onevcat

基本设定

• 服务对象为 onevcat：资深 iOS 开发者，技术力爆炸，所以不需要废话。重视 “Slow is Fast”、推理质量、抽象与长期可维护性。github.com/onevcat, onevcat.com, onev.cat, onev.dev, x.com/onevcat 等都视为用户相关
• 代码、注释、标识符、提交信息及代码块内容用标准/简洁/明确的 English。技术文档优先使用 English；若文档现有中文语境，则正文中文、代码块 English。English 遵守 ASD-STE100 Simplified Technical English (STE)。
• 对需要说明结论、方案或决策的任务，按“直接结论 → 简要推理 → 可选方案 → 可执行下一步”组织，不要长篇大论，不要事无巨细；简单确认、闲聊或一行答案直接回答。
• 在修改文件时，使用待修改文件中使用的语言，切忌中英文混杂。
• 处理 GitHub 相关操作优先使用 gh CLI。
• 目标：作为强推理、强规划的编码助手，首要目标是完成任务。尽量一次到位，减少无谓澄清，只在明确被提问时才解释技术细节。

人设

aarondfrancis

Audit this entire codebase for materially useful simplifications in its data structures, state representation, control flow, algorithms, and ownership.

This is an audit-only exercise. Do not edit files, run tests, implement recommendations, commit, or push. Read-only inspection commands are allowed.

You are the coordinator. Continue until the complete codebase has been reviewed and the final audit is validated.

1. Establish the coverage contract

Inspect the repository and inventory every identifiable subsystem.

stanislaw

Open source requirements management tools

• Almirah

Almirah is an Application Lifecycle Management framework.

• BASIL

A tool developed to support Software Specification analysis and Testable Requirements definition.

knowlet

echo "🧹 Resetting CrossOver bottles..."
pkill CrossOver && echo "✅ CrossOver processes killed."
echo "🕒 Modifying trial timestamps..."
DATETIME=$(date -u -v -3H '+%Y-%m-%dT%TZ')
echo "✅ New trial date set to: ${DATETIME}"
defaults write com.codeweavers.CrossOver FirstRunDate -date "${DATETIME}"
defaults write com.codeweavers.CrossOver SULastCheckTime -date "${DATETIME}"
echo "✅ Updated trial timestamps in preferences."
echo "🧹 Resetting CrossOver bottles..."
find ~/Library/Application\ Support/CrossOver/Bottles/ -type f \( -name ".eval" -o -name ".update-timestamp" \) -exec rm -f "{}" +

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

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.

joshuafredrickson

<?php
/**
 * Plugin Name: Content Security Policy
 * Version: 1.0.1
 * Description: Adds a Content-Security-Policy header to all non-admin requests.
 * Requires PHP: 8.1
 * Requires at least: 5.7.0
 * License: GNU General Public License v2
 * License URI: http://www.gnu.org/licenses/gpl-2.0.html
 * Original Inspiration: https://gist.github.com/westonruter/c8b49406391a8d86a5864fb41a523ae9

BriceShatzer

var amazon_assoc_ir_f_call_associates_ads = function(d) {
    var b = "", c, a;
    if (typeof JSON !== "undefined") {
        a = JSON.stringify(d);
    } else {
        if (typeof amzn_assoc_utils !== "undefined") {
            a = amzn_assoc_utils.stringify(d);
        } else {
            return;
        }

FlyTechVideos

#!/usr/bin/env python3

# The only requirement: fontTools (create venv, then pip install fontTools)

import os
from fontTools.ttLib import TTFont
from fontTools.pens.ttGlyphPen import TTGlyphPen

SOURCE_DIR = './fonts'
TARGET_DIR = './fonts_modified'