Customizable DuckDuckGo Search Bangs Service

Hacker News

I love DDG !bangs so much, that I kept finding myself in need of custom search !bangs... So I built them! I absolutely love DDG's search !bangs! ... even more than their stance on privacy. If you've ever found yourself wishing for a custom search !bang you're in the same boat as me :) Sure, sometimes submitting a public !bang is the way to go... But not always. Maybe you want to override the default !weather or you want a custom !bang for an internal tool used by your employer. You can create your custom search !bangs at https://interrobang.online

Visit product View on Hacker NewsMar 20, 2019

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Actual performance

7points

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Launch Intel predictions

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

62%62% 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

52%52% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

52%52% 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

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

40%40% 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

13%13% 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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