u/Donald-the-dramaduck

Hey! Just shipped a side project I've been working on and looking for real users to stress test it.

What it is: HabitFlow — a habit tracker where nudges are selected by a contextual multi-armed bandit that learns per-user intervention preferences in real time.

The ML side (for those interested):

• Each user has 10 bandit arms — one per intervention strategy (streaks, loss framing, dark humor, social proof, etc.)
• Thompson Sampling maintains a Beta(α, β) distribution per arm and updates on every feedback signal
• Feedback signals: completed (+1.0), engaged (+0.5), ignored (0.0), dismissed (-0.2), negative (-0.5)
• The system learns your preferred strategy without any offline training — purely online learning from production feedback
• Built a separate MLOps dashboard with policy registry, A/B testing framework, fairness constraints, and automated retraining pipeline

Stack: FastAPI · PostgreSQL · Redis · React · Celery · SQLAlchemy

What I need: Real users generating real feedback signals. Even 5-10 people for a week gives me actual bandit convergence data to analyze.

If you want to try out the app or check out the dashboard, DM me and I'll be happy to share the links.

Happy to answer questions about the implementation — the bandit engine and policy evaluator were the most interesting parts to build.

Hey! Just shipped a side project I've been working on and looking for real users to stress test it.

What it is: HabitFlow — a habit tracker where nudges are selected by a contextual multi-armed bandit that learns per-user intervention preferences in real time.

The ML side (for those interested):

• Each user has 10 bandit arms — one per intervention strategy (streaks, loss framing, dark humor, social proof, etc.)
• Thompson Sampling maintains a Beta(α, β) distribution per arm and updates on every feedback signal
• Feedback signals: completed (+1.0), engaged (+0.5), ignored (0.0), dismissed (-0.2), negative (-0.5)
• The system learns your preferred strategy without any offline training — purely online learning from production feedback
• Built a separate MLOps dashboard with policy registry, A/B testing framework, fairness constraints, and automated retraining pipeline

Stack: FastAPI · PostgreSQL · Redis · React · Celery · SQLAlchemy

What I need: Real users generating real feedback signals. Even 5-10 people for a week gives me actual bandit convergence data to analyze.

If you want to try out the app or check out the dashboard, DM me and I'll be happy to share the links.

Happy to answer questions about the implementation — the bandit engine and policy evaluator were the most interesting parts to build.

Hey! Just shipped a side project I've been working on and looking for real users to stress test it.

What it is: HabitFlow — a habit tracker where nudges are selected by a contextual multi-armed bandit that learns per-user intervention preferences in real time.

The ML side (for those interested):

• Each user has 10 bandit arms — one per intervention strategy (streaks, loss framing, dark humor, social proof, etc.)
• Thompson Sampling maintains a Beta(α, β) distribution per arm and updates on every feedback signal
• Feedback signals: completed (+1.0), engaged (+0.5), ignored (0.0), dismissed (-0.2), negative (-0.5)
• The system learns your preferred strategy without any offline training — purely online learning from production feedback
• Built a separate MLOps dashboard with policy registry, A/B testing framework, fairness constraints, and automated retraining pipeline

Stack: FastAPI · PostgreSQL · Redis · React · Celery · SQLAlchemy

What I need: Real users generating real feedback signals. Even 5-10 people for a week gives me actual bandit convergence data to analyze.

If you want to try out the app or check out the dashboard, DM me and I'll be happy to share the links.

Happy to answer questions about the implementation — the bandit engine and policy evaluator were the most interesting parts to build.

Hey everyone!

I've been building this for the past few months and finally have something worth sharing.

The problem I was trying to solve: Most habit apps motivate everyone the same way. Streaks, badges, reminders — same for every single person. But that's not how motivation actually works. Some people respond to streaks. Some need a challenge. Some respond better to humor. Some need to feel like they're losing something if they skip. We're all wired completely differently.

What I built: A habit tracker that learns which type of motivation works specifically for YOU.

Every day you get a personalized nudge. You react to it — did it help? did you ignore it? was it annoying? Over time the app stops sending you the nudges that don't work and doubles down on the ones that do. After a week it knows your pattern.

The nudge styles it learns between:

• 🔥 Streak based — "Day 7! Don't break the chain"
• ⚡ Micro challenge — "Just 5 minutes. That's it. Go."
• ⚰️ Dark humor — "You're not getting younger. Maybe do the thing."
• ⚠️ Loss framing — "You're losing 3 days of progress by skipping"
• 🌟 Positive reinforcement — "Amazing work! You're 80% there this week"
• 👥 Social proof — "Hundreds of people like you finished this today"
• and a few more...

It's not random — it's actually learning from how you personally respond.

What else is in the app:

• Daily habit tracking with streaks
• 30-day activity heatmap so you can see your patterns
• Social feed to follow friends and stay accountable
• Installs on your phone home screen like a native app

If you want to try it — just DM me for the link🙌

Happy to answer any questions in the comments too.