Obituary bot eulogizes the long-dead using GPT-4

Hacker News

I made this Twitter bot a a few years back with the idea of scraping Wikipedia to help people learn about interesting, lesser-known people from history. It used to write obituaries by scraping the information directly from Wikipedia, but I found I was able to get surprisingly high-quality results by just showing GPT-4 the obituary format and allowing it to write everything. I’m pretty happy with the current iteration! Source code, including the prompts, are in the bot’s bio. By the way, you may notice that some of the prompts include “written by a leftist twitter account”. I just thought it’d be funny to see what would happen if you told a bot to take a political stance on obituaries. For the most part, that part of the prompt seems to get ignored other than that the bot occasionally calls a person an imperialist haha.

Visit product View on Hacker NewsMar 22, 2023

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting

82%82% 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: using, code · Missing: mac, agents, macos

72%72% 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: ide, io, including · Missing: https docs, excited, just released

50%50% 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: way · 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: occasional, calls · 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

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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