I see engineering as a creative craft. Whether my canvas is healthcare, art, or e-commerce, I build beauty by creating elegant solutions for complex problems. I work best with a small crew, digging in with the business to find the one lever that can move a mountain. For me, success isn't just shipping a quality product—it's creating small empowered teams that grow the business.
An introduction to the U.S. healthcare system as "harder than rocket science" because it's a massively expensive and underperforming system plagued by "consolidated fragmentation." Despite enormous spending, outcomes lag behind other developed nations. The core issue is that the major players—insurers, hospitals, and providers—have grown into powerful, competing silos. This creates a gridlock that stifles innovation and prevents them from working together effectively to improve patient health, as each entity focuses on its own financial advantage.
As CTO at firsthand, I grew the tech team from 3 to 25 people so we could handle larger projects and more payer integrations. I led the effort to get our HITRUST r2 Certification. This improved our security and helped us land major business partners.
I also led the creation of helpinghand. It's our own AI-enabled care management tool that our care teams now use every day. I led partnerships and development efforts that deeply integrated that tool with multiple EHRs, claims data sources and common healthcare data feeds. To drive these results, I restructured our organization into small, empowered teams focused on business verticals, fostering a culture of ownership that accelerated our ability to deliver value.
I founded Kelp to solve a complex problem: getting people the right information at the right time. As a solo founder, I independently designed, built, and launched the contextual recommendation tool, engineering integrations across multiple workplace platforms. The process of taking Kelp to market provided a crucial insight: true contextual awareness is nearly impossible without deep, OS-level integration. This analysis led to my strategic decision to pause the project and share my work, open-sourcing the codebase and publishing my findings on the future of contextual computing.
I was a founding team member at Cityblock Health, which started at Alphabet's Sidewalk Labs. I focused on building the company's main software systems. This included setting up data sharing agreements with payers and deep EHR integrations including custom Chrome extensions. I also led the development of Commons, our own care management software and our tooling to integrate our data insights into that care management tool.
At Motivate, I was the engineering lead for the software that ran bike share systems in 10 cities, including New York's Citi Bike. I led the company's first project to make our payment platform PCI compliant. I managed our developers and contractors to complete projects that helped the business. We worked on things like improving the billing system to increase revenue and building new marketing tools.
I was the tech lead manager for the web engineering team at Artsy. My team handled the public website's architecture and performance. I led several major technical projects. We open sourced our frontend code and moved to an in-house isomorphic framework that improved our SEO. My team also built the software for live auctions and art fairs, which became important products for Artsy's business.
This post argues that building successful software for value-based care (VBC) requires a shift in mindset: create a Customer Relationship Management (CRM) tool, not just a better Electronic Health Record (EHR). VBC realigns healthcare incentives around long-term patient outcomes, succeeding through proactive, relationship-based care rather than transactional services. Technology's role is to support this relationship by helping care teams orchestrate interventions effectively. The most valuable tools are often simple and pragmatic, focusing on the unique, core needs of the care model and enabling proactive management of patient health.
Reflections on pausing the contextual recommendation tool, Kelp, concluding that its goal—getting people the right information at the right time—is nearly impossible for a third-party app to achieve. The core problem is technical: without deep, OS-level access to user data and behavioral signals, recommendations remain mediocre. True contextual help must be built into the operating system itself. The key business takeaway was the need to solve a highly specific, paying use case for a narrow audience before attempting a broad, cross-platform solution.
This reflection on leadership in a hyper-growth startup argues that self-management is the most crucial skill. Management in such a chaotic environment is inherently reactive and emotionally draining, not strategic and proactive. The key to effectiveness is to abandon "ruinous empathy"—the futile attempt to please everyone—and instead fiercely conserve personal energy for high-impact moments. This is achieved by accepting failure and tradeoffs as constant, communicating them transparently, and focusing on maximizing success in key areas rather than fighting every fire.
Brooklyn homeownership is not "worth it" as a financial investment. After accounting for renovation costs, high transaction fees, and the opportunity cost of not investing in the stock market, my profitable-on-paper sale was actually a financial loss. The true costs were the non-financial headaches: months of living in construction dust, battling city bureaucracy over permits, and fixing bank errors over property liens. I conclude that you buy a home not for the return, but for the control and satisfaction of making a space your own.
This post argues that as startups grow, the initial high-trust environment often collapses into chaos. The common leadership mistake is to push for more speed; the real solution is to slow down and rebuild trust through predictability. The author outlines a four-stage journey where a team matures by making and keeping progressively more abstract promises: evolving from committing to specific tasks (via ticketing systems), to achieving monthly goals, and ultimately, to delivering business impact measured by KPIs. This entire process is driven by retrospectives, which help a team understand its current level of trust and take the next step.
An engineer at a successful startup sold $x million in shares to diversify my wealth, only to discover afterward that they were just two months shy of qualifying for the QSBS tax exemption. This "hidden rule" would have eliminated their entire federal tax bill. The post serves as a cautionary tale about the immense financial cost of navigating the complex and often obscure tax laws surrounding startup equity without specialized knowledge.
Creating a successful engineering ladder isn't about perfect documentation—it's about building trust and psychological safety. The best ladders are actively used by teams and trusted across the organization because they're built on a foundation of "trust but verify" principles and open communication. Treat your ladder like a business relationship with SLAs, acknowledge potential failure modes, and remember that its success hinges on whether both engineers and management share a common understanding of its terms.
Finding smart, motivated people who work well together is virtually impossible. If Artsy has a secret sauce, it is how it hires. All else falls from the assumption that they have hired the best people who want to work together to achieve Artsy’s mission.
We all have stories, as engineers, of fixing some crazy thing at the last minute right before the demo goes up. We have all encountered situations where we needed to fix something that was our fault and we needed to fix it now.
Why Value-Based Care is Harder Than Rocket Science
This series argues that U.S. healthcare is "harder than rocket science" due to its "consolidated fragmentation," where powerful, siloed players hinder effective, affordable patient care. The main conclusion is that in an era of AI and consolidation, we need a major shift in data policy to deliver on the promise of improved quality of care at reduced cost.
An introduction to the U.S. healthcare system as "harder than rocket science" because it's a massively expensive and underperforming system plagued by "consolidated fragmentation." Despite enormous spending, outcomes lag behind other developed nations. The core issue is that the major players—insurers, hospitals, and providers—have grown into powerful, competing silos. This creates a gridlock that stifles innovation and prevents them from working together effectively to improve patient health, as each entity focuses on its own financial advantage.
This post explains that value-based care is a model that pays providers to keep patients healthy, contrasting it with the traditional fee-for-service system. I argue that while the goal is simple, implementing it is "harder than rocket science." Drawing from my experience, the post details the extreme difficulties involved, including navigating multi-year contract negotiations with insurers, building a massive and complex operational system before seeing a single patient, and the immense challenge of aligning internal teams who often have conflicting goals. I argue that this operational gridlock makes it nearly impossible to create a scalable, efficient, and truly patient-centered system.
Your official health record is a useless mess, fragmented across different doctors and insurers. I argue that advertisers at companies like Google and Amazon know more about your daily life and habits than your own physician. Because this foundational data is so broken, new healthcare models like value-based care are failing, and simply applying AI won't fix the problem until the data itself is fixed.
Value-based care contracts are challenging due to lengthy negotiations, strict and evolving security demands (especially post-Change Healthcare), and rigid terms that hinder innovation. These issues create financial strain for startups, making value-based care primarily accessible to large, established healthcare entities. In a world rapidly changing due to AI and at the policy level, we should set a target of contracts taking 1 month rather than 1 year. My recommendation is for contract standardization and data sharing processes that are either centralized or fully open-sourced.
September 2025
The wide business: VBC through the lens of operations research
Brennan Moore, Max Van Kleek, David R. Karger, mc schraefel
In this paper, we present an ongoing project designed to make self-reflection an integral part of daily personal information management activity, and to provide facilities for fostering greater self-understanding through exploration of captured personal activity logs.
Max Van Kleek, Brennan Moore, David R Karger, Paul André, M C Schraefe
Our system, Atomate, treats RSS/ATOM feeds from social networking and life-tracking sites as sensor streams, integrating information from such feeds into a simple unified RDF world model representing people, places and things and their timevarying states and activities. Combined with other information sources on the web, including the user's online calendar, web-based e-mail client, news feeds and messaging services, Atomate can be made to automatically carry out a variety of simple tasks for the user, ranging from context-aware filtering and messaging, to sharing and social coordination actions.
