From support to shipped product · APM Candidate
I spent nine years at Fidelity and Global Payments learning exactly where products fail — by being the person who answered when they did. Now I build the ones that don’t.
Led a four-person team through Fidelity’s internal Shark Tank-style challenge: 18 teams, two rounds, one winner. I owned the whole product lifecycle for a gamified financial-literacy concept, from discovery and competitive analysis to MVP scoping, journey maps, and the final pitch.
Most kids reach adulthood without the basics of financial literacy, and it shows up later as poor money habits, high debt, and a lasting distrust of financial institutions. The tools meant to help are usually too dry and too abstract to hold a young person’s attention. The real opportunity was to reach them early, in a format they’d actually want to use.
I started with competitive research across EdTech and FinTech onboarding flows so the team understood what had already been tried and why most of it never stuck. With that shared picture in place, I ran a brainstorm that surfaced about ten possible features for Finny & Flo, our gamified literacy concept for the Frog Tank challenge. Getting from ten ideas to three meant running each one through two questions: does it genuinely help a middle schooler, and does it connect directly to the core problem? Anything that didn’t clearly do both got cut, which left us with a focused MVP and a pitch we could defend with a straight face.
There was real pressure to add digital features and make it feel more like a tech product, and I pushed back. A physical board game is far easier to adopt in a classroom, and it meets middle schoolers in a setting they already trust. Keeping it physical made the concept more defensible and the pitch cleaner; a digital build would have piled on complexity without earning any extra engagement.
Most traders don’t lose because their strategy is wrong — they lose to emotion under pressure. I traced that to three root causes over years on the Fidelity trade desk and built a full trading cockpit that makes the disciplined move the easy one: regime-gated risk sizing, separate guided workflows for swing and intraday, and an Edge Intelligence layer that scores your setup before you commit.
Launch Live AppThree root causes kept surfacing, drawn equally from my own bad trades (and the honest post-mortems after) and from years on the Fidelity trade desk watching users repeat the same mistakes. Emotional Bias — revenge trades and FOMO after a loss override the plan. Risk Fragmentation — sizing positions by hand leads to chronic oversizing, especially when conditions shift. No Pre-Decision Structure — traders act on impulse without checking whether the setup, the regime, and the risk actually line up. Every tool I tried recorded what happened after the trade. None of them stepped in while the decision was still live.
I scoped Trapper’s Edge as a decision cockpit, not a journal or a scanner. It had one job: make a bad trade structurally harder to take.
The centerpiece is a Market Regime system (Risk-On / Neutral / Risk-Off) that gates position sizing automatically — full size in trending markets, trimmed in choppy ones, puts-only in downtrends. The regime is wired into every calculation, so it can’t be quietly ignored. On top of it sits a rolling kill-switch: cross your drawdown floor and new trades lock out, which takes away the option to dig the hole deeper on impulse.
The trade flow is mode-specific. Swing and intraday traders follow separate guided workflows, each with its own setup catalog, entry/stop/target logic, and pre-trade checklist. I considered one unified flow and dropped it — the inputs for a three-day swing and a thirty-minute opening-range break differ enough that merging them adds noise, not simplicity.
Edge Intelligence is the AI layer: it reads the full context — regime, setup, direction, confluence — and scores the trade before you commit. An AI Setup Builder lets traders define their own strategy templates, thesis and pre-mortem and all, without leaving the app.
The high-density terminal aesthetic was deliberate. The trader is already under pressure, so a calm, low-distraction interface takes away one more reason to rush.
I shipped this end to end on my own — problem definition, wireframes, build, deployment. It’s a PWA in React 19 and TypeScript, installable on mobile from day one, with a custom risk engine that handles regime-adjusted sizing, R-multiple targets, and the kill-switch in-house. Supabase keeps state in sync across devices, and a SnapTrade integration pulls live broker fills straight into the app for review and promotion to the trade log. Hosted on GitHub Pages.
Shipped end to end as solo product lead under my studio, Mac Real Studios, from spotting the gap to Google Play distribution. It’s a Wear OS safety utility for night runners, walkers, and anyone moving in low-light conditions who needs to stay visible without stopping to dig out a phone.
Visit StudioI noticed the gap on a run: there was no clean way to stay visible without stopping to pull out my phone. A look through the Wear OS store confirmed it, with almost nothing purpose-built for this. Night runners and outdoor users have been stuck choosing between clumsy workarounds and bulky accessories. A real need, and a fairly straightforward fix.
I looked at three interaction patterns and chose one-tap activation over a menu, because the user is mid-movement, not mid-focus. The app had to work without asking for any attention. The hard part was the discipline to cut everything that didn’t serve that, so color modes, SOS patterns, and sync all got scoped out. Every extra step is another place the app could fail someone who needs it. With this one, simplicity was the product itself, not a corner I had to cut.
Every Mac Real Studios app is built against four rules: Focused over bloated — ship only what the problem actually needs. Structure over noise — clarity is a feature. Craft over shortcuts — the details are the product. Usefulness over image — solve a real problem or don’t ship.
Mac’s influence was the key factor in our team’s victory. He was the railroad track that kept everything together and moving seamlessly. His ability to take multiple perspectives and transform them into a cohesive vision was invaluable — and his ability to blend strategy with creativity made him indispensable to the success of the project.
Most product candidates study user problems from the outside. I lived on the inside of them. At Fidelity Investments and Global Payments, two Fortune 500s, I was the person users reached when something wasn’t working. I handled the escalations, logged the patterns in Salesforce, and sat in the meetings where we decided what was breaking badly enough to push to the platform team first.
That’s where my product thinking actually started: in the support queue, not a classroom. I kept seeing the same problems surface for completely different users, and kept asking why no one had fixed them yet. These days I’m on the other side of that question, building for the users I used to support.
I spent years in the support queue before I ever wrote a PRD, so I already know what real user friction feels like. Not from a research deck, but from handling it firsthand across 9,700+ cases.
At Fidelity’s Frog Tank, we started with ten ideas for Finny & Flo. I walked the team through which ones genuinely solved the problem and which just sounded good, and we cut down to three. That focus is what won.
FINRA-licensed (Series 7/63) and comfortable with PRDs, user journey maps, and AI-augmented PM workflows. I can dig into the details with engineers and then explain the same thing to a stakeholder without losing either side.
For hiring managers & recruiters
If you’re building a product team and want someone who has already spent years learning where complex software breaks, and who ships the fix end to end, let’s talk.
Targeting APM and Product Analyst roles in FinTech, AI-driven consumer products, or EdTech. Open to remote and hybrid.
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