QuickOWL – Quickly open favourite URLs with selected text as input

Hacker News

QuickOWL (https://quickowl.app) is a custom contextmenu extension/add-on, for browsers & Thunderbird Email client, to quickly open user defined web links (URLs) with selected text from a browser/email as input. Inspiration: The inspiration behind developing this extension was: work related bugs or tickets which come in plain text format in emails or internal work portal reports. In order to open the bugs/tickets which are plain text, the usual steps followed are: 1) copy the bug/ticket number 2) open the respective internal bugs/tickets portal 3) paste the copied bug/ticket number and hit a button to do the search or to open it directly Above scenario is only for bugs/tickets, if our work or day-to-day life demands such copying and pasting of text across websites we will be wasting significant amount of time on this process. The QuickOWL extension/add-on aims to remove the steps 1 & 2, and directly sends the selected text (bug/ticket number) to respective bug/tickets portal as input.

Visit product View on Hacker NewsJun 16, 2022

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

89%89% 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 · Missing: supports, reddit linkedin, podcasting

57%57% predicted probability of success on Indie Hackers, 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

46%46% 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

45%45% 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

43%43% 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

23%23% 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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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