marquesds

#include<stdlib.h>
#include<stdio.h>
#include<string.h>

int main(){

char *buffer = NULL;
char palavra;

printf("Alocando memoria\n");

kwmiebach

Usage

(Create a symlink pytest for py.test)

pytest [options] [file_or_dir] [file_or_dir] ...

Help:

fancellu

import java.util

import org.apache.kafka.clients.consumer.KafkaConsumer

import scala.collection.JavaConverters._

object ConsumerExample extends App {

  import java.util.Properties

dogrocker

Oh my zsh.

Install with curl

sh -c "$(curl -fsSL https://raw.githubusercontent.com/robbyrussell/oh-my-zsh/master/tools/install.sh)"

Enabling Plugins (zsh-autosuggestions & zsh-syntax-highlighting)

• Download zsh-autosuggestions by

wojteklu

Code is clean if it can be understood easily – by everyone on the team. Clean code can be read and enhanced by a developer other than its original author. With understandability comes readability, changeability, extensibility and maintainability.

---

General rules

1. Follow standard conventions.
2. Keep it simple stupid. Simpler is always better. Reduce complexity as much as possible.
3. Boy scout rule. Leave the campground cleaner than you found it.
4. Always find root cause. Always look for the root cause of a problem.

Design rules

marwei

Kafka 0.11.0.0 (Confluent 3.3.0) added support to manipulate offsets for a consumer group via cli kafka-consumer-groups command.

1. List the topics to which the group is subscribed

kafka-consumer-groups --bootstrap-server <kafkahost:port> --group <group_id> --describe

Note the values under "CURRENT-OFFSET" and "LOG-END-OFFSET". "CURRENT-OFFSET" is the offset where this consumer group is currently at in each of the partitions.

2. Reset the consumer offset for a topic (preview)

jiffle

Tricks and tips for using Strikt assertions

Strikt is very powerful, however it is sometimes hard to find particular methods. This is a handy cheatsheet.

Basics

Top-level assertions

expect, expectThat, expectCatching and expectThrows

lalizita

package main

import (
	"fmt"
	"os"
	"time"
)

func timer() {
	// Cria um ticker, um relógio que retorna um channel com o tempo atual,

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.