justinrmiller

# Generate / Update a Work Log

You are generating (or updating) a **Work Log** — an evergreen summary of one person's
engineering work, sourced from GitHub pull requests, Linear tickets, and Notion docs.
It is both **thematic** (high-level themes) and **chronological** (a month-by-month
timeline). Follow this spec.

## Configuration

Fill these in before running:

cloudy44-a

const M3U_CONTENT = `#EXTM3U
#EXTINF:-1 group-title="FIFA World Cup 2026",FOX 01
http://89.187.179.148:826/anto.j/c9yJDcXyPe/109548
#EXTINF:-1 group-title="FIFA World Cup 2026",FOX 02
http://89.187.179.148:826/anto.j/c9yJDcXyPe/109511
#EXTINF:-1 group-title="FIFA World Cup 2026",FOX 03
http://stream.cammonitorplus.net/1752/index.m3u8
#EXTINF:-1 group-title="FIFA World Cup 2026",FS1 01
http://89.187.179.148:826/anto.j/c9yJDcXyPe/120160
#EXTINF:-1 group-title="FIFA World Cup 2026",FS1 02

jbsulli

Demo:

Spoiler warning

Spoiler text. Note that it's important to have a space after the summary tag. You should be able to write any markdown you want inside the <details> tag... just make sure you close <details> afterward.

console.log("I'm a code block!");

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.

dmitry-osin

Полная шпаргалка по Kotlin

1. Основы и типы данных

Базовые типы

• Числовые: Byte, Short, Int, Long, Float, Double
• Символьные: Char
• Логические: Boolean
• Строки: String

frankywahl

Netflix

List of genres for Netflix accessible via https//www.netflix.com/browse/genre/<code> where <code> is in one of the following

Kind	Code
Action & Adventure	1365
Action Comedies	43040
Action Sci-Fi & Fantasy	1568
Action Thrillers	43048

aaronjamt

To use this, open the developer console, enable pasting, and copy+paste the script. Open the emoji picker (make sure all the servers you want included are un-collapsed) and scroll to the top. Leave it open and run getEmojis(); in the console. It will scroll through the list, moving down every 750ms. Once it either reaches the bottom, or you're satisfied with the number collected, run finished(); and copy the output. Paste it into a file in an empty folder and execute it as a Linux shell script (/bin/bash or similar). It will create a folder for each server, then download all of their emojis.

Based on https://gist.github.com/junebug12851/8c5c42587a824c22a79359fb56b4023d

meeramnoor16

{
  "name": "MN dental receptionist speed to lead",
  "nodes": [
    {
      "parameters": {
        "httpMethod": "POST",
        "path": "00000000-0000-4000-8000-000000000001",
        "responseMode": "responseNode",
        "options": {}
      },