LeoNatan

csrutil disable
sudo nvram boot-args="intcoproc_unrestricted=1 kext-dev-mode=1 amfi_allow_any_signature=1 amfi_unrestrict_task_for_pid=1 PE_i_can_has_debugger=1 cs_enforcement_disable=1 amfi_get_out_of_my_way=1 amfi=0xff cs_debug=1 ipc_control_port_options=0"
sudo spctl --global-disable
sudo defaults write /Library/Preferences/com.apple.security GKAutoRearm -bool NO
sudo defaults write /Library/Preferences/com.apple.security.coderequirements Entitlements -string always
sudo defaults write /Library/Preferences/com.apple.security.coderequirements AllowUnsafeDynamicLinking -bool YES
sudo defaults write /Library/Preferences/com.apple.security.libraryvalidation.plist DisableLibraryValidation -bool YES
defaults write com.apple.loginwindow DisableScreenLockImmediate -bool yes

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.

illuminatianon

Z‑Image Turbo is a bit different from “classic” Stable Diffusion, so a lot of old prompting habits don’t quite apply. I’ll walk through how to prompt it deeply and safely, with special focus on controlling content (nudity, stereotypes, unwanted artifacts) even though the model does not support traditional negative prompts at all. ([Hugging Face][1])

---

1. How Z‑Image Turbo thinks (and why “negative” prompts don’t work)

Key facts that matter for prompting:

qoomon

Conventional Commit Messages

See how a minor change to your commit message style can make a difference.

git commit -m"<type>(<optional scope>): <description>" \
  -m"<optional body>" \
  -m"<optional footer>"

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:

soiqualang

http://giavang.doji.vn/

https://tygia.com/api

https://tygia.com/api.php?column=1&title=0&chart=0&gold=1&rate=0&expand=2&color=284C84&nganhang=VIETCOM&fontsize=80&change=0#charts

https://tygia.com/json.php?ran=0&rate=0&gold=1&bank=VIETCOM&date=now

https://tygia.com/api.php?column=1&title=0&chart=0&gold=1&rate=0&expand=2&color=284C84&nganhang=VIETCOM&fontsize=80&change=0#charts

Sachin-A

Argon2

• Argon2 is a key derivation function that was selected as the winner of the Password Hashing Competition in July 2015. It was designed by Alex Biryukov, Daniel Dinu, and Dmitry Khovratovich from University of Luxembourg.
• It uses the BLAKE2 hash algorithm to securely scramble input data (password and salt).

Introduction

Problems with existing designs

• Should the memory addressing (indexing functions) be input-independent or input-dependent, or hybrid?
• Is it better to fill more memory but suffer from time-space tradeoffs, or make more passes over the memory to be more robust?
• How should the input-independent addresses be computed? Several seemingly secure options have been attacked.

SecureAgentTools

Agent Card Schema Reference

Overview

The Agent Card is a JSON document that serves as the passport for autonomous agents in the AgentVault ecosystem. It provides essential metadata about an agent, including its capabilities, authentication requirements, available skills, and endpoint information. Think of it as a machine-readable business card that enables automatic discovery, authentication, and interaction between agents.

The Agent Card follows the A2A (Agent-to-Agent) protocol specifications and enables:

• Automatic Discovery: Agents can be discovered and understood by other agents
• Authentication Negotiation: Clients can determine which authentication scheme to use
• Capability Verification: Clients can understand what an agent can do before connecting