Jonarod

1. Create an hetzner server using Ubuntu
2. Go to the Hetzner's Server dashboard > Images
3. Click on "Mount" over the alpine-linux-extended.iso image
4. Shutdown the server
5. Start the server
6. Click the "Console" icon from the dashboard to open an interactive terminal session
7. Login is root
8. Configure the interface using the command setup-interfaces
9. Pick to setup default eth0
10. Custom config: no

br3ndonland

Getting the Gist of GitHub Actions

Tutorial and tips for GitHub Actions workflows

Table of Contents

• Introduction
• Workflows and actions

wlib

MIT License

Copyright (c) 2021 Daniel Ethridge

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

travisbrown

936815461303508992

	Twitter ID	Screen name	Created	Status	Follower count
	1042206142506655744	jessicakevibes	2018-09-19		1983657

devinschumacher

title	The Best Cloud GPU Providers for Artificial Intelligence & Machine Learning
tags	cloud gpu providers cloud gpu artificial intelligence

Cloud GPUs: Servers, Providers & Everything You Would Ever Need

ice09

//usr/bin/env jbang
//JAVA 21
//DEPS com.openai:openai-java:1.6.0

import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.responses.ResponseCreateParams;
import com.openai.models.responses.ResponseOutputText;

import java.io.IOException;

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.