GP

GPTed – use GPT-3 for semantic prose-checking

Hacker News

GPTed – use GPT-3 for semantic prose-checking

Hi HN! I threw together a little prototype that uses GPT-3's logprob output to flag unlikely tokens, so they can be double-checked by a human proofreader. It's sort of a turbo-spellchecker: in addition to catching "They had fuor eggs", it can also catch "They had for eggs", "He asked me to prostate myself to the king", and even the somewhat subtle bug in this C code: for (int y = 0; y < HEIGHT; y++) { for (int x = 0; x < WIDTH; x++) { buf[y * HEIGHT + x] = 0; } } I wrote a blog post going into more detail here: https://vgel.me/posts/gpted-launch/ . Hope you like it, and curious to hear people's thoughts, feedback, or questions! :-)

Visit product View on Hacker NewsFeb 6, 2023

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

82%82% 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.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

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

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

38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

33%33% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

16%16% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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