dmmulroy

Below is the Effect 4 shape I’d create for a short top-level mlink CLI.

Commands

No links prefix:

  mlink health
  mlink list [--q] [--status active|tombstoned|all] [--kind alias|generated|all] [--limit] [--cursor] [--all]
  mlink get <slug>

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.

jaraco

 @ docker run -it jaraco/multipy-tox
  # import os
... def is_docker():
...     def text_in_file(text, filename):
...         try:
...             with open(filename, encoding='utf-8') as lines:
...                 return any(text in line for line in lines)
...         except OSError:
...             return False
...     cgroup = '/proc/self/cgroup'

coolaj86

Chat GPT "DAN" (and other "Jailbreaks")

• https://chat.openai.com/
• Is ChatGPT "DAN" Real? Gonna find out [Part 1]
  (https://www.youtube.com/watch?v=-q8woRG9FrI)
• Part 2: I thought ChatGPT DAN was a hoax, but...
  (https://www.youtube.com/watch?v=rHZRrDu3A2U&lc=UgxfrxX8aK7gnCzkend4AaABAg)

Abhishekrajpurohit

{
  "name": "WhatsApp AI Bot — 3 Varieties",
  "nodes": [
    {
      "parameters": {
        "httpMethod": "POST",
        "path": "wa-webhook",
        "options": {}
      },

iliion

 #!/bin/sh

set -e

# Install dependencies.
sudo apt install -y curl apt-transport-https \
software-properties-common ca-certificates

# Install docker.
curl -fsSL https://download.docker.com/linux/debian/gpg | sudo apt-key add -

MangaD

From CS101 to Expertise: The Hidden Structure of Computer Science Education

Disclaimer: ChatGPT generated document.

If you’ve ever seen names like CS101, CS102, or CS301, you’ve already encountered a quiet but powerful system that shapes how knowledge is taught in modern universities.

At first glance, these look like arbitrary course codes. In reality, they encode an entire philosophy of learning — one that spans centuries of academic history and defines how computer science (and most disciplines) are taught today.

MangaD

🧠 A Deep Dive into Debuggers: Names, Philosophy, and Design Trade-offs

Disclaimer: ChatGPT generated document.

Modern systems programming—especially in C and C++—relies heavily on powerful debugging and profiling tools. Names like gdb, lldb, perf, valgrind, and rr come up constantly, but they represent very different philosophies and approaches to understanding program behavior.

This article gives you a complete, structured overview: