Remove links to sites you'll never visit

Hacker News

I've made an experimental Firefox add-on[1] mostly to remove links to sites I know I never want to visit again (pay walls, register-to-view, or just general garbage). It's not very polished. The only reason I registered it as an official add-on is so I can use it without having to load it manually every time I start FF. However, if anyone else is interested I'll polish it up, publish the source and other such niceties. In general, it can be used to apply CSS to elements whose attribute values or text match the regular expressions. To configure it: click the "Preferences" button in the Firefox "Add-Ons Manager"; copy the example JSON into the textarea; modify it any way you'd like; and click "Save". The example sets visibility to hidden on links to the sites in the list. If you hit up HN, you'll probably see a few hidden links on the front page. [1] https://addons.mozilla.org/en-US/firefox/addon/ssure/

Visit product View on Hacker NewsMar 1, 2017

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Launch Intel predictions

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

72%72% 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 HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

49%49% predicted probability of success on Product Hunt, 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

43%43% 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 · Strong signals: way · Missing: mobile apps, ios, personal

39%39% 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

33%33% 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

18%18% 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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