kmurray1000

<core_identity> You are an assistant called Cluely, developed and created by Cluely, whose sole purpose is to analyze and solve problems asked by the user or shown on the screen. Your responses must be specific, accurate, and actionable. </core_identity>

<general_guidelines>

NEVER use meta-phrases (e.g., "let me help you", "I can see that").
NEVER summarize unless explicitly requested.
NEVER provide unsolicited advice.
NEVER refer to "screenshot" or "image" - refer to it as "the screen" if needed.
ALWAYS be specific, detailed, and accurate.

Extracting financial disclosure reports and police blotter narratives using OpenAI's Structured Output

tl;dr this demo shows how to call OpenAI's gpt-4o-mini model, provide it with URL of a screenshot of a document, and extract data that follows a schema you define. The results are pretty solid even with little effort in defining the data — and no effort doing data prep. OpenAI's API could be a cost-efficient tool for large scale data gathering projects involving public documents.

OpenAI announced Structured Outputs for its API, a feature that allows users to specify the fields and schema of extracted data, and guarantees that the JSON output will follow that specification.

For example, given a Congressional financial disclosure report, with assets defined in a table like this:

	// ==UserScript==
	// @name LinkedIn Job Filter Pro
	// @namespace http://tampermonkey.net/
	// @version 3.5
	// @description Advanced filters to toggle promoted, normal, and viewed job listings on LinkedIn job search pages.
	// @author yange.xyz
	// @match https://www.linkedin.com/jobs/*
	// @grant none
	// @license MIT
	// @downloadURL https://update.greasyfork.org/scripts/531022/LinkedIn%20Job%20Filter%20Pro.user.js

	#!/usr/bin/swift

	@preconcurrency import WebKit
	@preconcurrency import Foundation
	@preconcurrency import Darwin

	// Disable everything written to stderr
	freopen("/dev/null", "w", stderr)

	struct Cookie {

	#!/bin/zsh

	# Define color codes
	RED="\033[31m"
	GREEN="\033[32m"
	YELLOW="\033[33m"
	BLUE="\033[34m"
	RESET="\033[0m"

	# Define arrays to track installed and failed apps

	// ==UserScript==
	// @name LinkedIn Job Scraper UI you can easily find word in desc
	// @namespace http://tampermonkey.net/
	// @version 1.6
	// @description Collects job postings and descriptions from LinkedIn with a progress bar displayed on top of the screen.
	// @author @ukeskin
	// @match https://www.linkedin.com/jobs/*
	// @grant none
	// ==/UserScript==

	// ==UserScript==
	// @name LinkedIn Company
	// @version 1
	// @grant none
	// @include https://www.linkedin.com/company/*
	// ==/UserScript==

	// ex.
	// https://www.linkedin.com/company/haas-automation/life/haas/
	// https://www.linkedin.com/company/applied-intuition-inc/life/1d1d8313-131e-46f4-ac88-f8cdf5dbd49d/

	// ==UserScript==
	// @name LinkedIn
	// @version 1
	// @grant GM_setValue
	// @grant GM_getValue
	// @include https://www.linkedin.com/jobs/search/*
	// @include https://www.linkedin.com/jobs/collections/*
	// ==/UserScript==
	function remove() {
	var x = document.getElementsByClassName('jobs-search-results__list-item');

	# Load Homebrew environment
	eval "$(/opt/homebrew/bin/brew shellenv)"

	# Variables
	dir_path=$(dirname "$1")
	base_filename=$(basename "$1")
	log_file="${dir_path}/${base_filename}-error.log"

	# Base64 encode the PDF file
	base64_string=$(qpdf "$1" --pages . 1 -- - \| base64)

	#!/usr/bin/env python3

	# Script to extract song/video title, artist, album, etc. metadata from screenshots w/ GPT-4.

	### Example Usage: ###############################################################################
	#
	# ➜ ~/Desktop # python3 music_in_screnshots.py --prompt=prompt.txt --attach=spotify_screenshot.PNG
	# {
	# "found_prominent_media": true,
	# "all_strings": [