Michael Lund.
I build AI tools that clear my own bottlenecks, and I get inside an unfamiliar system fast enough to fix it before anyone asks.
Things I built and ran.
Wage Garnishments: What the Company Must Do to Support Them Correctly
A payroll-tech company gave me this as a take-home during a Chief of Staff interview. I had about four hours and no background in the domain. I read the statutes, mapped what the payroll platform handles against what the company has to own itself, and wrote a recommendation the team could act on.
What the briefing does
- It names the real problem in one line. The math is easy. The hard part is moving money to the right place on the right clock and answering the court.
- It gives a phased plan. Launch the common order type now, run the rest on a written manual playbook, and let real volume decide what to build first.
- Every claim carries a confidence marker and a source. Statutes cite the statute, not a blog.
- It reads for a busy person. There's a glossary, a capability map, a competitor teardown, a set of questions for each stakeholder, and a checklist for the first order.
I built this for an interview exercise. It isn't legal advice, and the company names are blacked out.
Crypto sentiment and random forest strategy
A BTC and ETH strategy that predicts 15-day-average returns and sizes each position by how confident the model is. It runs a random forest on price data, a local FinBERT model on news sentiment, and a Google Trends signal. We backtested it on QuantConnect. It took 2nd in Duke's FinTech Trading Competition. The competition scores on Sharpe ratio, so a high finish rewards steady risk-adjusted returns.
What I owned
- I designed and coded the piecewise importance function. It turns the model's predicted move into a confidence weight, and we blended it with a modified Kelly criterion to size positions. That sizing layer made the final strategy.
- I built the Google Trends signal with pytrends and got it working in the QuantConnect backtest.
- I split the team into role-based workstreams and edited the final report.
The strategy (team)
- A random forest predicts direction and size on 15-day-average returns, which cut the daily noise. We benchmarked it against linear regression.
- A local FinBERT model read news sentiment in-house, with no data sent out. It fed dynamic working-memory features.
- Drawdown and liquidation controls held the backtest positive through the 2021 to 2022 crypto crash, when buy-and-hold dropped hard.
CrowdVolt market-expansion pilot
CrowdVolt has traction in New York and Miami, so the opening is to enter cities where the product is strong but listings are thin. I designed a low-cost experiment to do that: hand out free CrowdVolt-branded reusable earplugs at a major EDM festival in a market like LA, San Francisco, or Chicago. Each case carries a tracked QR code, so a giveaway that solves a real need at the show ties straight to app installs, signups, and transactions.
What I designed
- A market-selection rubric: pick a city with low CrowdVolt listing density, a large EDM base, one concentrated festival, and a full follow-on concert calendar.
- The acquisition funnel and metrics, from cost per scan to customer acquisition cost, new listings, and GMV from acquired users.
- A before, during, and after operating checklist, plus a decision rule to scale, revise, or stop on the readout.
The economics
- Sourced three overseas suppliers and compared quotes on the branded case.
- The recommended vendor runs about $0.18 a unit at 5,000 units for the preferred black molded case, so a real-volume pilot stays cheap.
- Distribution treats promoter and venue permission as part of the test, so a win also builds relationships for entering the next city.
This is the plan, economics, and playbook. It is a proposal, not a campaign I ran.
Trading operating system
Daily P&L is noisy, so I built a system to tell luck apart from skill and work on what I actually control. It started as a daily report card and grew into morning prep, end-of-day reviews, weekly pattern checks, automated reporting, and AI workflows that summarize my notes and flag the mistakes I repeat.
What it does
- A daily report card logs my thesis, execution, risk, mistakes, and state of mind.
- Morning prep and end-of-day reviews run every day, with a weekly pass to catch patterns.
- AI workflows condense my notes and surface the errors I keep making.
- Automated reports track how closely I follow the system and my ratio of green to red days.
Claude market-news feed
I kept losing time reading news during trades, so I built a feed. Web scrapers pull market-moving headlines and language models summarize them faster than I can read the source. Nobody assigned it. I hit the bottleneck and built the fix.
What it does
- Scrapers watch the sources and language models summarize each catalyst as it lands.
- It turns minutes of reading into a few seconds when the tape is moving.
- It set the pattern for how I work now. I see a bottleneck, build the tool, and keep it running.
It's a live trading edge, so I keep the details private. A cleaned-up version will go on GitHub.
Notes aggregator
Trading threw off more notes than I could keep straight. I built a tool that pulls the scattered note-taking into one pipeline so I actually review it. It gave me back about two hours a day.
What it does
- It pulls notes out of a dozen places into one feed.
- It summarizes and orders the day so end-of-day review is quick.
- It freed up about two hours a day I used to spend sorting notes.
Built at work. The code on GitHub is a cleaned-up version.
Sell-through dashboard
I spent a week shadowing a friend's 20-person ticket-resale business to learn how they moved inventory. Then I built them a Claude dashboard that models sell-through targets in the weeks before an event, so they can release inventory over days instead of dumping it. It was the first thing I built for them, and they still run it themselves.
What it does
- It models sell-through targets before an event and guides how to release inventory over time.
- The team runs it on their own. It isn't real-time, and it doesn't place buys or sells.
- It came out of a week of shadowing. Get into the system fast, find the thing that moves it, build the tool.
While I watched a few live drops, I saw the team working off memory and steps slipping. I proposed writing down each person's job per drop. That became the playbook below.
Ticket-drop operating system
I watched a few live ticket drops and saw the team running off memory. Steps slipped under pressure and money got left on the table. I worked with the founder to turn the drop into a repeatable system: seven roles, each with a checklist for before, during, and after, so nothing gets missed when it's fast and live.
What it defines
- Seven roles cover the whole drop: ops manager, research analysts, traders, tech, pricer, inventory manager, and fulfillment.
- Each role has an overview, its responsibilities, and checklists for before, during, and after.
- Ownership and hand-offs are written down. Who decides, who buys, who prices, who confirms.
Difmo, my DJ and events company
When bars reopened after Covid, I started a DJ and events company at Duke. I became the resident DJ at Shooters, Duke's main nightclub, and played events for clubs and organizations across campus. It was the first thing I built and ran as my own business.
Highlights
- Resident DJ at Shooters, Duke's main nightclub.
- One of my mixes played at Coach K's Countdown to Craziness, Duke basketball's sold-out season opener.
- Booked and played campus events end to end, from the booking to the night itself.
Where I've built and operated.
- Manage an intraday U.S. equities book, synthesizing Level 2 order flow, tape, technical charts, and real-time news to execute discretionary trades and manage risk.
- Run an asymmetric, catalyst-driven book, concentrating maximum size into a few high-conviction setups with defined downside risk on each position.
- Built a proprietary Bloomberg-style market-news feed with Claude-assisted development, web scrapers, and language-model APIs that shaves critical seconds off intra-trade information processing.
- Built a personal operating system (Atomic Habits framework) plus trade-prep and review tooling: a notes aggregator that cuts ~2 hours of daily note-taking and an AI coach that flags behavioral patterns from daily report cards.
- Mentored four junior traders on setups, risk rules, habit formation, and structured review; all four subsequently recorded multiple consecutive profitable months during their first year of trading.
- Built a Claude-powered dashboard for a 20-person ticket-resale business that models sell-through targets in the weeks before an event, giving the team a planning read on how to release inventory over days; the team runs it themselves.
- Collaborated on-site on live buying decisions during launch week, including the Gracie Abrams and The Chicks ticket drops.
- Advise the team on buying across the EDM and dance-music market, applying firsthand domain expertise to flag mispriced inventory and demand trends.
- Owned all product deliverables for a cross-functional intern capstone built on Suncor's business needs (user stories, PRD, and roadmap) and ran daily standups to coordinate the engineering team.
- Managed sprint planning in Jira and Confluence and prototyped in Figma, translating requirements into a prioritized backlog.
- Partnered with engineers to deliver a full-stack digital prototype, iterating on features through continuous stakeholder feedback.
- Crafted a compelling pitch deck facilitating a successful Series B financing round, securing $25MM in investments through targeted market research and product strategy.
- Constructed a 50-slide Confidential Information Memorandum (CIM) deck, offering detailed insights to potential investors.
- Conducted financial analysis, driving strategic decisions to optimize financial performance and growth trajectory.
- Partnered with trading-software company TrendSpider to drive customer acquisition and funded-account growth through an incentivized-funding program.
- Launched and managed targeted digital and social-media campaigns, implementing UTM tracking and analyzing Google Analytics data to evaluate performance and refine targeting.
- Created graphics for tutorial videos and blog posts to support marketing content and user education.
Skills and education.
Skills & Tools
Education
Defined risk, asymmetric payoff.
Figures are realized percentage gains. Past results don't predict future ones.
Let's talk.
I'm looking for an operator seat at an early-stage company: chief of staff, founder's associate, or ops. Email is the fastest way to reach me.