Klerith

Instalaciones recomendadas - Curso de Nest.js

• Visual Studio Code

• Postman

nakamuraos

#!/bin/bash
set -euo pipefail

# Author: NakamuraOS <https://github.com/nakamuraos>
# Latest update: 03/19/2025
# Tested with Navicat 15.x, 16.x, and 17.x on Debian and Ubuntu.

BGRED="\e[1;97;41m"
ENDCOLOR="\e[0m"

emschwartz

This is an OPML version of the HN Popularity Contest results for 2025, for importing into RSS feed readers.

Plug: if you want to find content related to your interests from thousands of obscure blogs and noisy sources like HN Newest, check out Scour. It's a free, personalized content feed I work on where you define your interests in your own words and it ranks content based on how closely related it is to those topics.

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.

jborean93

# Copyright: (c) 2018, Jordan Borean (@jborean93) <jborean93@gmail.com>
# MIT License (see LICENSE or https://opensource.org/licenses/MIT)

Add-Type -TypeDefinition @'
using Microsoft.Win32.SafeHandles;
using System;
using System.Collections.Generic;
using System.Runtime.ConstrainedExecution;
using System.Runtime.InteropServices;

Pusnow

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.

JohnJocoo

Handover — Illustrated Floorplan Dashboard for Home Assistant

Who this is for: an AI agent (or engineer) helping someone build an illustrated, animated floorplan dashboard for Home Assistant from scratch, with their own house, their own illustrations, their own sensors.

How to read it: it is written as questions and answers. Find the question the user is really asking and answer from that section. The numbers, room names and entity IDs here are placeholders — every one of them will be different in a new build. What transfers is the method: the canvas rule, the file naming scheme,

karpathy

"""
The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
This file is the complete algorithm.
Everything else is just efficiency.

@karpathy
"""

import os       # os.path.exists
import math     # math.log, math.exp

Kartones

PSQL Cheatsheet

Magic words:

psql -U postgres

Some interesting flags (to see all, use -h or --help depending on your psql version):

• -E: will describe the underlaying queries of the \ commands (cool for learning!)
• -l: psql will list all databases and then exit (useful if the user you connect with doesn't has a default database, like at AWS RDS)