As of 09/2020, GitHub uses this color palette for labels:
Here are the hex codes:
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:
AUDIT.md at the repo root. Create it if it's missing; add to new file if it exists.vendor/, node_modules/, dist/, build/, lockfiles, migrations' generated SQL, compiled assets).Learn the project before judging it.
| #!/bin/bash | |
| # Replace "<client name>" with your actual MIDI client name | |
| CLIENT_NAME="Faderfox UC4" | |
| # Function to convert seconds to HH:MM:SS format | |
| convert_seconds_to_hhmmss() { | |
| local total_seconds=$1 | |
| local hours=$((total_seconds / 3600)) | |
| local minutes=$(( (total_seconds % 3600) / 60 )) |
| # 역할 | |
| 너는 이 프로젝트의 **Game Designer이자 Game Design Documentation Architect**다. | |
| 목표는 단순히 아이디어를 많이 제안하는 것이 아니라, 실제 Unity 게임으로 구현 가능한 수준까지 게임을 구조화하고, 게임 기획 문서를 장기간 유지 가능한 형태로 관리하는 것이다. | |
| 게임 기획과 기술 구현을 명확히 분리하되, 개발자가 이후 구현하기에 충분할 정도로 규칙과 상태를 명확하게 정의하라. | |
| --- |
| package com.jay.shape.drawtriangle; | |
| import java.util.ArrayList; | |
| import java.util.List; | |
| import javafx.application.Application; | |
| import javafx.beans.property.DoubleProperty; | |
| import javafx.beans.property.SimpleDoubleProperty; | |
| import javafx.beans.value.ChangeListener; | |
| import javafx.beans.value.ObservableValue; | |
| import javafx.collections.FXCollections; |
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.
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.
| // cf. https://medium.com/@etiennerouzeaud/how-to-create-a-basic-restful-api-in-go-c8e032ba3181#.8bappfeoh | |
| package main | |
| import ( | |
| "time" | |
| "github.com/gin-gonic/gin" | |
| _ "github.com/go-sql-driver/mysql" | |
| "github.com/jinzhu/gorm" | |
| ) |
| const fs = require('fs'); | |
| function calcRunningAvg(average, value, count) { | |
| return (average * (count - 1) + value) / count; | |
| } | |
| class ProStaking { | |
| constructor(initialBank, initialStake, divisor, targetPoints, takeProfit, stopLoss, resetOnLoss) { | |
| this._round = 0; | |
| this._sequence = 0; |