elliottminns

# ===========================================
# ZSH Hacks - Dreams of Code
# ===========================================
# Add these to your .zshrc file
# ===========================================

# -------------------------------------------
# 1. Edit Command Buffer
# -------------------------------------------
# Open the current command in your $EDITOR (e.g., neovim)

ashokvarmamatta

🧠 Make Your AI Coding Assistant 500x Smarter

Stop Burning Tokens — Use Knowledge Graphs + MCP to Give AI a Map of Your Code

netj

deepakpk009

{
  "categories": [
    {
      "name": "Movies",
      "videos": [
        {
          "description": "Big Buck Bunny tells the story of a giant rabbit with a heart bigger than himself. When one sunny day three rodents rudely harass him, something snaps... and the rabbit ain't no bunny anymore! In the typical cartoon tradition he prepares the nasty rodents a comical revenge.\n\nLicensed under the Creative Commons Attribution license\nhttp://www.bigbuckbunny.org",
          "sources": [
            "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/BigBuckBunny.mp4"
          ],

farbius

#!/usr/bin/env python
"""
The Matched Filter implemenation in frequency domain 
"""
__author__ = "Aleksei Rostov"
__contact__ = "aleksei.rostov@protonmail.com"
__date__ = "2023/03/23"

import numpy as np
import matplotlib.pyplot as plt

k16shikano

name	japanese-tech-writing
description	日本語の技術文書・書籍原稿の文章規範。整形（一文一行、引用ブロック、脚注、コラム記法）、段落と論証の構成（パラグラフライティング）、論証の厳密さ（ツッコミどころの除去）、読み手の負荷の管理、視点と語り、演出の抑制、LLM っぽい空句の禁止、冗長の排除を定める。日本語で技術書の章、草稿、記事、解説文を書くとき、または推敲・リライトするときに使用する。

日本語技術文書の文章規範

日本語で技術的な原稿（書籍の章、記事、解説文）を書く・推敲するときは、以下の規範に従う。

整形

sickerin

Resume claude after running out of usage limit. For Macos, it uses the automation scripts to resume usage.

For instance if your usage resets at 3pm

./resume_claude.sh "15:00"

IamMohaiminul

#!/bin/zsh

# 1. Close all JetBrains applications
echo "Closing running JetBrains instances..."
jbs=(phpstorm pycharm intellij webstorm datagrip clion goland rider)
for app in "${jbs[@]}"; do
    pkill -fi "$app" 2>/dev/null
done

# 2. Define Base Directories

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.

karpathy

"""
The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
This file is the complete algorithm.
Everything else is just efficiency.

@karpathy
"""

import os       # os.path.exists
import math     # math.log, math.exp