Advisor, operator, investor, and sports fanatic. Deep financial and analytical background, working at the intersection of data, analytics, and AI for several years.
My focus is on building data and AI infrastructure, workflows, and products that grow revenue, generate operational efficiency, and drive enterprise-wide adoption.
Having executed across finance, product, and business intelligence, I understand what it takes to move from insight to action at scale.
Led a 0-to-1 subscription, advertising, and engagement demand forecasting data product.
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Building an AI-native household management app designed for busy parents.
View Project →Building an AI-native household management app designed for busy parents. Provides households with an intuitive application that learns more about your family and improves over time. Eliminates the setup, development, maintenance, API-cost tracking, and overall burden of building with Claude Cowork/Claude Code, while delivering a comprehensive household management solution that drives actionable workflows rather than piecemeal outputs, like an email digest of the week ahead.
Tech stack: Next.js 16 (App Router), TypeScript, Supabase/Postgres, Claude API, Tailwind CSS + shadcn/ui, deployed on Vercel. Claude API calls run server-side.
Applied AI features:
Key technical work:
Built a multi-page financial planning dashboard in Streamlit + PostgreSQL to model a 40-year household projection, inspired by FIRE (Financial Independence, Retire Early). Upgraded from a scenario-based Excel financial model to a fully interactive, database-driven application with significantly expanded scenario and sensitivity analysis capabilities.
Key technical work:
Ported a Claude Code-developed, synthetic Netflix streaming analytics HTML dashboard (4 tabs, ~10K subscribers, 105K watch sessions) to a production Sigma workbook, rebuilding the Python data pipeline from scratch in order to generate Sigma-compatible CSVs that were added to and power a Sigma data model.
Key technical work:
sigma_export.py, expanding output from four CSVs to nine; added waterfall.csv, top_titles.csv, top_titles_by_plan.csv, device_summary.csv, and monthly_views.csv as pre-aggregated files for charts that could not be derived cleanly in Sigma from grain-level session datasubscription_plan dimension to cohort_retention.csv and waterfall.csv so that the plan filter control on the LTV tab correctly targets all three data sources
I recently collapsed several days of work into roughly three hours, and the end result was better than what I would have produced the traditional data science way.
The project followed a familiar path: read files into a Jupyter notebook, work through EDA and data transformations to understand what I have and figure out how to best synthesize and present my findings. The key difference for this project is that I used Claude Code throughout.
Python syntax I used to search for on Stack Overflow, Claude handled directly. When I ran into issues with the synthetic dataset, I asked Claude to review it and tell me if it saw the same problems that I did. Once we agreed on fixes, Claude normalized the data and kept a running record of the adjustments. Last, what would have been an intractable challenge for my front-end skills took about 20 minutes of iteration to produce a finished dashboard.
I’ve been using AI daily for analysis for several years, but the past few weeks ramping on Claude Code and Claude in Excel have felt much different. The ability to move from concept to working product quickly and spend most of my time on insights rather than implementation is here.
Migrated personal website from Jekyll to Hugo, replacing a stale Jekyll 3.6.3 + Minima stack (last updated ~2021, Ruby 2.3.1) with Hugo 0.160.1 + PaperMod theme, that is now deployed via GitHub Actions to GitHub Pages.
Key technical work:
<details>/<summary> (no JS), thumbnail support with full-width banner and lightbox, and a reusable partial as a single source of truth across the home page and Projects page
Thesis Today, most enterprise AI implementations are point solutions with a strategy-enforced ceiling. The greatest opportunity for an AI-native M&A or FP&A execution is one that: Establishes an enterprise-wide platform and decision engine that leverages deterministic workflows, parallelizes specialized agent execution, and compounds knowledge over time Evaluates and evolves its compute allocation strategy to drive scale and operating leverage, and Leverages a build vs. buy framework that provides future optionality to use best of breed solutions throughout Anthropic Is Building the Platform Opportunistically building the application layer (non-core focus) ...
Note: diagrams for the economic model and the structural workflow for the Super App can be found in the Appendix. As covered in part 1 of this series, the jagged frontier for live sports exists for the following reasons: Economics: advertising only/advertising mostly or subscription only/subscription mostly business models are likely not the path to profitable longevity, Discovery: customer discovery remains a challenge, even when leagues are available on the largest TV platforms out there, including Roku and Samsung, Fragmentation: a customer pain point for every league, from the NFL and NBA down to college sports and below, Customer retention: for subscription-based services, churn is an ever-present challenge, that arguably becomes more and more challenging as the proliferation of customer options continues, and Engagement share: Competition for engagement and time spent, when you’re competing against behemoths like Netflix, YouTube, and Meta, has never been more challenging and will only get more competitive. Recent Industry Announcements Versant Sports Portfolio: Versant, the Comcast cable network spinoff that began trading independently in January 2026, announced in November 2025 that “USA Sports” would be the unified brand and division name for its sports portfolio across USA Network, Golf Channel, and CNBC. The portfolio includes NASCAR, PGA Tour, Premier League, WWE, WNBA, USGA, LPGA Tour, and League One Volleyball (LOVB), totaling more than 10,000 hours of sports programming in 2026, approximately 1,000 of which are dedicated to women’s sports. ...
One of my favorite generative AI/AGI writers is Professor Ethan Mollick, whose latest book is “Co-Intelligence: Living and Working with AI” and who is also the author of the substack One Useful Thing. As someone with early access to the latest frontier models from labs including Open AI and Anthropic, he’s one of my first reads when the latest models are released so that I can quickly be up to speed on the advancements and new capabilities. And while Professor Mollick finds AI remarkable and shares compelling use cases for the technology, he also talks about “the jagged frontier”. The jagged frontier is a concept that describes the uneven and unpredictable boundaries of AI capability. In essence, AI can perform complex, expert-level tasks, such as math and coding, with remarkable efficiency and efficacy, while failing at seemingly simpler tasks that lie outside the capability reach of the models. In his GPT-5.5 post, he mentions that “every few months a new model arrives…[and] the size of the leaps grows each new release cycle. The jagged frontier is still there. It is just much further out than it used to be”. ...
Evaluating home purchase scenarios and associated investment opportunity costs. Photo by Breno Assis on Unsplash. Part 1 of Home Purchase Scenario Model Analyses. Introduction. For those who have previously considered or are considering the purchase of a home, then this post is meant for you to help customize your scenarios and vet your available options. I hope that you find the included financial model, available here, informative as you evaluate potential home purchase scenarios. This post and model should help you understand the full costs associated with home ownership and the inherent opportunity costs of not investing downpayment and ongoing maintenance costs in other assets, e.g., passive market ETFs. ...
Extending Stock Portfolio Analyses and Dash by Plotly to track Robo Advisor-like Portfolios. Photo by Aditya Vyas on Unsplash. Part 3 of Leveraging Python for Stock Portfolio Analyses. Introduction. This post is the third installment in my series on leveraging Python for finance, specifically stock portfolio analyses. In part 1, I reviewed a Jupyter notebook with all of the code needed to extract financial time series data from the Yahoo Finance API and create a rich dataframe for analyzing portfolio performance across individual tickers. The code also included a review of some key portfolio metrics with several visualizations created using the Plotly library. In part 2, I extended Part 1’s analyses and visualizations by providing the code needed to take the data sets generated and visualize them in a Dash by Plotly (Dash) web app. ...