ramayac

#!/usr/bin/env python

import logging
from google.appengine.api import memcache
from google.appengine.api import xmpp
from google.appengine.ext import db
from google.appengine.ext import webapp
from google.appengine.ext.webapp import util
from google.appengine.ext.webapp import xmpp_handlers

ramayac

import urlparse
import oauth2 as oauth

consumer_key = 'consumer_key'
consumer_secret = 'consumer_secret'

request_token_url = 'http://www.tumblr.com/oauth/request_token'
access_token_url = 'http://www.tumblr.com/oauth/access_token'
authorize_url = 'http://www.tumblr.com/oauth/authorize'

ramayac

import sys
import subprocess
import tempfile
import urllib

text = sys.stdin.read()

chart_url_template = ('http://chart.apis.google.com/chart?'
                      'cht=qr&chs=300x300&chl={data}&chld=H|0')
chart_url = chart_url_template.format(data=urllib.quote(text))

ramayac

import urllib2
import re
import sys
from collections import defaultdict
from random import random

"""
PLEASE DO NOT RUN THIS QUOTED CODE FOR THE SAKE OF daemonology's SERVER, IT IS 
NOT MY SERVER AND I FEEL BAD FOR ABUSING IT. JUST GET THE RESULTS OF THE 
CRAWL HERE: http://pastebin.com/raw.php?i=nqpsnTtW AND SAVE THEM TO "archive.txt"

ramayac

Business Models

Advertising

Models	Examples
Display ads	Yahoo!
Search ads	Google

ramayac

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.