Feep! search, an independent search engine for programmers

Hacker News

Hi HN! This started late last year as an afternoon project to play around with ElasticSearch, and then I kept thinking of new features I wanted to add. I still have a lot of things I want to build, but now seemed like a good time to put it out there: even if the results aren’t nearly the quality I’d like, I’ve still found it useful and I want to show it off! I’ve been working on it since September 2021, but only in fits and starts. The entire thing runs on a computer in my living room (there’s a picture on the About page); I haven’t done any load testing so we’ll see how it holds up.

Visit product View on Hacker NewsNov 6, 2022

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182points

72comments

Made the leaderboard

Launch Intel predictions

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

86%86% 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

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · 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.

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

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · 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

17%17% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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