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Surfer a browser that blocks the many of the most visited websites

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Surfer a browser that blocks the many of the most visited websites

I've had an idea for a while of a web browser that blocks the world's most popular websites as I've gotten very tired of spending a lot of time on the same select few and was missing the days when I used to explore and surf the web. So last weekend when I found one of Qt's Pyside based web browser examples I decide to actually build out the idea as a fun little project. Now as for Surfer itself the landing page is marginalia-search but I've also have bookmarks to wiby.me and neocities to help kickstart exploring. While not the most cutting edge it is fun to use because as it breaks up the habit of visiting the same few websites everyday. Let me know if you have any questions, know of any other good websites to have bookmarked by default, or have any simple features that you think would improve it.

Visit productView on Hacker NewsJan 19, 2025
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
78%78% 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
73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
63%63% 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
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: margin · Missing: arr, mrr, revenue
12%12% 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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