kaiix

{
  "providers": {
    "amazon": {
      "urlPattern": "^https?:\\/\\/(?:[a-z0-9-]+\\.)*?amazon(?:\\.[a-z]{2,}){1,}",
      "rules": [
        "p[fd]_rd_[a-z]*",
        "qid",
        "srs?",
        "__mk_[a-z]{1,3}_[a-z]{1,3}",
        "spIA",

kaiix

#!/usr/bin/env python3
"""Patch an installed @github/copilot CLI so xhigh effort is not filtered out.

Default mode is dry-run. Use --apply to modify files.
"""

from __future__ import annotations

import argparse
import hashlib

kaiix

import inspect
from functools import wraps

import openai


def generate_code(signature, docstring):
    init_prompt = "You are a Python expert who can implement the given function."
    definition = f"def {signature}"
    prompt = f"Read this incomplete Python code:\n```python\n{definition}\n```"

kaiix

#!/bin/bash

set -e

SSH_DIR="${HOME}/.ssh"
SSH_CONFIG_BASE64=`curl -s http://vault.in.ein.plus/keys/ein-ci`

mkdir -p "${SSH_DIR}"
chmod 0700 "${SSH_DIR}"

kaiix

__kubectl_init_completion()
{
    COMPREPLY=()
    _get_comp_words_by_ref "$@" cur prev words cword
}

__kubectl_get_pod()
{
    if declare -F _init_completion >/dev/null 2>&1; then
        _init_completion -s || return

kaiix

import csv
import os
import plistlib


def parse_safari_reading_list_plist():
    plist = plistlib.load(
        open(os.path.expanduser('~/Library/Safari/Bookmarks.plist'), 'rb'))

    items = []

kaiix

Linux NetFilter, IP Tables and Conntrack Diagrams

IPTABLES TABLES and CHAINS

IPTables has the following 4 built-in tables.

1) Filter Table

Filter is default table for iptables. So, if you don’t define you own table, you’ll be using filter table. Iptables’s filter table has the following built-in chains.

kaiix

#include <unistd.h>
#include <stdio.h>
#include <setjmp.h>
#include <signal.h>

jmp_buf j;

void try()
{
    printf("call try\n");

kaiix

EC2 相關設定

記憶體

1. 設定swap（預設為60, 當系統使用到超過40% memory, 就會嘗試使用swap）

   sysctl -w vm.swappiness=0   # from 60 -> 0
   

kaiix

import math
from collections import Counter

def tf(word, doc):
  word_counter = Counter(doc.split())
  return word_counter(word) / sum(word_counter.values())
  
def idf(word, corpus):
  return math.log(len(corpus) / (1 + sum(1 for doc in corpus if word in doc)))