NE

NEXT Browser – A Lisp-based power browser

Hacker News

NEXT Browser – A Lisp-based power browser

nEXT redefines browsing by being keyboard driven and powered by fuzzy completion. nEXT is designed to get out of your way, and get you going to where you want to go. https://next-browser.github.io https://github.com/nEXT-Browser/nEXT Still Alpha. Constantly evolving and improving. New features added recently! - Link hint based navigation - history (sqlite db in home dir) - bookmarks (sqlite db) - new minibuffer controls (C-n, C-p) to go up and down candidates. nEXT is not intended to replace your current browser. It is just like adding another tool in your toolbox, use nEXT when you want to get actual work done. If you like this project, please leave a star! Thanks for your time.

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Actual performance

6points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: new · Missing: mac, agents, macos
74%74% 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.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
66%66% 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
52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
46%46% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
31%31% 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
11%11% 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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