olafhartong

CLSID,ClassName
{0000031A-0000-0000-C000-000000000046},CLSID
{0000002F-0000-0000-C000-000000000046},CLSID CLSID_RecordInfo
{00000100-0000-0010-8000-00AA006D2EA4},CLSID DAO.DBEngine.36
{00000101-0000-0010-8000-00AA006D2EA4},CLSID DAO.PrivateDBEngine.36
{00000103-0000-0010-8000-00AA006D2EA4},CLSID DAO.TableDef.36
{00000104-0000-0010-8000-00AA006D2EA4},CLSID DAO.Field.36
{00000105-0000-0010-8000-00AA006D2EA4},CLSID DAO.Index.36
{00000106-0000-0010-8000-00AA006D2EA4},CLSID DAO.Group.36
{00000107-0000-0010-8000-00AA006D2EA4},CLSID DAO.User.36

loggedbytucker-blip

Raspberry Pi 5 Media Server — Commands

Immich (photos) + Jellyfin (movies/TV) + Tailscale (access from anywhere). Commands only, in the order they're run in the video.

Tested August 2026 on:

Hardware	Raspberry Pi 5, 8GB — CanaKit Starter Kit PRO

linbaiwpi

sudo apt-get install fcitx im-config
im-config ## choose fcitx

sudo apt-get install fcitx-googlepinyin

## choose fcitx keyboard icon, choose "Text Entry Setting"
## in the opned windows, click "+" icon
## search "pinyin" and Google Pinin" will come out

## if cannot not be used immediately, log out once

aw-junaid

Penetration Testing & Red Team Operations

From Ethical Hacking to Advanced Adversary Emulation

• PART I – FOUNDATIONS OF OFFENSIVE SECURITY

  • Chapter 1: Introduction to Penetration Testing

    • 1.1 History of Ethical Hacking
    • 1.2 Threat Landscape Evolution
    • 1.3 Penetration Testing vs Red Teaming vs Bug Bounty

• 1.4 Types of Penetration Tests

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.

NTheDrone-come

return(function(...)local Q={"\069\043\065\117\101\109\082\085","\052\055\043\086\067\112\068\102\115\057\114\053\069\115\078\051\100\051\098\104\088\050\061\061";"\069\074\109\065\078\048\061\061";"\107\113\109\112\098\051\121\088\076\083\048\051\117\120\098\052\068\053\098\055\070\073\109\083\069\048\061\061";"\103\051\110\077\071\117\080\070";"\088\054\074\078\086\099\061\061";"\113\117\101\054\048\089\112\043","\048\075\048\119\050\098\082\098\117\053\053\081\082\099\061\061";"\111\113\088\084\100\076\088\084\090\113\104\082\069\107\114\086\066\076\078\061";"\077\080\104\083\043\082\122\114","\069\117\098\104";"\113\049\082\052\102\085\101\111\078\108\081\061","\078\072\089\101\078\053\101\053\066\079\047\108\085\113\053\108\068\072\085\061";"\084\116\100\089\109\050\061\061","\081\068\085\117\115\116\104\089\113\099\061\061","\067\043\068\116\070\110\074\121\110\085\061\061";"\056\087\099\109\111\121\111\109\122\121\089\108\088\073\056\070","\106\106\057\105","\055\097\077\089";"\055\050\106\049\048\069\

dbilanoski

sergiecode

Instalaciones necesarias para curso de SQL:

• DBNGIN
• TABLE PLUS
• DOCKER

Utilizaremos en la práctica avanzada:

• GOOGLE CHROME (NAVEGADOR)
• VISUAL STUDIO CODE (Editor Código)