GPT-3 powered Recruiting Email Generator

Hacker News

Hello everyone! Today I am presenting a GPT-3 powered recruiting email generator. Over the past few years, as many of you, I have been receiving an overwhelming number of recruiting emails both directly and via LinkedIn. Last week I was using some of my vacation and browsing through those messages, and I've noticed poorly executed obvious attempts of personalisation some of those have, or being simply poorly worded cold emails. I thought to myself immediately about potential performance of GPT-3 in that field, and how such email generation could be easily automated, beating most of those human written generic messages. I have also figured out, once I build such a tech demo, I could then respond to each of those unsummoned recruiting emails with a link to my tool, flipping the coin and turning recruiters into my leads. Please let me know what you think about it. All feedback is welcome!

Visit product View on Hacker NewsFeb 18, 2023

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

77%77% 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 · Missing: supports, reddit linkedin, podcasting

77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

14%14% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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