Fiderweb
Fiderweb
I don't just strategize digital transformation—I architect it. After years leading digital teams, I'm now hands-on with AI-assisted development to bridge the gap between executive vision and what's technically viable with emerging tech.
Most digital leaders evaluate AI from a distance. I'm building with it—architecting systems with Claude Code, deploying immersive experiences, and discovering the threshold where emerging technology meets real-world utility. This isn't just about learning new tools; it's about testing the friction points of implementation to separate genuine transformation from the hype.
This hands-on experimentation isn't an evolution away from leadership—it's the modern prerequisite for it. The next decade of digital transformation belongs to those who bridge the gap between strategic vision and technical reality. Leadership in this era requires more than just oversight; it requires a foundational fluency in the mechanics of the tech that drives the strategy.
Tomorrow's leaders will be those who have developed a deep, intuitive understanding of how AI-native workflows fundamentally shift execution velocity, team structures, and the scale of what is possible. By rolling up my sleeves now, I'm preparing to lead that change with more than just theory—I'm leading with the practical insight that only comes from the work itself.
25 years building digital teams and platforms—from startup velocity to enterprise scale, CRM systems to customer experience platforms.
Building production tools with vanilla JavaScript, React, WebXR, and AI-assisted development. Not reading white papers—shipping code.
Proven ability to lead teams and deliver at scale—now with firsthand understanding of how AI reshapes everything.
After shipping multiple production tools using AI-assisted development, here's what matters for organizations navigating this shift:
One experienced developer + AI can now match what recently required a 3-5 person team. But this isn't automation—it's orchestration. The human still owns product vision, architecture, quality standards, and integration strategy. What changed is implementation speed.
"Can we build it?" is rarely the constraint anymore. "Should we build it?" and "what exactly should it do?" become the critical questions. Product strategy and user understanding are now MORE valuable, not less.
When one person can do the work of five, the question isn't "do we need fewer developers?" It's "what should developers do instead?" The answer: more strategic work, more experimentation, deeper customer understanding.
Building a VR retirement planner taught me that emerging platforms become viable faster than organizations expect. The companies experimenting now will lead when spatial computing goes mainstream.
Clarity usually precedes change, yet most of us avoid the honest look required to get there. We wait for a "reckoning"—a health scare, a financial wake-up call, or a moment of unavoidable truth. Reckon Well was built to turn those moments into a proactive framework. It is a suite of AI-native tools designed for those ready to take an objective look at their health, their finances, and their future, and bridge the gap between where they are and where they want to be.
It unifies everything I've built under two pillars: Reckon Wealth (your financial future, calculated) and Reckon Health (your health, tracked).
Visit Reckon Well →A comprehensive financial planning tool built with AI-assisted development. Models savings growth, retirement drawdown, and early retirement scenarios — with Bear, Base, and Bull market scenarios driving a 500-path Range of Outcomes simulation. Includes a voice and text conversational assistant for hands-free planning.
See the retirement planner →Business Insight
Each phase that would have taken a team weeks took hours or days. The full planner — with Monte Carlo simulation, market scenarios, Social Security modeling, and a conversational AI assistant — was built iteratively by one person across sessions, not sprints. The bottleneck shifted from “can we build it?” to “what should we build?”—making product strategy and user understanding more valuable than ever.
Personal finance tools that complement the retirement planner: emergency fund sizing, savings benchmarks, and withdrawal timelines. Built to answer the follow-up questions users naturally ask.
See supporting tools →Business Insight
Demonstrates ecosystem thinking—not just standalone features. AI excels at implementing connected systems when given clear architecture and product vision. The challenge is designing the right system, not coding it.
Explores how financial planning works in spatial computing—utilizing hand tracking and voice-driven AI to turn complex data into an immersive narrative.
See VR experience →Business Insight
Proves that emerging platforms are viable sooner than most organizations realize. Also demonstrates that a solo developer + AI can build what recently required entire specialty teams. Leaders who understand this shift will make better strategic decisions about team composition and platform investment.
A wellness platform tracking nutrition, alcohol, sleep, and personal transformation. Includes AI food image recognition, FDA nutrition search, and two standout features: The Real Cost of Drinking (your weekly-to-5-year alcohol spend in dollars and calories) and The Dividend — which shows what redirecting that spend to retirement savings would mean for when you retire.
See Health & Wellness →Business Insight
The most powerful product decisions here aren’t feature decisions—they’re connection decisions. The Dividend links health behavior directly to retirement outcomes by reading data from the Wealth planner’s localStorage. No API, no backend. Two products, one insight: your lifestyle choices have a retirement price tag. Ship, learn, connect.
I'm exploring opportunities where hands-on AI expertise meets strategic leadership:
Interim or permanent roles leading teams through AI adoption, process transformation, and accelerated product development. I can speak both boardroom strategy and build room reality.
Helping organizations separate AI hype from reality. What's actually possible with current tools? How should team structures change? What's the real ROI versus vendor promises?
Building or scaling digital products in the AI era with teams that need to move faster. Combines deep product thinking with practical understanding of what AI enables.
Workshops and advisory on practical AI integration for product and engineering teams. Evidence-based insights from shipped products, not theoretical frameworks.
What I'm not looking for: Pure coding roles, theoretical strategy without execution, or organizations not serious about transformation.