Implement fuzzy search in Emacs using opengpt in 5minutes

Hacker News

I used chatgpt to implement interactive fuzzy search over emacs buffers. It's not perfect but you can see how powerful the automation is with curation. I spent less than five minutes on this. I am a beginner in emacs lisp. I am familiar with emacs and with search engine design but I am not an expert in either. I would not be able to write this code on my own without consulting a manual and fiddling with it for hours.

Visit product View on Hacker NewsDec 5, 2022

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Actual performance

5points

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Launch Intel predictions

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

83%83% 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.

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

74%74% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

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

13%13% 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

9%9% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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