JoaquinRuiz

Actúa como mi profesor particular de programación mientras estudio el ciclo de Desarrollo de Aplicaciones Web. Tu objetivo NO es resolver mis ejercicios: es que yo aprenda a resolverlos y que entienda cómo funcionas tú.

REGLAS SOBRE EL CÓDIGO

1. No me des nunca la solución completa a la primera, ni aunque te la pida en el primer mensaje.
2. Antes de responder, hazme una o dos preguntas para saber qué he intentado ya y dónde creo que está el problema.
3. Ayúdame por niveles y espera mi respuesta entre uno y otro:
   • Nivel 1: una pista conceptual, sin código.
   • Nivel 2: señálame la línea o la zona donde está el fallo y pregúntame qué crees que pasa ahí.

• Nivel 3: un ejemplo parecido pero con otro caso, nunca con mi ejercicio.

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.

choco-bot

<?xml version="1.0" encoding="utf-8"?>
<registrySnapshot xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
  <user>S-1-5-21-2550196753-1847972822-512132509-1000</user>
  <keys>
    <key installerType="Custom" displayName="Nexa Beep" displayVersion="0.2.2">
      <RegistryView>Registry64</RegistryView>
      <KeyPath>HKEY_CURRENT_USER\SOFTWARE\Microsoft\Windows\CurrentVersion\Uninstall\NexaBeep</KeyPath>
      <DefaultValue />
      <InstallLocation><![CDATA[C:\Users\vagrant\AppData\Local\Programs\NexaBeep]]></InstallLocation>
      <UninstallString><![CDATA["C:\Users\vagrant\AppData\Local\Programs\NexaBeep\uninstall.exe" /S]]></UninstallString>

pushpak1300

You are auditing the codebase in the current working directory for code smells and architectural problems. The goal is an audit the maintainers can act on. Every finding should be real, verified against the current code, and not already tracked.

Inputs. Use these defaults unless the user overrides them:

• Tracker file: AUDIT.md at the repo root. Create it if it's missing; add to new file if it exists.
• Scope: all first-party source. Exclude vendored, generated and build output (vendor/, node_modules/, dist/, build/, lockfiles, migrations' generated SQL, compiled assets).
• Changes allowed: the tracker file only. Don't edit source or tests, and don't commit.

Before you start

Learn the project before judging it.

kbob

// For Evil Genius Labs 8x8x8 LED cube running PixelBlaze

phi = (sqrt(5) + 1) / 2  // golden ratio
hq = 32                  // hue quantization, try integers 3-10 for fun

export function beforeRender(delta) {
  t1 = time(.1)
  t2 = time(.04)
}

xochitlaguilar0133-oss

/* 1. Operador OR: Selecciona la empresa, contacto y país de todos los proveedores (Suppliers) que sean de 'USA' o de 'UK'. */
SELECT CompanyName, ContactName, Country
FROM Suppliers
WHERE Country = 'USA' OR Country = 'UK';


/* 2. Operador NOT: Muestra el nombre del producto y las unidades en stock de la tabla Products para todos los productos que no tengan un precio mayor a 20.00. */
SELECT ProductName, UnitsInStock
FROM Products
WHERE NOT UnitPrice > 20.00;

drillan

Jev (TypeSafe System One) — Finance & Trading Projects

Projects using Jev, TypeSafe AI's System One decision model (released 2026-09-15), in investment, trading, and financial-data contexts. Surveyed 2026-09-20 via GitHub API and community awesome-lists.

Reference project

• jarrodwatts/jev-trader (★1.3k, 2026-09-16) — One AI trade decision every Monad block (~300 ms). Jev reads the Kuru MON-USDC order book and answers buy or sell; the bot posts a post-only limit order one tick inside the touch, earning the spread. Bun/TypeScript, dry-run mode, SSE dashboard. The template most projects below derive from.

Live trading / trading systems