Mehver

# author: GitHub@Mehver
# This document allows GitHub to identify Markdown as a language and add them to GitHub Repository's language statistics.
# 这个文档可以使GitHub将Markdown文档识别为Repo语言并加入到语言统计中。

# https://gist.github.com/Mehver/5c7d35afb0999bfc7a3e03adab99438b

*.md linguist-detectable=true
*.md linguist-documentation=false

Mehver

# author: GitHub@Mehver
# This document allows GitHub to correctly identify Altium Designer, KiCAD, Gerber and Eagle documents and add them to GitHub Repository's language statistics.  
# 这个文档可以使GitHub正确的识别Altium Designer、KiCAD、Gerber以及Eagle的文档，并将它们加入到GitHub Repository的语言统计中。

# https://gist.github.com/Mehver/61c37bf71159cc9a511558ec7c218339

*.OutJob linguist-detectable=true
*.PcbDoc linguist-detectable=true
*.PrjPCB linguist-detectable=true
*.SchDoc linguist-detectable=true

noelboss

Simple automated GIT Deployment using GIT Hooks

Here are the simple steps needed to create a deployment from your local GIT repository to a server based on this in-depth tutorial.

How it works

You are developing in a working-copy on your local machine, lets say on the master branch. Most of the time, people would push code to a remote server like github.com or gitlab.com and pull or export it to a production server. Or you use a service like deepl.io to act upon a Web-Hook that's triggered that service.

zebreus

# firebase.tf https://gist.githubusercontent.com/Zebreus/906b8870e49586adfe8bd7bbff43f0a8/raw/firebase.tf
# Terraform configuration for creating a firebase project with firestore, functions and storage
# Unfinished

terraform {
  required_providers {
    google-beta = {
      source  = "hashicorp/google-beta"
      version = "4.11.0"
    }

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.

qoli

{
  "uuid": "e24b4542-ce5d-4625-b2cb-e40429945328",
  "json": [
    {
      "type": 1,
      "name": "if101[屏蔽国内ip]",
      "api": "http://api.if101.tv/v1/vod"
    },
    {
      "type": 0,

loggedbytucker-blip

Raspberry Pi 5 Media Server — Commands

Immich (photos) + Jellyfin (movies/TV) + Tailscale (access from anywhere). Commands only, in the order they're run in the video.

Tested August 2026 on:

Hardware	Raspberry Pi 5, 8GB — CanaKit Starter Kit PRO

mdehling

Python 3.13+ Rejects CA Certificates Missing the Key Usage Extension

Summary

Python 3.13 enables the VERIFY_X509_STRICT flag by default in ssl.create_default_context(). This enforces RFC 5280, which requires CA certificates to include the Key Usage X.509v3 extension with at least keyCertSign and cRLSign. Any CA certificate that omits this extension is now rejected at the TLS handshake with:

jansanchez

Install wine in linux

Type in your terminal

sudo apt-get install wine

sudo apt-get install winetricks

k16shikano

name	cognitive-rhythm-writing
description	説明的な文章に緩急を設計するための規範。緩急を装飾ではなく認知モードの切替（観察→逡巡→断定→再観察）と未回収の緊張の管理として扱い、文の拍、段落の密度波形、節の入り方、緩みと駄文の判別、執筆後の機械的な点検手順を定める。読み物として読ませたい章・記事・解説文を生成するとき、または「密度はあるが平坦でおもしろくない」文章を診断・修正するときに使用する。

認知リズムを生むための日本語ライティング規範

密度の高い文章が退屈になるのは、情報が多いからではなく、全文が同じ認知モードで書かれているからである。 この規範は、読者の認知モード（観察する、迷う、確信する、確かめ直す）を意図的に切り替え、常に「続きを読む理由」を維持することで、読み進める推進力を作る。