Im

Improved index of DuckDuckGo bang operators

Hacker News

Improved index of DuckDuckGo bang operators

I have a search tool / catalog of duckduckgo !bang operators https://mosermichael.github.io/duckduckbang/html/main.html - I am hoping that it allows for better discoverability of specialized search engines. The latest addition is a description for each search engine, just hover over the name, and you get a description derived from the sites meta and title tags. I think that specialised search engines are gaining ground, it has become easier to set one up, thanks to elasticsearch/lucene. They can be quite good, for a limited domain, and they don't have to invade your privacy in order to find out what you are looking for. I think that what is missing are tools like this, that would aid the discovery and use of these search engines. I hope that this will allow them to eat into the market from the 'low end'. The projects source is here: https://github.com/mosermichael/duckduckbang Unfortunately they don't invest too much into !bang operators at duckduckgo, however that's my input data...

Visit product View on Hacker NewsFeb 11, 2022

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2points

Did not reach leaderboard

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

69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

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

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

57%57% 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

54%54% 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

27%27% 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

10%10% 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.

Incorrect prediction on native model

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