VincentAntoine

WITH chunks AS (
    SELECT 
        ht.schema_name AS hypertable_schema,
        ht.table_name AS hypertable_name,
        ck.table_name AS chunk_name
    FROM _timescaledb_catalog.hypertable ht
    JOIN _timescaledb_catalog.chunk ck
    ON ht.id = ck.hypertable_id
),

vee1e

Extend FLOSS to use the rendering techniques pioneered by QUANTUMSTRAND

Vladkarok

The author of the original article - https://t.me/Pryslonskyi
All rights belongs to him. Here is only .md adaptation and translation.

Table of contents

• Introduction
• Starting point
• Preliminary Setup
• Multi-WAN configuration
• Configuring ISP connections and implementing routing based on marks

mndambuki

@echo off
title Activate Microsoft Office 2019 !
cls
echo ============================================================================
echo #Project: Activating Microsoft software products
echo ============================================================================
echo.
echo #Supported products:
echo - Microsoft Office Standard 2019
echo - Microsoft Office Professional Plus 2019

greghaskins

Enable Microsft Entra SSO in Firefox on macOS

Microsoft Intune / "Company Portal" integrates with the macOS system-wide SSO mechanism. Beyond just single-sign-on, this also helps the Microsoft-hosted websites (such as SharePoint and OneDrive) verify that the device is "trusted" so you don't get the annoying non-compliance banner:

Your organization doesn't allow you to download, print, or sync using this device

In Microsoft Edge (unsuprisingly) and Safari, this "just works." In Chrome, you can use the official browser extension. Firefox requires a policy settings change that isn't obvious since it's not in the UI.

Details

machida

カレーのレシピ

誰でもできる、美味しいカレーの作り方です。旦那も息子もこのカレーが大好物。
ポイントは玉ねぎと人参はミキサーで細かくしてしまうところ。逆にコクのある美味しいカレーになります。

レシピの作者

山田サチ子さん

材料 （ 8皿分 ）

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.

iscgar

#!/usr/bin/sudo ruby

# 
# revealer.rb -- Deobfuscate GHE .rb files.
# 
# This is simple:
# Every obfuscated file in the GHE VM contains the following code:
# 
# > require "ruby_concealer.so"
# > __ruby_concealer__ "..."