When I write, it usually clusters into three threads: founder mindset, the economics of code, and what I call the wrong-right way—the temptations that feel efficient in the moment and expensive later.
As teams get smaller and execution gets cheaper, the systems that create clarity disappear.
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Most people don’t have clarity on what their network actually produces.
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If you can’t explain your system clearly, you don’t actually understand it.
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Speed increases with AI—but clarity becomes the real bottleneck.
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How a whirlwind of AI insights and hands-on experiments is reshaping how we build, debug, and deliver value.
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AI doesn’t simplify Agile—it exposes how unclear your thinking already is.
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Revenue shows up—but most teams don’t have clarity on what actually drives it.
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Founders often mistake early code for progress. But the real challenge isn’t shipping features — it’s avoiding the trap of sunk cost thinking. This post breaks down why pausing before you commit is the smartest move you can make.
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Every time I asked my team to evaluate SaaS tools, I watched the same thing happen. A predictable resistance. A build-first mindset. And over time, I realized: it wasn’t just bias — it was culture.
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I use AI focus groups in my daily meditation! It's now part of my process. It's how I start my day on a bounce and not stuck in the grind of execution. How in the hell did AI get so far into the world that it's part of meditation?
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I am a developer. While I have no intention of going back to being a staff engineer, wrangling code, branches, PRs, and agile process, I do rely on my background to get things moving. Engineering rigor is the pattern. AI adoption in the tech community has some things to teach (to me).
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Forms used to feel like progress. Typeform was sleek, Google Forms was simple, and building your own meant control. But today, AI agents are rewriting the rules. They gather data with context, tone, and flow—no rigid input fields required. Going back to old-school forms feels like switching from Spotify to a cassette tape. The tools didn’t just age—they missed the AI shift.
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It seems like a long time ago that I decided to try a No-Code February. Going cold turkey on code has some interesting side effects. The math of applying AI coding tools to advising and consulting has forced some changes in how I approach all code projects: Launch By Lunch AI Accelerators, Vibe Coding Office Hours, and tech stack audits. And a renewed focus on the scoring site of the low-code cookbook.
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AI and low-code, in conjunction, are changing the core math for how 'code' is generated, priced, and sold. I am exploring how this math is changing, learning as I go.
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Low-code and Marcus Aurelius had a gentle battle in my startup brain. One quote triggered an insight, which triggered a pivot and then lead to Ai Accelerator that sold out in hours.
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No founder I know doesn't need more funding. In the back of every startup founder's brain is the wish to get the funding problem off their backs and work without distraction. It's unfair to say, stop, and embrace scarcity as a superpower.
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The last two months have been stressful for many founders working out of Venture Lane. EdTech startups have taken the brunt of the funding changes for Universities. It's not over yet, but there is a silver lining!
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I reworked my fractional CTO practice by changing my mindset. I had been operating like a German Casino, and missing the mark, but I needed to be more like Las Vegas. Make it stupid easy for a client to hire and pay.
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The next wave of software builders won't know how to code. And that's ok. The challenge for tech leaders will be bringing their work into a company's technology stack.
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A good low-coder is a cheap low-coder. Cheap means fast. It means you get to revenue and outcomes faster. It means you don't spend time and money on complexity. No matter how easy Google and Microsoft make getting started, it's still complex.
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A key part of startups technology stack is how they approach public vs private features. Usernames and passwords are the logic starting point. But there are other ways, that require a little imagination, but lower the impacts on roadmap and tech spend.
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Ok this week, I got off the Ai horse for a bit. I am a bit Ai saddle sore, if thats a thing. Turns out there is a limited, that even I can tired of. This week I did some reading, explored the Magma AI Agent Platform and built out a Beehiiv Identity solution. Happy Friday everyone!
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Scrambled codebases, competing NPM packages, duplication code and a high token burn are all part of the learning curve. I remain very focused on driving my code utility rate as high as possible.This is a rolling set of learning...
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Exploration of how much I can get AI to replace my "Code Brain" approach to building solutions.
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Week 2 was all about remapping a code approach to an AI code approach, with little learning lessons. Big insights are on the value of planning prompts to get better outcomes.
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This week has been all about the concept of the 'Mixture of Experts' AI concept.
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Low code is built on the work of 20+ years of code, SAS, and patterns. To talk about low code as a solution, as a stack, as a solution, we need some shared terms.
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Which database to start with is a key decision, as it impacts the technology you can select, the pattern of storage and the frameworks that work the best.
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Github is great for hosting code, git repositories, but it has other superpowers that often get overlooked.
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Usernames and Passwords are easy, but come with a roadmap cost later on as a project matures.
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Paul Krugman outlined the economics of "Car Brain" in his recent article about the congestion economics of cars, commuters, and the city of New Year. That concept resonated in a huge way! Thanks, Paul. It's nice to have a name for it!
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Don't over-thinking your hosting. No need to jump to EC2 or CloudFront.
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Using database schemas to scaffold API endpoints provides scalable endpoints and boilerplate-free codebases.
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Most people look past the value of Slack in building solutions. Don't.
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Deployment is a late-stage development process that should be included from inceptions of a project.
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This is the battle cry of many developers when faced with technology changes, recurring bugs, or scaling issues.
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