Hi everyone, my name is Thanh, Co-Founder of allin 👋
💡allin is the ultimate app designed to help you "detox" from the overwhelming and often misleading content on social media. We were inspired to create allin because we noticed how difficult it is to sift through the endless amount of information on our phones. We’re always on the go, trying to make the most out of our limited time, whether it’s on the bus, during a break, or while waiting for an appointment.
With allin, you can:
✅ Quickly access useful information each day through spotlight swipes.
✅ Understand the essence of topics or events with detailed analysis.
✅ Debating mindset then apply things into daily life.
✅ Personalized content recommendations and enhanced categorization.
To stay up to date with our progress, follow me here on Product Hunt, or visit our website for more updates. And don’t forget to leave your thoughts in the comments below! 😊
Your feedback is super important to us, so please check it out and share your thoughts 🔥
Cheers,
Thanh (MR).
Hi Thanh Nguyễn Khắc, congrats on the allin launch! 👋 The mission to help people "detox" from overwhelming/misleading content and apply useful information is incredibly relevant today. The spotlight swipe interface for quick access sounds like a smart way to deliver value efficiently.
As we're building @UNI AI, which includes the UNINET network focused on growth-oriented connections, I'm particularly interested in the intersection of information discovery and social elements.
Question: How does allin plan to balance personalized content recommendations with preventing filter bubbles, ensuring users are still exposed to diverse and potentially challenging perspectives when appropriate?
Looks like a valuable tool for navigating the information age. Wishing you a successful launch! 🧠💡🌐
First of all, allin ensure the information on the app is helpful information that even them be filtered or not, the benefit for user still have and risks in terms of toxin contents still not.
Secondly, we are looking forward to recommend topics that user interested and did choosing before using app, and belong to user's demand by their behaviors of using.
To escape the problem of filter bubbles: (1) we have the feature is Discovery for them to find any topic, key words,etc. at anytime; (2) the concepts of content is follow Bloom's Taxonomy to let user have analysis and debate mindset; (3) the recommendations looking forward to extend the topic and contents related to their demand not fixed.
Hope the answer is clear for you. Thanks again, Theo L.
What a great app!
Reading is one of the most usefull ways to receive information, along with listening. If you are a kind of curious and busy guy, want to know stuff in various aspects of life. This app is for you!
Allin provides brief, transparent, trustful information in different topics that you can freely select based on your concern.
With a phone connected Wifi or 4G/5G, you can take advantage of your short time, click Allin to absorb useful information it supplying. Yepp, i bet that you will be content with such great moments Allin bringing about.
@kts1 Thank you all for your upvotes and feedbacks for us to continuously level-up allin in the next versions 🤗
Hi Thanh Nguyễn Khắc, congrats on the allin launch! 👋 The mission to help people "detox" from overwhelming/misleading content and apply useful information is incredibly relevant today. The spotlight swipe interface for quick access sounds like a smart way to deliver value efficiently.
As we're building @UNI AI, which includes the UNINET network focused on growth-oriented connections, I'm particularly interested in the intersection of information discovery and social elements.
Question: How does allin plan to balance personalized content recommendations with preventing filter bubbles, ensuring users are still exposed to diverse and potentially challenging perspectives when appropriate?
Looks like a valuable tool for navigating the information age. Wishing you a successful launch! 🧠💡🌐
@theo_l Thanks for your question!
First of all, allin ensure the information on the app is helpful information that even them be filtered or not, the benefit for user still have and risks in terms of toxin contents still not.
Secondly, we are looking forward to recommend topics that user interested and did choosing before using app, and belong to user's demand by their behaviors of using.
To escape the problem of filter bubbles: (1) we have the feature is Discovery for them to find any topic, key words,etc. at anytime; (2) the concepts of content is follow Bloom's Taxonomy to let user have analysis and debate mindset; (3) the recommendations looking forward to extend the topic and contents related to their demand not fixed.
Hope the answer is clear for you. Thanks again, Theo L.
This should streamline our research processes! 👀