format check on pull request
on: pull_request
jobs:
  dotnet-format:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout repo
        uses: actions/checkout@v2.2.0
        with:
          fetch-depth: 0

octocat

# Compiled source #
###################
*.com
*.class
*.dll
*.exe
*.o
*.so

# Packages #

threerocks

《Grok Bot 保姆级做视频教程》配套代码

文章里的代码截图和提示词，原文都在这里，可以直接复制。

最快跑通

把本页所有文件下载到同一个文件夹，装好 uv、ffmpeg 和 Noto Sans CJK 字体，然后运行：

bash run_all.sh

karpathy

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.

souhaiebtar

# path on linux /usr/share/dbeaver/dbeaver.ini
# path on macos /Applications/DBeaverEE.app/Contents/Eclipse/dbeaver.ini
-vm
/usr/bin/java
-startup
plugins/org.eclipse.equinox.launcher_1.5.600.v20191014-2022.jar
--launcher.library
plugins/org.eclipse.equinox.launcher.gtk.linux.x86_64_1.1.1100.v20190907-0426
-vmargs
-javaagent:/home/tunknown/.apps/dbeaver/dbeaver-agent.jar

simonista

" Don't try to be vi compatible
set nocompatible

" Helps force plugins to load correctly when it is turned back on below
filetype off

" TODO: Load plugins here (pathogen or vundle)

" Turn on syntax highlighting
syntax on

pudquick

from Foundation import NSKeyedUnarchiver
from struct import unpack

# This entire function is black magic of the highest order and I'll blog about it later
def extract_share(bookmark_data):
    content_offset, = unpack('I', bookmark_data[12:16])
    first_TOC, = unpack('I', bookmark_data[content_offset:content_offset+4])
    first_TOC += content_offset
    TOC_len, rec_type, level, next_TOC, record_count = unpack('IIIII', bookmark_data[first_TOC:first_TOC+20])
    TOC_cursor = first_TOC + 20