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Episode Outline

1. Beta vs. Alpha: Choosing Your Strategy Introduces the foundational distinction between sector-level (beta) and stock-level (alpha) strategies, explains why time horizon and investor experience should determine which approach to use, and establishes patience as the defining competitive advantage in resource markets.

2. Evaluating Producers: Balance Sheet, Management, and Free Cash Flow Examines how to assess large producing companies through debt structure, management continuity, and free-cash-flow discipline — using Franco-Nevada and Freeport-McMoRan as canonical examples of capital-efficient franchises.

3. Royalties and Streams: The Financiers' Edge Defines royalty and streaming instruments as distinct from equity, explains why the model exists and who benefits, and sizes position recommendations by company tier — using Franco-Nevada's Carlin royalty and Wheaton's silver streaming arbitrage as case studies.

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Mike's SaaS Playbook: 5 Apps to $200K MRR

Source: "I Built 3 SaaS Apps to $200K MRR: Here's My Exact Playbook" — Starter Story (YouTube: 67zh8_yiPh4) Interviewee: Mike, founder from Australia Channel: Starter Story (Pat Walls)

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Overview

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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.

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{
  "name": "Youtube url post to linkedin",
  "nodes": [
    {
      "parameters": {
        "formTitle": "Youtube transcribe",
        "formFields": {
          "values": [
            {
              "fieldLabel": "Youtube URL",

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#!/bin/bash
adduser webui
apt update && apt install wget git python3 python3-venv ffmpeg libsm6 libxext6  -y
su webui
cd /home/webui/
git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
cd stable-diffusion-webui/models/Stable-diffusion
wget https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt
cd ../../
sed -i "s/#export COMMANDLINE_ARGS=\"\"/export COMMANDLINE_ARGS=\"--listen --skip-torch-cuda-test --share\"/g" webui-user.sh

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import feedparser
from datetime import datetime, timedelta, timezone
from dateutil import parser

import requests
from bs4 import BeautifulSoup
import re

import urllib.parse

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// ==UserScript==
// @name        OpenAI GPT-2 Detector
// @namespace   https://www.jolibrain.com/demo/openai-gpt2-detector-userscript
// @description Paragraph of text reports the GPT-2 log prob of that text
// @author      Alexandre Girard <alex.girard@jolibrain.com>
// @version     1.2
// @grant       none
// @include     https://en.wikipedia.org/wiki/*
// ==/UserScript==

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arxiv
from requests import sessions
from pprint import pprint
from rocketchat_API.rocketchat import RocketChat
import arxiv
import re

regex = r"^https://arxiv\.org\/.*\/(\d+\.\d+(?:v1)?).*$"

login = ""

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var fs = require('fs');
var moment = require('moment');
var DD = require('deepdetect-js');

const dd = new DD({
  host: 'localhost',
  port: 18104,
  path: '/api/private/predict'
})

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/* The API controller
   Exports 3 methods:
   * post - Creates a new thread
   * list - Returns a list of threads
   * show - Displays a thread and its posts
*/


var Thread = require('../models/thread.js');
var Post = require('../models/post.js');