jnsahaj

name	code-refactor-review
description	Reviews code changes for reuse, composition, codebase consistency, and slop. Use when asked to review PRs/diffs, check code reuse, composition, cleanliness, or whether code fits the codebase.

Code Refactor Review

Review code changes the way Sahaj usually asks for review: go deep on reuse, composition, codebase consistency, and anything that reads like slop.

First Pass

hediet

type StringBool = "true"|"false";


interface AnyNumber { prev?: any, isZero: StringBool };
interface PositiveNumber { prev: any, isZero: "false" };

type IsZero<TNumber extends AnyNumber> = TNumber["isZero"];
type Next<TNumber extends AnyNumber> = { prev: TNumber, isZero: "false" };
type Prev<TNumber extends PositiveNumber> = TNumber["prev"];

mattppal

Frontend Security

	Security Measure	Description
☐	Use HTTPS everywhere	Prevents basic eavesdropping and man-in-the-middle attacks
☐	Input validation and sanitization	Prevents XSS attacks by validating all user inputs
☐	Don't store sensitive data in the browser	No secrets in localStorage or client-side code
☐	CSRF protection	Implement anti-CSRF tokens for forms and state-changing requests
☐	Never expose API keys in frontend	API credentials should always remain server-side

shawwwn

# µPing (MicroPing) for MicroPython
# copyright (c) 2018 Shawwwn <shawwwn1@gmail.com>
# License: MIT

# Internet Checksum Algorithm
# Author: Olav Morken
# https://github.com/olavmrk/python-ping/blob/master/ping.py
# @data: bytes
def checksum(data):
    if len(data) & 0x1: # Odd number of bytes

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.

MuYouGuanX

使用JAV金鸡儿奖官网附带的工具JAV SQL 查询器，可查询各种类别的JavDB TOP250影片：

• JavDB TOP250
• JavDB 有码 TOP250
• JavDB 无码 TOP250
• JavDB 欧美 TOP250
• JavDB FC2 TOP250

及分年数据（存在部分重复影片，原始数据的问题）：

eyecatchup

// Generates the SAPISIDHASH token Google uses in the Authorization header of some API requests
async function getSApiSidHash(SAPISID, origin) {
    function sha1(str) {
      return window.crypto.subtle.digest("SHA-1", new TextEncoder("utf-8").encode(str)).then(buf => {
        return Array.prototype.map.call(new Uint8Array(buf), x=>(('00'+x.toString(16)).slice(-2))).join('');
      });
    }
    
    const TIMESTAMP_MS = Date.now();
    const digest = await sha1(`${TIMESTAMP_MS} ${SAPISID} ${origin}`);

sunmeat

class Person:
    def __init__(self, name, surname=""):
        self.name = name
        self.surname = surname


class Cat:
    def __init__(self, nick, suit=""):
        self.nick = nick
        self.suit = suit # порода

sunmeat

class Monster:
    # це і є "статичне поле" (атрибут класу)
    count = 0

    def __init__(self, health=100, attack=10, mana=50):
        # ініціалізація екземплярних (звичайних) атрибутів
        self.health = health
        self.attack = attack
        self.mana = mana