DEV Community: Sola Janet Browne

ClapMetrics: a satirical clapping analytics app

Sola Janet Browne — Wed, 23 Jul 2025 23:43:06 +0000

This is a submission for the World's Largest Hackathon Writing Challenge: Building with Bolt.

ClapMetrics: Built with Bolt.new for the One Shot Bonus Prize

ClapMetrics began as a ridiculous idea for the Reddit x Bolt Silly Sh!t Challenge. I thought, what if we turned clapping into performance data? Not just silly metrics, but a whole ecosystem: mood tracking, leaderboards, achievements. I wanted to poke fun at how far quantified-self culture can go, and how seriously we sometimes take it.

Originally, I planned to integrate it directly with Reddit, but after a few failed attempts at setup, I pivoted. Instead, I entered it into the One Shot Bonus Prize, using a single, carefully crafted prompt to generate the whole frontend via Bolt.

What It Does

ClapMetrics is a playful web app that treats clapping like high-performance behaviour, complete with:

Real-time clapping interface with audio and visual feedback
Analytics dashboard showing progress, streaks, and performance trends
Competitive leaderboard with mock global users
Achievement system with badges like Mood Master and Clap Legend
Mood tracking with emoji-based emotional states
Daily data resets using persistent localStorage
Responsive, glassmorphic UI that works across devices

It’s supposed to be silly, fun, and oddly satisfying to use.

How I Built It

Everything was generated with Bolt.new using a detailed one-shot prompt.

Behind the scenes, it uses:

Web Audio API for sound effects
Lucide React icons for UI polish
Custom gradient and blur layers for aesthetics
Emoji-based state management for mood input
Frontend-only persistence with graceful daily resets

It’s a single-prompt build that includes animation, gamification, responsive design, and subtle critiques.

What I Learned

Sometimes the original idea fails, but you can still ship something that works and makes people smile.

What’s Next

I could evolve ClapMetrics into:

A workshop tool for real-time engagement and feedback
A Slack/Zoom plugin for team celebration culture
A satirical digital art piece/artefact for Are.na or Glitch

Or maybe I will leave it as it is — a mirror held up to self-optimisation culture.

A joyful parody.

Try it Out

https://willowy-torte-b268b7.netlify.app/

ClapMetrics Demo

Not Sure What Therapy’s Right for You? There’s a Quiz for That | MindFLTR

Sola Janet Browne — Wed, 23 Jul 2025 18:18:43 +0000

This is a submission for the World's Largest Hackathon Writing Challenge: Beyond the Code.

Beyond the Code. Why Building a Comprehensive Therapy Match Tool Matters

Therapy is not a one-size-fits-all, and it never has been.

The therapeutic process isn’t just about which modality you use, but who you do it with, and how it’s delivered. The relationship between client and therapist can shape the entire outcome. Fit matters. Timing matters. So does trust.

In the UK especially, when someone tells their GP they’re struggling with anxiety or depression, the most common referral is for CBT.

Now, CBT is an incredibly effective and well-researched approach, but it’s not right for all people and all problems, all the time. And even within CBT, there are many different forms and adaptations. When it’s offered in a rigid, one-size-fits-all way, it can feel invalidating or unhelpful for some.

People deserve more than that.

They deserve options, information, and agency.

Should We Be Concerned That People Are Using ChatGPT as a Therapist?

I built MindFLTR because too many people are left guessing, when it comes to seeking the right mental health and wellbeing support:

Guessing what type of therapy they need.
Guessing if it’s normal to feel unheard or disconnected.
Guessing if they should even bother trying again.

And in the meantime, many are turning to AI.

Whether people think it's a good idea or not. ChatGPT is already being used as a makeshift therapist.

Just spend a few minutes scanning the Reddit threads, or ask people IRL, you’ll see it, it's happening.

Some are using ChatGPT to talk through their thoughts, manage crises, or simulate support.

You could write this off as a sign of the times, you could even think it's a bad idea; the point is it's happening, and I think we should ask why.

Therapy can be expensive
Waiting lists are long
People have been burned by bad experiences
Many simply don’t know where to start
Some people have given up on trying to be understood by other people.

So they turn to what feels safe, immediate, and accessible.

Reframing the AI-as-Therapist Debate

Instead of getting stuck debating whether people should use ChatGPT as a therapist, I think we need to shift the conversation.

People are already doing it, and that tells us something. Rather than dismissing it, we should be asking how we can innovate in this space. How can we design human–AI interactions that are safer, more transparent, and actually point people in the right direction?

I don’t believe AI can, or should, replace human connection.

Real therapists, coaches, and mental health professionals are irreplaceable. But that doesn’t mean AI has no place here. Used wisely, it can support reflection, offer structure, and help people feel less alone when other support isn’t accessible.

We shouldn’t miss that opportunity.

Why I Built MindFLTR and Why it Matters

MindFLTR is not trying to replace therapy. But it is trying to meet people earlier in the journey.

Because there’s a lot we can do before that first session.

We can help people clarify what they want and need.

We can help them understand the range of options.

We can support them to walk into therapy with language, goals, and self-awareness, not just confusion and overwhelm.

That way, the therapy journey doesn’t have to start with four sessions of backstory.

It can start with traction. With alignment. With support that fits.

MindFLTR is my small contribution to that future.

MindFLTR in a Nutshell

MindFLTR isn’t a therapist, but it might help you find one that fits.

MindFLTR is a quiz-based web app that helps people explore which type of therapy might suit them best.

Users answer multiple-choice questions about how they think, feel, and respond to life scenarios. Behind the scenes, a custom scoring algorithm maps each answer to seven evidence-based therapy types. Scores are then normalised into percentage matches.

After completing the quiz, users receive:

A ranked list of their top three therapy matches
A personalised AI-generated summary of their best-fit therapy, written in a warm, encouraging tone
A downloadable report to save or reflect on later
The option to sign up via email for future updates

The quiz logic is also built with neuro-inclusivity in mind.

MindFLTR is designed to take the guesswork out of therapy discovery, offering a clearer, more empowering way to start your mental health journey.

MindFLTR Beyond the Code

My hope for MindFLTR is that it becomes a launchpad - a tool that nudges people to look beyond the usual therapy options and consider the wider landscape of support that exists.

There’s so much more out there than traditional talk therapy, and I’d love to see MindFLTR evolve to include more unconventional and cutting-edge approaches — things like movement-based therapies, nature-connected practices, and even VR-supported experiences as those spaces continue to grow.

The vision is to make that broader ecosystem more visible, accessible, and less intimidating to explore.

Right now, I’m proud to have made a start. I'm incredibly grateful to the Bolt hackathon team and their partners/ sponsors for providing the tools for me to build MindFLTR. It’s been a springboard in more ways than one.

I have continued to develop MindFLTR beyond the initial build and idea.

However, you can see a demo of the earliest version that was submitted to The World's Largest Hackathon below.

MindFLTR Demo:

By Sola Janet Browne

Therapy Discovery Needed a Reboot. So I Built MindFLTR With Bolt.

Sola Janet Browne — Wed, 23 Jul 2025 15:39:12 +0000

This is a submission for the World's Largest Hackathon Writing Challenge: Building with Bolt.

You’ve probably come across the love languages quiz.
Maybe even the attachment styles one.
I wanted to build something just as approachable, but focused on helping people discover the type of therapy that might best suit their needs and preferences.

MindFLTR: Building a Therapy Match Tool with Bolt (My First Full-Stack Build to Deployment Project)

When I joined the World's Largest Hackathon, I had a simple idea: create something that helps people explore therapy in a way that feels straightforward, accessible and informed. I wanted to test an idea I’d been sitting on, something to help people understand what kind of therapy might suit them best, because therapy is not a one-size-fits-all.

That idea became MindFLTR — a short quiz that gives users a sense of which types of therapy might suit them best, based on how they think, feel, and approach various scenarios in life. It's not meant to replace professional advice, but it can be a useful place to start when seeking support for mental health and wellbeing.

What the App Does

MindFLTR is a mobile-first web app. Users take a short multiple-choice quiz, and the app calculates how well their responses align with seven recognised therapy types. These include CBT, Psychodynamic, Humanistic, DBT, Solution-Focused Brief Therapy, EMDR, and Expressive Arts Therapy.

After the quiz, users get:

A ranked breakdown of therapy matches, shown as percentages
A personalised summary explaining their top match, generated using OpenAI
A downloadable report
The option to sign up by email for future updates
Accessibility features: font size adjustments, high contrast mode, keyboard navigation

It’s built to be calm, clear, and genuinely helpful, especially for people who’ve felt lost trying to figure out where to start with therapy.

I intentionally made the quiz logic, weighting and scoring neuro-inclusive, so that neurodiversity is accounted for.

MindFLTR is meant to spark reflection, offer clarity, and hopefully give people a better sense of where to start when it comes to therapy.

Building with Bolt

I used Bolt.new to build the app from the ground up. The frontend was built with React and Tailwind, and Supabase handled backend tasks like email collection and Edge Functions.

Here’s what I implemented:

A modular component structure for the landing page, quiz flow, results, and email screen
A custom scoring system that turns user answers into normalised match percentages
Supabase Edge Function to call the OpenAI API, sending user traits and their top therapy match to generate a personalised summary. Getting that API integration working smoothly and securely was a big part of the build.
State management across the app to handle navigation and accessibility settings
A floating toolbar for font and contrast adjustments

What Informed the Design

I didn’t want to build just another quiz. Before I started, I used Perplexity to synthesise peer-reviewed research around therapy-seeking behaviour, common misconceptions about modalities, and how digital tools often fall short.

That early research shaped the logic, tone, and structure of the quiz. My background in psychology (from undergrad through to my current PhD) definitely influenced the way I approached the background research, matching system and how I framed the results. The goal was to inform and encourage, not overwhelm.

Therapy isn’t one-size-fits-all. It took building this tool to realise just how hard it is to design for nuance — and just how worth it that challenge is.

What I Learned (and Had to Figure Out)

This was my first time wiring up both frontend and backend. Getting Supabase and Bolt to talk to each other, handling Edge Functions, and working with environment variables — all new and challenging.
The scoring logic felt simple at first, but it wasn’t. I wanted it to feel balanced. It wasn’t just a matter of tallying points. I wanted the output to be meaningful, not just cosmetic or a surface-level match, so I spent time refining how percentages were calculated and presented.
For the AI summaries, I spent time refining the prompts to guide the AI toward more semantic clarity and emotional nuance.
Accessibility features took careful planning — especially dynamic font scaling and full keyboard support.

The hardest part wasn’t building — it was knowing when to stop.

There’s always more to add, but the time constraints of the hackathon forced me to draw a line between what's essential and what's just nice to have. I learned to scope responsibly, make tough calls, and trust that simplicity can still be impactful.

Why Bolt Helped

Bolt gave me the space to focus on what I wanted to build, rather than worrying about setup and structure. AI-generated components were surprisingly helpful for getting past the initial blank canvas. Real-time previews and smooth Supabase integration made the workflow easier than I expected.

Using Entri

I also used one of the hackathon partners — Entri — to purchase a custom domain for the project: mindfltr.me. I bought it right at the end of the hackathon window, so I didn’t have time to connect it properly to the deployed version of the app.

Still, it felt like an important step, giving the project a real name and a permanent home. I plan to point the domain to the live version of MindFLTR as I continue developing it.

Looking Ahead

MindFLTR is just getting started. I’d like to build on it, improve the AI responses, add more therapy modalities (and adaptations), languages, and pay further attention to cultural sensitivity. I also have plans to include next-step guidance, and perhaps collaborate with organisations for this part. But the core of it is live, and it works.

I want MindFLTR to be more than just a quiz; I want it to be a starting point for exploring the full spectrum of support that exists for our mental health and wellbeing. There’s a whole universe beyond traditional talk therapy, and I think people deserve to know what’s out there.

Eventually, I’d love to expand MindFLTR to include lesser-known, emerging, and even immersive approaches — from nature-based practices to body-led modalities to future-forward therapies like VR-assisted exposure or guided simulations. As those fields grow, I want MindFLTR to grow with them; offering people a broader, more inclusive way to find the kind of support that fits.

For now, I’m proud of what it’s become. And I’m grateful to the Bolt hackathon team and all of their sponsors and partners for giving me the fuel to power forward and build the things I’ve been dreaming about.

MindFLTR demo

By Sola Janet Browne