Superlogic, a startup that helps give consumers a way to apply rewards points toward experiences, has raised $13.7 million at a $200 million valuation, the company tells TechCrunch exclusively. Lin Dai, CEO and co-founder of Miami-based Superlogic, said…

Superlogic, a Miami-based startup specializing in experiential rewards technology, has successfully raised $13.7 million in a Series A funding

Superlogic, a startup that helps give consumers a way to apply rewards points toward experiences, has raised $13.7 million at a $200 million valuation,

Some leftover chores. I setup Calendly as my meeting scheduler, with Google Meet for web conferencing (I contemplated Jitsi but that does not integrate in Calendly as smoothly); I installed the Excali...

I recently left my role at Superlogic. I am not leaving technology. I am not stepping away from building. And I am not taking a traditional sabbatical. Instead, I am using this next chapter to focus on my family, help build a nonprofit, and teach my kids how to create real products and businesses with AI. That last part is the experiment I am most interested in. My kids are growing up in a world where the cost of turning an idea into working software is dropping fast. A motivated person can now move from concept to prototype in days, sometimes hours. But faster development does not automatically produce better products. AI can generate code, designs, copy, research, and business plans. It cannot reliably decide which problems are worth solving, whether users actually care, what tradeoffs are acceptable, or when a product is ready to ship. Those are the skills I want to teach. ## The goal is not to teach them prompting I am not trying to train my kids to become professional prompt engineers. I want them to learn how to build. That means learning how to: * notice real problems * talk to potential users * test assumptions * define a small first version * use AI to accelerate execution * inspect and challenge generated work * launch something publicly * measure what happens * improve it or shut it down The AI tools are important, but they are not the curriculum. The curriculum is judgment. A child who can ask an AI model to generate an app has done something interesting. A child who can explain why the app should exist, who it serves, how it might make money, where it could fail, and what should be built first has learned something much more valuable. ## Senternet is our workshop I have launched Senternet, a product and consulting studio. The name is not new: Senternet was my first company, founded 27 years ago, and reviving it now feels right. It is the umbrella under which I will build products, create apps, advise companies, and experiment with new business ideas. My kids will participate where it makes sense, not as passive observers, but as contributors. That may involve: * researching a market * naming a product * interviewing users * designing an interface * creating marketing assets * testing a prototype * reviewing analytics * helping decide what to build next Some projects will be client work. Others will be products we own. Some may become businesses. Some will fail quickly. That is expected. I want them to experience the entire loop from idea to execution, including the uncomfortable parts: unclear requirements, bad assumptions, bugs, rejection, and users who behave differently than expected. AI makes it easier to build the wrong thing faster. The only defense is learning how to think. ## We will use AI, but we will not outsource responsibility My background spans engineering, security, operations, product, design, and executive leadership. I have worked as a CEO, CTO, COO, CISO, founder, and builder. The most useful thing I can teach my kids is not a specific framework or programming language. Those will change. I can teach them how to reason through systems. When we use AI to generate software, they will need to ask: * Does this code actually work? * Is it secure? * What assumptions did the model make? * What data are we collecting? * Who owns that data? * What happens when the API fails? * How much will this cost at scale? * Are we solving the original problem or just adding features? * Would someone pay for this? * Should this exist at all? The model can produce an answer. The builder still owns the consequences. That distinction matters, especially for kids who may otherwise grow up believing that plausible output is the same thing as correct output. ## Bee Ready gives us a real-world problem to solve I will also be volunteering with Bee Ready, a nonprofit co-founded by my wife, Andi Senter, another physician mom, Elda Fisher, and me. Bee Ready is focused on improving emergency preparedness at youth sporting events through CPR and AED training, visible volunteer response teams, and better access to lifesaving equipment. I will serve as CTO and COO. That means helping with technology, operations, internal systems, volunteer coordination, data collection, and the infrastructure required to grow the organization. This gives us something better than a classroom exercise. It gives us real constraints. Nonprofits have limited budgets. Volunteers have limited time. Users may be stressed, distracted, or nontechnical. Systems need to work at fields, pools, and community events. Software cannot exist just because it is interesting to build. It has to reduce friction. It has to support the mission. And in this case, failure can matter. That makes Bee Ready an unusually meaningful environment for teaching product development, operations, and responsible technology. ## The stack matters less than the process We will use modern AI-assisted development tools. That will likely include coding agents, design tools, automation platforms, cloud services, analytics, and traditional development environments. But I do not want this project to become a running list of tools. Tools change too quickly. The repeatable process is more important: 1. Find a real problem. 2. Define the user. 3. Identify the riskiest assumption. 4. Build the smallest test. 5. Use AI to accelerate the work. 6. Review everything critically. 7. Put it in front of real people. 8. Measure behavior instead of collecting compliments. 9. Decide whether to continue. That process works whether the product is a mobile app, a nonprofit workflow, a consulting service, or a small online business. It also prevents AI-assisted development from turning into endless prototype generation. Shipping is not the end of the process. Shipping is when reality starts grading the work. ## I expect them to fail I do not expect every project to succeed. I would be concerned if they did. A project nobody uses can teach positioning. A product nobody buys can teach pricing. A confusing onboarding flow can teach design. A security mistake can teach threat modeling. A feature that takes three weeks and adds no value can teach scope control. The point is not to manufacture a string of impressive launches for social media. The point is to help them develop the ability to recover, adapt, and keep building without confusing failure with personal inadequacy. AI reduces the cost of experimentation. That should make us more willing to test ideas, not more attached to every idea we generate. ## This is also an experiment in education Traditional education often separates disciplines. Programming is one subject. Business is another. Writing, design, finance, operations, and marketing live somewhere else. Building a product combines all of them. A small software project can require a student to write clearly, think mathematically, understand users, evaluate tradeoffs, manage time, communicate decisions, and accept criticism. AI can help at every stage, but it also creates a new educational problem: students can produce work they do not understand. So one rule will be simple: You should be able to explain what you built. That does not mean memorizing every line of generated code. Professional developers already rely on frameworks, libraries, abstractions, and tools they did not write. It means understanding the system well enough to reason about its behavior, limitations, risks, and purpose. If you cannot explain why it works, you are not done. ## What I plan to write about I intend to document this experiment. That may include: * how we choose projects * how I teach kids to use coding agents responsibly * where AI-assisted development works well * where it creates hidden problems * how we validate product ideas * how we structure small family projects * lessons from building nonprofit technology * mistakes we make * products we launch * projects we decide to kill I am especially interested in the gap between generating software and building a business. That gap is still enormous. AI is making implementation cheaper. It is not making customer understanding, distribution, judgment, leadership, or accountability obsolete. In many cases, it is making them more important. ## A different kind of career move Leaving a day job is often framed as a dramatic leap into entrepreneurship. This feels different. I am not betting everything on one startup. I am building a studio, helping operate a nonprofit, spending more time with my kids, and creating a place where we can test ideas together. For most of my career, I have helped companies build products and solve technical problems. Now I want to apply that experience more directly, while teaching the next generation of my family how to create rather than simply consume. We will use AI heavily. We will also question it, test it, reject its output, and take responsibility for what we ship. That is the part of AI-assisted building I think matters most.