christopher-baek

Mount VirtualBox VDI

carpasse

import {test} from 'node:test';
import assert from 'node:assert/strict';
import {spawnSync} from 'node:child_process';

const runInFreshNode = (scenario) => {
  const program = `
    const fail = (message, ms = 5) => new Promise((_, reject) => setTimeout(() => reject(new Error(message)), ms));
    const succeed = (value, ms = 5) => new Promise((resolve) => setTimeout(() => resolve(value), ms));

stewartadam

GitHub PR Review Shortcuts

A Tampermonkey userscript for keyboard-first navigation through GitHub pull-request diffs.

Shortcuts

• j: move to the next viewport-sized diff segment
• k: move to the previous viewport-sized diff segment
• v: toggle the selected file's Viewed state and advance to the next file
• l: load the selected large diff when GitHub defers it

veekaybee

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

Pre-Transformer Models

kauefraga

print("""
  Código nosso que estás em C
  Santificado seja vós, Console
  Venha a nós o vosso array[10]
  E seja feita, sim, {vossa chave}
  Assim no if{} como no else{}
  O for (nosso; de cada dia; nos dai hoje++)
  Debugai as nossas sentenças
  Assim como nós colocamos o ponto e vígula esquecido;
          E não nos deixeis errar identação

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.

Klerith

Claude Code: Guía completa para desarrolladores de software

Para empezar el curso, les dejo una lista de las instalaciones generales que les recomiendo. Recuerden que más adelante tendremos una sección dedicada a la configuración y generalidades de ClaudeCode, estas son configuraciones generales del equipo.

techambient

OpenHelper AI Discontinuation Notice

Thank you for using and supporting OpenHelper AI.

We would like to inform you that OpenHelper AI has discontinued as of October 11, 2026, 00:00 (UTC).

Following the discontinuation of the service, OpenHelper AI will no longer operates. We kindly recommend moving to our other open source AI chatbot project such as TalkVXR.

Upon the termination of the service, all AI model(s) in connection with OpenHelper AI will be deleted without undue delay. However, certain information may be retained for upgrading our TalkVXR models. Any such information will be retained only for the AI model training.