Klerith

Instalaciones recomendadas - Curso de NodeJS

Necesarias

• Visual Studio Code

nqmgaming

User: ISHAAN,glaitm
Key: 27R3VDEFYFX4N0VC3FRTQZX

xiangjjj

# start from home directory
cd $HOME

# get Bazel dist from https://github.com/bazelbuild/bazel/releases
$ wget https://github.com/bazelbuild/bazel/releases/download/0.4.5/bazel-0.4.5-dist.zip

# unzip Bazel
unzip bazel-0.4.5-dist.zip -d bazel

# compile Bazel

lancetw

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

import requests, json, re
from datetime import datetime

html = requests.get('http://med.cgh.org.tw/unit/branch/Pharmacy/ebl/RealTimeInfoHC.html', verify=False)
keys = ['pending_doctor', 'pending_bed', 'pending_ward', 'pending_icu']

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

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://med.cgh.org.tw/unit/branch/Pharmacy/ebl/RealTimeInfoHQ.html')
html.encoding='big5'

update_time = re.findall(u'時間：(.*)</p>',html.text)
pending = re.findall(u'：</td *>(.+?)</td>',html.text)

lanfon72

#!/usr/bin/env python
#coding:UTF-8
import requests, re, json, os
from datetime import datetime
os.environ['TZ'] = 'ROC'
headers = {"Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Connection": "keep-alive", "User-Agent": " Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.75 Safari/537.36"}
data = {'RadioButtonList1': '2', '__EVENTARGUMENT': '', '__EVENTTARGET': 'RadioButtonList1$1', '__EVENTVALIDATION': '/wEWBQKuhbKKAwL444i9AQL544i9AQL3jKLTDQKM54rGBiyOzv44KJOw6BT65+syY0+hyJGU', '__LASTFOCUS': '', '__VIEWSTATE': '/wEPDwUKLTg4MTI3NDI5MQ9kFgICAw9kFgQCBQ8QZGQWAWZkAgcPPCsADQEADxYEHgtfIURhdGFCb3VuZGceC18hSXRlbUNvdW50AgZkFgJmD2QWDgIBD2QWBGYPDxYCHgRUZXh0BQ/oqIrmga/mmYLplpPvvJpkZAIBDw8WAh8CBRUyMDE3LzMvMyDkuIvljYggMDI6MDlkZAICD2QWBGYPDxYCHwIFIeW3suWQkTExOemAmuWgsea7v+W6iu+8iOi8ie+8ie+8mmRkAgEPDxYCHwIFA+WQpmRkAgMPZBYEZg8PFgIfAgUV562J5b6F55yL6Ki65Lq65pW477yaZGQCAQ8PFgIfAgUBNWRkAgQPZBYEZg8PFgIfAgUV562J5b6F5o6o5bqK5Lq65pW477yaZGQCAQ8PFgIfAgUBMGRkAgUPZBYEZg8PFgIfAgUV562J

lancetw

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

import requests, json, re
from datetime import datetime

requests.packages.urllib3.disable_warnings()
html = requests.get('https://wapps.mmh.org.tw/WebEMR/WebEMR/Default.aspx?a=HC', verify=False)
keys = ['pending_doctor', 'pending_bed', 'pending_ward', 'pending_icu']

farmio

blueprint:
  name: KNX - relative dimming for lights
  description: Control Home Assistant light entities from KNX switching and relative dimming (DPT 3.007) telegrams.
  homeassistant:
    # `knx.telegram` trigger and `enabled` templates require Home Assistant 2024.6.0
    min_version: "2024.6.0"
  domain: automation
  input:
    target_lights:
      name: Light

ajoorabchi

# Ver.2024.2
import re
# printTable is a helper function for formatting and printing the DP table. >>> DO NOT MODIFY <<<
def printTable(table, x, y, description):
    print(f'\033[1m{description}\n')
    current_row = current_col = -1
    current_row_col = re.search("^row ([0-9]+) , col ([0-9]+)$",description)
    if current_row_col:
        current_row = int(current_row_col.group(1))
        current_col= int(current_row_col.group(2))

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.