Bayan Flow Earns a 34 Proof of Usefulness Score by Building Interactive, Real-Time Visualizations

In this interview, we sit down with Ayoub Abidi, the creator behind Bayan Flow. Bayan Flow is an innovative web application designed to help computer science students learn complex algorithms with clarity through interactive, real-time visualizations.

What does Bayan Flow do? And why is now the time for it to exist?

https://youtu.be/ZwcT68ZRD0U?si=4UF1acz6HIDC38lW

Learn algorithms with clarity through interactive, real-time visualizations. Now’s a good time for Bayan Flow to exist because computer science education increasingly demands highly visual, engaging, and accessible tools to help students intuitively master complex computational concepts.

What is your traction to date? How many people does Bayan Flow reach?

Still in a very early stage, but it's very promising once I start promoting it

Who does your Bayan Flow serve? What’s exciting about your users and customers?

Computer Science Students who struggle with understanding how algorithms work, this app simply helps them visualize it better and makes it easier to understand

What technologies were used in the making of Bayan Flow? And why did you choose the ones most essential to your tech stack?

To build a seamless and attractive user experience, Bayan Flow relies on a modern front-end tech stack featuring React.js, Tailwind CSS, and Framer Motion. These technologies were specifically chosen to ensure smooth, real-time visual animations and a clean, responsive interface that keeps students engaged without distraction.

What is the traction to date for Bayan Flow? Around the web, who’s been noticing?

While Bayan Flow is currently in its pre-launch phase, it is already turning heads in academic circles. Ayoub has shared early versions of the app with university professors who have expressed strong enthusiasm, noting that the tool will significantly ease the process of teaching complex algorithms to their students.

Bayan Flow scored a 34 proof of usefulness score (https://proofofusefulness.com/report/bayan-flow) - how do you feel about that? Needs reassessed or just right?

I think that's fair enough for the current project and what is currently offering, right now we are still in version 0.3.0 so we just are offering teh very core feature and purpose of the app but looking at the dev version I also offer publicly on https://dev-bayanflow.netlify.app you will notice the new tiny features I am working on that will make the exprience more useful and exiting to the user, while also working on adding more algorithms types aside from the current pathfinding and sorting algorithms the official app is offering right now. This is why I believe in the potential of this small app (without the AI hype we see in every app these days) to help CS students around the world.

What excites you about this Bayan Flow's potential usefulness? *

Although this kind of project exists in many forms, I believe mine offers a better user experience because it provides a simpler, smoother, and definitely more attractive interaction, and is definitely not overwhelming or making it look complicated for young learners

Walk us through your most concrete evidence of usefulness. Not vanity metrics or projections - what's the one data point that proves people genuinely need what you've built?

The main target of this app is obviously the students, but I believe the right route for the true usefulness of this app is through the teachers themselves. This is why I shared the app with many teachers that I know to let them use it to help them explain algorithms to their students in a more meaningful and fun way. In the end, this project was always on my bucket list, the moment I started struggling with learning algorithms when I was a CS student, so in one way or another, this project is a gift from me to my younger self that I promised to deliver to make other fellow learners understand painlessly this beautiful art called algorithms.

How do you measure genuine user adoption versus "tourists" who sign up but never return? What's your retention story?

This is still a very young project, so there's no authentication currently (but I am planning on doing that with version 0.5.0). Right now, what I can call my retention story is just the three algorithm teachers and my algorithms-obsessed friend, who are using it and want to have more algorithms. Of course, there are more "tourist" users coming from my views on my DevLog YouTube video series, where I share the process of making this project and my shares over multiple Discord servers. Those views are measured through my umami stats I have installed on the project. I should also mention that this project is an open-source project, so potentially in the future, there's a chance that we will not only have consuming users but also real contributors to help out achieve the vision and goals of this app.

If we re-score your project in 12 months, which criterion will show the biggest improvement, and what are you doing right now to make that happen?

I believe that after 12 months, both real-world utility and audience reach impact will show the biggest improvement, since the main focus right now, according to the roadmap page, I am sharing the app itself with the users is adding small features to help complete the flow of the core features (like the insight panel that will let the user learn about the algorithm he chose to learn about and visualize, and improve the code panel to be more interactive instead of showing only static code that the user can't play with) and of course adding more algorithms to cover as much as we can to make this app a true reliable source of learning.

How Did You Hear About HackerNoon? Share With Us About Your Experience With HackerNoon.

I always liked the HackerNoon platform and liked their policy of not letting any user out there share any random articles until admins/editors approve them. This is why I have already been sharing multiple articles on this beautiful platform (and I need to admit that many of them didn't get approved to be published, but I am totally accepting that due to the logical reasons the editors provided) to share my stories, point of view, and maybe some useful info that might help fellow developers with their journey.

As you transition Bayan Flow from the pre-launch phase to a full release, what specific strategies will you use to convert that early enthusiasm from university professors into active student user growth?

At the full release phase (version 1.0.0), I am expecting to have a fully functional and rich platform that is worth creating an account on. The current strategy is cost-free organic marketing through sharing videos in different formats (my DevLog YouTube series, shorts/reels, and even educational YouTube videos using the platform itself to explain algorithms and concepts in the near future), but at a certain point, the true reliable and mesurable strategy will be of course paid ads to reach as many students as much as possible.

With the educational technology market being quite competitive, how do you plan to scale Bayan Flow and acquire your first 1,000 active student users?

I believe that when you have a true quality educational platform that is constantly evolving and adding real value content, the 1K users will come easily through both organic and paid marketing, and most importantly, they will come back and share with others just because of the quality this forever ad-free platform will provide.

Since Bayan Flow focuses heavily on a simple and attractive user experience through React and Framer Motion, what user feedback loop will you establish to ensure these visualizations actually improve a student's algorithmic comprehension over time?

Currently, Bayan Flow doesn't have a feedback loop yet. The visualizations are good. Whether they're teaching is a different question.

The most useful signal we're not capturing right now is backward stepping in manual mode. This is why that would be a total priority in future versions, but the current hot fix I am thinking about for the moment, after having the first 100 active users, is to create multiple GitHub Discussions with a simple "Which algorithm confused you most and why?" template that will give us a qualitative signal no analytics tool will. And with that, the most valuable data (where students get stuck) can be collected passively from day one, without a backend.