Build your own search engine using bookmarks

Hacker News

Since many years, I've been using bookmarks only as a way to index a link to remember or go back later quickly from address bar, so I built a browser extension to expand on this idea and search also on content which firefox doesn't do Firefox: https://addons.mozilla.org/en-US/firefox/addon/cataloger/ Chrome: https://chrome.google.com/webstore/detail/cataloger/dkfaoaegfclpedkbhmpcbifpicpcedko?hl=en&authuser=0 Medium post: https://rchavesferna.medium.com/create-your-personal-google-7def237e5aef My idea going forward is also to scrape my favorited tweets, and maybe other APIs as well to be able to search even more on content that personally got my attention What do you think?

Visit product View on Hacker NewsJan 2, 2021

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Launch Intel predictions

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

81%81% 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

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

42%42% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

17%17% 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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