BumgeunSong

name	crig-concept-learning
description	Helps build deep understanding of any concept by scaffolding its structure (rigging) and crystallizing it into a seed sentence. Use when learning something new, feeling stuck while understanding a concept, or wanting to truly own an idea. Triggered by phrases like "이해하고 싶어", "그려지지 않아", "왜 필요해?", "내 것으로 만들고 싶어", "정리해봐", "핵심이 뭐야?".
author	Jong Taek Oh

Crig (Crystal Rigging)

씨앗: Crig은 개념의 골격을 세워 머릿속에서 돌릴 수 있게 하고, 그것을 다시 씨앗으로 굳히는 것이다.

linearuncle

请阅读当前项目，为我制作一个可交互的“项目架构与运行流程地图”，帮助我理解这个项目现在实际如何运转。

项目不断迭代，我可能已经失去对整体结构的把握。请从真实代码、配置和现有资料出发，梳理主要模块、业务对象、依赖关系，以及关键操作发生后的完整流程。

不要只输出文字报告；请实现一个可以打开并探索的可视化成果。

一、先理解项目

1. 识别项目的主要入口、核心模块、数据存储、外部服务和后台任务；只包含项目实际存在的部分。
2. 说明每个核心模块的职责，以及它与其他模块的关系。

mcandre

Clear Homebrew cache

$ brew cleanup --prune=all

Update Casks

Once the cache is cleared, brew cask will see the latest versions of all Casks.

lanfon72

#!/usr/bin/env python
#coding:UTF-8
import requests, re, json, os
from datetime import datetime
os.environ['TZ'] = 'ROC'
html = requests.get('http://www6.vghtpe.gov.tw/ERREALIFO/ERREALIFO.jsp')
html.encoding='big5'

pending = re.findall(u'">?(\w+)</font>',html.text)
full_reported = re.findall(u'體">?(.?)</font>',html.text)[0]

t0mst0ne

#!/usr/bin/env ptyhon
#coding:UTF-8
import requests, json, os, re
from datetime import datetime
os.environ['TZ'] = 'ROC'
html = requests.get('http://www.scmh.org.tw/FirstAid.aspx?Kind=2')

reported = re.findall(u'(.*119.*)',html.text)
full_reported = False if u'未' in reported[1] else True

lancetw

# !/usr/bin/env python
# coding:UTF-8

import requests, json, re
from datetime import datetime

html = requests.get('http://reg.ntuh.gov.tw/EmgInfoBoard/NTUHEmgInfoT4.aspx', verify=False)
keys = ['pending_doctor', 'pending_ward', 'pending_icu', 'pending_bed']

pending0 = re.findall(r"<td(.*?)>(.+?)</td>", html.text)

sabival89

vSphere 6 Enterprise Plus:
1C20K-4Z214-H84U1-T92EP-92838
1A2JU-DEH12-48460-CT956-AC84D
MC28R-4L006-484D1-VV8NK-C7R58
5C6TK-4C39J-48E00-PH0XH-828Q4
4A4X0-69HE3-M8548-6L1QK-1Y240

vSphere with Operations Management 6 Enterprise:
4Y2NU-4Z301-085C8-M18EP-2K8M8
1Y48R-0EJEK-084R0-GK9XM-23R52

lanfon72

#!/usr/bin/env python
# coding: utf-8

import re
import os
import json
from datetime import datetime

import requests

wonseokjung

AI로만 운영되는 1인 기업 만들기 — 프롬프트 전문 (무료)

영상: 「JEV 같은 AI, 내 컴퓨터에서 0원으로 돌립니다 | AI 1인 기업 만들기 (클로드 vs GPT vs 제미나이)」 만든 사람: AI 멘토 제이 · CONNECT AI LAB (https://www.youtube.com/@CONNECT-AI-LAB)

준비 (운영비 0원)

1. 올라마 0.35 이상 설치: https://ollama.com/download
2. 터미널: ollama pull tev1:0.8b
3. 확인: ollama list 에 tev1:0.8b 가 보이면 성공

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.