Integ

# coding: utf-8
# py2 origin author lrdcq
# usage python3 unwxapkg.py filename

__author__ = 'Integ: https://github.com./integ'

import sys, os
import struct

class WxapkgFile(object):

Azayzel

#!/usr/bin/env bash
#
# ai-log-grep.sh — search local AI assistant chat logs / session transcripts
# for a sensitive string (e.g. your email) to find where an agent may have
# echoed it into commands, headers, or outbound requests.
#
# Usage:
#   ./ai-log-grep.sh "you@example.com"
#   ./ai-log-grep.sh --context "you@example.com"  # print text around each match
#   ./ai-log-grep.sh --deep "you@example.com"     # also scan $HOME for aider histories (slow)

rigwild

Patch APK

Full tutorial to patch APKs on Android using apk-mitm and APKLab. Support for bundle APKs.

Pull APK

adb shell pm list packages
adb shell pm path <package_name>

lucataco

webos-debloat

One shell script that makes a rooted LG webOS TV lighter and quieter:

• hides bloat tiles and background apps from the launcher
• stops preloading apps the TV keeps in RAM whether or not you open them
• turns off home-screen promotions, promo videos, sports alerts, content recommendations and Live Plus (ACR)

Nothing is deleted and the root filesystem is never touched. It only edits

bpteague

This worked as of Feb 17 2025, on OSX Sonoma 14.7.2, using PyInstaller 6.12.0. And this packaged a very nontrivial app, which you can now download from https://cytoflow.readthedocs.io.

Setup

• Create a developer account with Apple
  • https://developer.apple.com and shell out $99 for a developer account
  • Wait a day or so for your developer account to be activated.
• Download and install XCode from the App Store.
  • open XCode and install all of the command-line tools when it asks.
• Create a certificate signing request (CSR)

• Launch the "Keychain Access" utility

hemantapkh

# https://gist.github.com/aallan/b4bb86db86079509e6159810ae9bd3e4

{
  "OFFICIALLY XEROX": [
    "000000"
  ],
  "SUPERLAN-2U": [
    "000001"
  ],
  "BBN (WAS INTERNAL USAGE ONLY, NO LONGER USED)": [

DocteurSEO

The prompts behind "I Made an 8-Minute Film with Claude Sonnet 5.5"

Honest answer to "what was your prompt?": there was no single prompt. It was a conversation between a director (Ahmed) and Claude Sonnet 5.5 in Claude Code, plus one repeated brief that Claude gave to sub-agents (one agent per scene). Both are here, word for word.

1. The director's messages (translated from French; the originals were in French)

1. Sonnet 5.5 is out, I need a video. One approach is storytelling. Look at my video about Opus 5.5 on the YouTube channel Ahmed T'aide, it had a small success. Look at the comments and tell me which video I could make.

2. OK, let's also look at the comments of this video: <a competitor's Sonnet 5.5 tutorial>

3. Let's go with the one that answers every question. We do storytelling from ZERO frame. We do the same thing again, but this time we add humour, emotion, and above all the prompts and my working method. Start by writing the script, and we'll use ElevenLabs v4.

4. > Let's directly m

dominikmartn

Black October: check your Mac with your coding agent

This now lives in a repo, PRs welcome: https://github.com/dominikmartn/black-october-check

As of 2026-10-23. For people who run AI coding agents (Claude Code, Codex, and similar) on their Mac with full shell access.

How to use it: paste this file into your agent and say: run the checks, report, then ask me before every fix.

Lines marked "What we saw" come from two Macs (macOS 26.6.2 and 27.0.1) on Oct 22 and 23. Everything else comes from the linked sources. Versions change daily, so check them against the sources.

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.

Konamiman

Zenbu: an all-in-one MSX cartridge

Status: design proposal. Nothing has been built yet. This document describes the intended design so that MSX users can judge whether it's worth having and hardware people can judge whether it's worth building. Part numbers, prices and register layouts are proposals and may change during validation and prototyping. Feedback is welcome.

---

Contents

1. Introduction
2. User guide