A curated collection of GPT prompts that I use in production

Hacker News

A friend and I started collecting GPT prompts we actually use in production in our solepreneur businesses. Things like gpt-assisted high-quality blog writing, outreach email personalization, social media engagement and general best practices for writing prompts that actually work. The main idea when writing these prompts is to use the same frameworks that human experts use in these tasks, and then iterate until GPT seems to ‘get’ it to a high enough degree to get production-ready results Happy to review any prompts submitted (our main criterium to keep noise down is that it’s actually used in your business)

Visit product View on Hacker NewsApr 20, 2023

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: email, tasks · Missing: mac, agents, macos

71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

47%47% 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: personal · Missing: mobile apps, ios, entrepreneurs

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

15%15% 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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