A-

A-Pass Firefox Companion – Autofill Your Passwords from A-Pass

Hacker News

Hey Hacker News Community, Are you tired of manually filling in your login credentials every time you access a website? Are you looking for a more efficient way to manage your passwords? Look no further than A-Pass Firefox Companion! A-Pass Firefox Companion is a browser add-on that enables autofill of user and password fields stored in the password manager A-Pass. With this add-on, you no longer have to worry about remembering your login credentials or typing them in every time you access a website. A-Pass Firefox Companion takes care of all the tedious work for you. The best part is, A-Pass Firefox Companion is completely free and open source! You can find it in the Mozilla Addon Repository and on Github. You can trust that your login credentials are secure and only accessible by you. So why wait? Try out A-Pass Firefox Companion today and make your browsing experience hassle-free! Let us know what you think in the comments or on Github. Mozilla Addon Repository: https://addons.mozilla.org/en-US/firefox/addon/a-pass-browse... Github: https://github.com/balu-/a-pass-firefox-companion Thank you for your time and happy browsing!

Visit product View on Hacker NewsApr 30, 2023

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Hacker NewsStrong engagement from HN community · Strong signals: open source, hacker news, io · Missing: https docs, excited, just released

53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: user, new, open · Missing: mac, agents, macos

48%48% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

48%48% 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

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

34%34% 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

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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