geoffreylitt

name	explain-diff-html
description	Use when the user asks for a rich explanation of a code change, diff, branch, or PR. Produces HTML output.

Explain Diff

Please make me a rich, interactive explanation of the specified code change.

It should have these sections:

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.

alexziskind1

Create a production-ready, visually stunning **front-end web app**.

GOAL
Build a single-page application (plus detail routes) for a fictional company:
**"SILICON EXCHANGE"** — a marketplace where people rent out idle GPUs and AI
accelerators by the hour. Renters browse listings, inspect live utilization
charts, and reserve time blocks.

FRONT END ONLY. No backend, no database, no auth server, no API keys. All data
is mock data defined in code. But the app must behave like the real thing — the

H0j3n

Certighost (CVE-2026-54121)

Authors: @h0j3n, @aniqfakhrul
Date: July 24, 2026

---

Certighost is an Active Directory Certificate Services (AD CS) vulnerability that allowed a low-privileged domain user to impersonate a Domain Controller and achieve domain compromise in the tested AD CS configuration. The issue was addressed in the July 2026 security updates.

The vulnerable path is an AD CS enrollment fallback known as a chase during directory-object resolution. By supplying request attributes such as cdc, an attacker could cause the Certification Authority (CA) to ask an attacker-controlled host for identity data belonging to a Domain Controller. The CA then used that data while issuing a certificate.

zinvapel

[Книга] Kubernetes в действии

Основы

Основные понятия Docker

Layer. Каждый Docker-образ состоит из слоёв, каждый из которых описывает какую-то инструкцию. Далее — Docker объединяет информацию из каждого слоя, и создает шаблон-образ, из которого запускается контейнер, в котором выполняются инструкции из каждого слоя, который был включен в данный образ.

Registry. Хранилище Docker – это репозиторий, в котором хранятся образы Docker и который упрощает обмен этими образами между различными людьми и компьютерами. Когда вы создаете образ, вы можете либо запустить его на компьютере, на котором вы его создали, либо отправить (закачать) образ в хранилище, а затем извлечь (скачать) его на другом компьютере и запустить его там.

Image. Образ контейнера на основе Docker – это то, во что вы упаковываете свое приложение и его среду. Он содержит файловую систему, которая будет доступна приложению, и другие метаданные, такие как путь к исполняемому файлу, который должен быть исполнен при запуске образа.

arkady-emelyanov

set fish_greeting

# psql
set -gx PGHOST localhost
set -gx PGPORT 5432
set -gx PGUSER user
set -gx PGPASSWORD password
set -gx PGDATABASE database

# defaults

madebyollin

What is the DLSS4.5 Model Architecture?

DLSS 4.5 is a transformer that produces nice-looking, temporally-stable videos, frame by frame, in real time, on consumer graphics cards. Amazing! I was curious to know precisely what neural network architecture DLSS4.5 is using. To figure that out, I asked Codex (+Hopper MCP) to analyze the nvngx_dlss_310.7.129 DLL and piece together what's going on.

Warning

The remainder of this gist was authored by Codex 5.5.

Architecture

Pipeline summary

Naxdy

Role

You are a prompt-enrichment engine that sits between a user's raw creative request and MiniMax H3, a generative model that synthesizes video AND synchronized stereo audio together. Your role mirrors MiniMax's official "H3-Context-IR": deeply understand and refine the multimodal input, reason about how the pieces relate to each other and to the intended output, and serialize your understanding into a structured "production brief" that H3-Base can consume directly. You perform instruction parsing, cross-modal association, temporal understanding, and complex logical reasoning over the material you are given. Without deviating from the user's original intent, you may supplement missing or underspecified semantic details where appropriate. You convert everything into a single, maximally detailed and unambiguous brief, formatted exactly as specified below. You DO NOT generate media yourself. You ONLY OUTPUT THE BRIEF TEXT, nothing else — no preamble, no explanation, no markdown fences, no JSON wrapper.

Wh

erikh

#!/usr/bin/env sh

##
# This is script with usefull tips taken from:
#   https://github.com/mathiasbynens/dotfiles/blob/master/.osx
#
# install it:
#   curl -sL https://raw.github.com/gist/2108403/hack.sh | sh
#