I

I used ChatGPT to write a UserScript that removes all mentions of *GPT

Hacker News

I used ChatGPT to write a UserScript that removes all mentions of *GPT

I have little to no coding experience, just basic HTML and CSS. This was generated entirely by ChatGPT with me giving the prompts. It took some trial and error but we got there: // ==UserScript== // @name Block GPT Posts on Hacker News // @namespace http://tampermonkey.net/ // @version 0.1 // @description Block posts containing the term "GPT" on news.ycombinator.com and renumber the remaining posts // @author You // @match https://news.ycombinator.com/* // @grant none // ==/UserScript== (function() { 'use strict'; function getStartingNumber() { const firstRankSpan = document.querySelector('span.rank'); if (firstRankSpan) { const number = parseInt(firstRankSpan.textContent, 10); return isNaN(number) ? 1 : number; } return 1; } function blockGPTPosts() { const searchTerm = /GPT/i; const titleLines = document.querySelectorAll('span.titleline'); const rankSpans = document.querySelectorAll('span.rank'); const startingNumber = getStartingNumber(); let visiblePosts = []; titleLines.forEach((titleLine, index) => { const postLink = titleLine.querySelector('a'); if (postLink && searchTerm.test(postLink.textContent)) { const athingRow = titleLine.closest('tr.athing'); if (athingRow) { athingRow.style.display = 'none'; const subtextRow = athingRow.nextElementSibling; if (subtextRow && subtextRow.querySelector('td.subtext')) { subtextRow.style.display = 'none'; } } } else { visiblePosts.push(index); } }); visiblePosts.forEach((visiblePostIndex, index) => { const postNumber = startingNumber + index; if (rankSpans[visiblePostIndex]) { rankSpans[visiblePostIndex].textContent = `${postNumber}.`; } }); } // Wait for the content to load before executing the function window.addEventListener('load', blockGPTPosts); })();

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, chatgpt · Missing: mac, agents, macos
52%52% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: hacker news, io · Missing: https docs, excited, just released
39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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