Joy = Experiments

I believe the best way to learn is by doing. These experiments represent my ongoing exploration of AI, product discovery, and experimentation frameworks. Some will succeed, some will fail, but all will teach valuable lessons about building better products.

Active Experiments

Active

AI Staging Web App for Realtors

Testing a lightweight AI tool that stages property photos virtually for real estate agents.

What I'm Testing

Exploring if a simple AI-powered app can help realtors quickly generate staged property images and use them in listings or social media.

What I Learned

Realtors are curious about AI staging, but adoption depends heavily on ease of use, turnaround speed, and pricing models that fit their workflow.

AI Real Estate Staging SaaS

Progress: MVP live, planning outreach to 30,000 agents with target conversion of 3%.

Active

Social Feed First Storefront

A TikTok-style headless storefront where shopping feels like scrolling a social feed.

What I'm Testing

Testing if Gen Z–style vertical feeds drive higher product discovery and conversion compared to traditional Shopify PDP flows.

What I Learned

Shoppers engage more with swipeable, short-form content but merchants worry about setup complexity. Simplicity and plug-and-play integration are key.

E-commerce Headless Gen Z Shopify

Progress: Prototype live in vanilla JS with Shopify backend.

Active

Marketplace Inventory Solution with AI Descriptions

A multi-vendor inventory system with AI-generated product descriptions for watches, jewelry, and collectibles.

What I'm Testing

Exploring if AI can help standardize and enrich inconsistent product data across multiple sellers in a marketplace.

What I Learned

AI can quickly generate appealing descriptions, but consistency across vendors requires strong data structure and moderation workflows.

Marketplace Inventory AI SaaS

Progress: web app live; building integrations with Shopify.

Exploring

Pickleball Pop-Up Venues

Transforming vacant retail anchors into low-capex, high-demand pickleball courts.

What I'm Testing

Testing if temporary indoor pickleball setups can drive foot traffic to plazas while creating a repeatable, low-risk model for landlords.

What I Learned

Strong community demand for leagues and clinics, but landlord adoption depends on revenue-share clarity and minimal tenant improvements.

Sports Real Estate Pickleball Pop-Up

Progress: Pilot proposals sent to Florida landlords; targeting 5–6 court launch.

Active

Interview Prep App Voice

AI-powered voice app that simulates interviews and gives feedback on tone, clarity, and story delivery.

What I'm Testing

Exploring if practicing out loud with AI feedback improves job-seeker confidence and performance in real interviews.

What I Learned

Users value real-time voice feedback more than written scripts, but expect tailored coaching rather than generic corrections.

AI Career Interview Prep Voice

Progress: MVP voice prototype connected to journaling framework; plans to integrate into TellMeAboutYourself.co.

Want to Collaborate?

I'm always interested in collaborating on experiments, especially in AI, product discovery, and experimentation frameworks. If you have an idea or want to explore something together, let's talk.

Let's chat

Frequently Asked Questions

How do you select which experiments to pursue?
I look for experiments that have clear learning potential, address real problems, and can be tested quickly. I prioritize ideas that could benefit multiple companies or industries.
How do you measure success in experiments?
Success varies by experiment type. For tools, it's adoption and user feedback. For tests, it's statistical significance and business impact. For learning, it's new insights that inform future work.
What's your typical experiment timeline?
Most experiments run 2-4 weeks for initial testing. I believe in rapid iteration - if something shows promise, I'll continue. If not, I'll pivot or pause and move on.
Do you share experiment results publicly?
Yes, I believe in sharing learnings. I write about successful experiments, failed attempts, and key insights. This helps others learn and builds community around experimentation.
How do you decide when to pause or stop an experiment?
I pause experiments when market conditions change, when I hit technical roadblocks that require significant investment, or when the learning potential diminishes. I stop when the hypothesis is proven wrong or the opportunity cost becomes too high.