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A new(?) approach for your bookmarks

Hacker News

A new(?) approach for your bookmarks

Hey HN community, there are a couple of websites I visit on a daily basis. I type the first two oder three characters of the url and the autocomplete feature of my browser does the rest. That is also how I procrastinate. It happens that i visit the same website over and over again without that there is really new content on that site. Or on the other side it happens that I forget to visit a certain site for some days. To address all this I build the following website: https://everyday-favs.com The idea is: you have one personal link (1) and define a list of websites and how often you want to visit them. Then you set this link as your homepage or on your browser bookmark bar. Then by visiting your personal link you get redirected to a website of your list that you have not visited in your configured timespan. If there is no more website to visit you get redirect to a static site saying "all done, let's get back to work!". The second feature is that you can share the list of bookmarks (2) with others and they can create a copy of it and set their own timings and add/remove urls. I use this on my desktop pc and my mobile phone. Because it is the same link it is automatically synchronized on all your devices. It is a bit hard to explain. Hopefully you get the idea. But i tried it the last couple of weeks on my laptop and smartphone and love it! It really helps me stop procrastinating. (1) Example: https://everyday-favs.com/p/huam36op8jom-gu4mgf1ectnu-pmku8mnc1tzu (2) Example: https://everyday-favs.com/list/sp8p4bkuir1d

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: new, plain · Missing: mac, agents, macos
79%79% 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
76%76% 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
55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs
38%38% 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
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: smart · 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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