boringmarketer

The Direct Response Copy Skill

Write copy that converts. Landing pages, emails, sales copy, headlines, CTAs, social posts — anything persuasive.

This is an AI skill file. It turns any AI into a direct response copywriter trained on the frameworks of Schwartz, Hopkins, Ogilvy, Halbert, Caples, Sugarman, and Collier. Instead of getting generic AI copy, you get internet-native writing that sounds like a smart friend explaining something — while quietly deploying every persuasion principle in the book.

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How to use this

gone

import requests

cookies = dict(CMSESSION="YOUR SESSION TOKEN HERE")

for x in range(150):
    incoming = requests.get('http://creativemarket.com/op/download/%s' % x, cookies=cookies)
    if 'content-disposition' not in incoming.headers:
        print "%s not a valid file" % x
        continue
    file_name = incoming.headers['content-disposition'].split("\"")[1]

mmozeiko

This downloads standalone MSVC compiler, linker & other tools, also headers/libraries from Windows SDK into portable folder, without installing Visual Studio. Has bare minimum components - no UWP/Store/WindowsRT stuff, just files & tools for native desktop app development.

Run py.exe portable-msvc.py and it will download output into msvc folder. By default it will download latest available MSVC & Windows SDK from newest Visual Studio.

You can list available versions with py.exe portable-msvc.py --show-versions and then pass versions you want with --msvc-version and --sdk-version arguments.

To use cl.exe/link.exe first run setup_TARGET.bat - after that PATH/INCLUDE/LIB env variables will be updated to use all the tools as usual. You can also use clang-cl.exe with these includes & libraries.

To use clang-cl.exe without running setup.bat, pass extra /winsysroot msvc argument (msvc is folder name where output is stored).

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.

craftybones

Software Delivery Project Technology Landscape

A Practical Guide for New Engineers

Introduction

In training, most engineers learn programming languages, algorithms, databases, and software engineering principles. In real-world projects, however, applications are built and operated using an ecosystem of specialized tools and platforms.

This document provides an overview of common technology categories encountered in modern software delivery projects. It is intended as a reference guide to help new engineers understand the purpose of each class of tools and recognize common products used in the industry.

ganeshbabuNN

piyushgarg-dev

Kafka

Video Link: Apache Kafka Crash Course | What is Kafka?

Prerequisite

• Knowledge
  • Node.JS Intermediate level
  • Experience with designing distributed systems
• Tools
  • Node.js: Download Node.JS
  • Docker: Download Docker
  • VsCode: Download VSCode