bmaupin

See also:

• Free backend hosting
• https://free-for.dev/#/?id=dbaas

Service	Type	Storage	Limitations
Amazon DynamoDB	⚠️ Proprietary NoSQL	25 GB	⚠️ Payment method required
Amazon RDS	⚠️ Proprietary RDBMS		⚠️ Only free for 1 year
Azure SQL Database	MS SQL Server		⚠️ Only free for 1 year
👉 Clever Cloud	PostgreSQL, MySQL, MongoDB, Redis	256 MB (PostgreSQL)	Max 5 connections (PostgreSQL)

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.

raulandregp

<?php
   /***************************************************************/
   /* PhpCaptcha - A visual and audio CAPTCHA generation library
   
      Software License Agreement (BSD License)
   
      Copyright (C) 2005-2006, Edward Eliot.
      All rights reserved.
      
      Redistribution and use in source and binary forms, with or without

braaaax

<Project ToolsVersion="4.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003">
 <Target Name="Hello">
 <ClassExample />
 </Target>
 <UsingTask
 TaskName="ClassExample"
 TaskFactory="CodeTaskFactory"
 AssemblyFile="C:\Windows\Microsoft.Net\Framework\v4.0.30319\Microsoft.Build.Tasks.v4.0.dll" >
 <Task>
 

joeblau

we want to build a prototype mac app with my airpods pros

For AirPods Pro 3, Apple exposes motion, audio, and heart-rate data to third-party developers, but not every sensor’s raw output.

1. Developer-accessible sensors

Sensor	Developer access	API	Raw data?
Accelerometer (motion)	✅ Yes	Core Motion	Processed
Gyroscope / rotation	✅ Yes	Core Motion	Processed

subfission

# Run the last command as root
sudo !!

# Serve current directory tree at http://$HOSTNAME:8000/
python -m SimpleHTTPServer

# Save a file you edited in vim without the needed permissions
:w !sudo tee %

Anakiev2

How to install the Nvidia legacy driver (340xx or 390xx) on Debian 13 Trixie

This guide will show you how to compile and install the legacy Nvidia driver on Debian. The steps bellow are compatible with both nvidia-legacy-340xx-driver and nvidia-legacy-390xx-driver and can be installed on Debian 13 (Trixie) as well as Debian 12 (Bookworm) or Debian 11 (Bullseye) in both 64-bit and 32-bit version. Execute the lines one by one and wait for every line to finish before proceeding to next. Only the lines starting with sudo should be executed as root. The lines with sudo pbuilder will take a lot of time.

To install the nvidia-legacy-390xx-driver version replace 340xx with 390xx everywhere bellow.
To install both 64-bit and 32-bit libraries for wine/steam you should run Step 2. twice.
It was confirmed that this method also works with the nvidia-tesla-470-driver version.

Step 1. Prepare:

Update, upgrade and install the required packages.

Plok-Code

// ==UserScript==
// @name         Album+cleaner+Maximisation
// @namespace    local
// @version      12.21
// @description  Menu fixe sur toutes les pages (ou l'ancien menu, ou la taille de l'ancien site, en réglage) ; profil fixe avec ses albums et les onglets Collection, Catégories et souhaitées ; onglets et filtres fixes dans Ma collection et Toutes les cartes ; export des étiquettes et extraction des cartes au format Discord ; toutes les pages d'un livre d'un coup d'œil ; tes souhaits mis en valeur ; l'historique de tes échanges avec chaque joueur(euse) ; chances des cartes ; réglages gardés dans le navigateur ; Clean et Largeur à côté du crâne et de la lune ; suit le mode nuit ; clic droit
// @match        https://wiki-pick.com/*
// @grant        none
// @run-at       document-idle
// @noframes
// @updateURL    https://gist.githubusercontent.com/Plok-Code/3305b8d461789c702b60dc3185e7fedd/raw/album.user.js

ThiagoBarradas

NOTE

These are NOT product / license keys that are valid for Windows activation.
These keys only select the edition of Windows to install during setup, but they do not activate or license the installation.

Index

• Windows 11
• RTM Generic Keys

ry

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.