Klerith

Instalar dependencias Testing

1. Vitest

npm install --save-dev vitest jsdom

2. React Testing Library

Krazybug

#!/usr/bin/env python3

'''
calisuck: index, filter-out smartly and download ebooks from Calibre open directories

Installation:

    You need python 3.5 installed

    Download the file as a zip and unzip-it and get into the dir

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.

gemmadlou

Develop WordPress as a Modern PHP Project with Composer

WordPress is popular because it's easy to setup without much technical know-how. However, to build a more robust PHP project with command line deployments, updates and ongoing maintenance, working with WordPress out-of-the-box raises specific challenges:

• How can we make our WordPress projects portable between developers?

jberkus

WITH table_scans as (
    SELECT relid,
        tables.idx_scan + tables.seq_scan as all_scans,
        ( tables.n_tup_ins + tables.n_tup_upd + tables.n_tup_del ) as writes,
                pg_relation_size(relid) as table_size
        FROM pg_stat_user_tables as tables
),
all_writes as (
    SELECT sum(writes) as total_writes
    FROM table_scans

thilojaeggi

#EXTM3U
#EXTINF:0 tvg-name="SRF1 HD" tvg-language="chde" tvg-country="ch" tvg-id="1010" tvg-logo="https://tv7api2.tv.init7.net/media/logos/SRF1HD_300x300px.png" group-title="",SRF1 HD
https://tv7api2.tv.init7.net/api/live/?channel=5620aa6c-3349-44bc-91eb-dfb08e70634f
#EXTINF:0 tvg-name="SRFzwei HD" tvg-language="chde" tvg-country="ch" tvg-id="1012" tvg-logo="https://tv7api2.tv.init7.net/media/logos/srf-zwei-hd-neu.png" group-title="",SRFzwei HD
https://tv7api2.tv.init7.net/api/live/?channel=0d838fbe-c285-4cc9-8f94-c1b1d11ea87c
#EXTINF:0 tvg-name="SRF Info HD" tvg-language="chde" tvg-country="ch" tvg-id="1015" tvg-logo="https://tv7api2.tv.init7.net/media/logos/srf-info-hd-neu.png" group-title="",SRF Info HD
https://tv7api2.tv.init7.net/api/live/?channel=9f30a91d-7f1a-4307-964a-31e19ca28d7e
#EXTINF:0 tvg-name="blue Zoom D" tvg-language="chde" tvg-country="ch" tvg-id="1020" tvg-logo="https://tv7api2.tv.init7.net/media/logos/Zoom_D.jpg" group-title="",blue Zoom D
https://tv7api2.tv.init7.net/api/live/?channel=35e

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