AI where it changes the business
AI matters when it changes the economics or operations of a real business: the work people do, the decisions they make, the information they can use, or the time a process consumes. These essays begin with that consequence, then examine whether AI is the right intervention.
When Does Process Automation Actually Need AI?
Lead routing, financial disclosures, and bank integrations show where AI adds value—and where rules or conventional software are the better choice.
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Start With the Business Problem, Not the AI Model
Why model selection should not be the first decision in an AI project, and what must be defined about the business outcome, action boundary, and evidence first.
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Preserving API Quality in AI-Assisted Development
A short guide to preserving resource models, HTTP behaviour, error handling, authorization, security, and contract decisions in prompts, reviews, and delivery checks when building REST APIs with AI.
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Managing Technology and Transforming the Business Are Not the Same Thing
Why keeping technology running and using it to transform the business should not be treated as the same responsibility.
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Keeping Customers Happy Isn’t Enough. You Have to Follow Up.
A positive customer experience does not automatically turn into a review or recommendation. A small observation about follow-up, timing, and how AI changes the cost of building useful software.
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Do AI Visibility Tools Really Work? What They Actually Measure
Do AI visibility tools really work? They track selected prompt outputs and trends, not real-user brand visibility or commercial impact on their own.
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llms.txt Was Never the Point
A closer look at the AI Visibility hype around llms.txt, the information architecture experiment I built, and the difference between a technical hypothesis and a marketing promise.
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Do You Know How Dependent Your Company Is on AI?
AI is not only accelerating work people already did. It is creating operational capacity companies could never afford to have — and that capacity needs a continuity plan before it becomes structural.
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AI Wasn’t Always This Good. We Just Got Used to It.
AI is rapidly changing team capacity and software economics. That calls for a fair view of the past—and new expectations for today.
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AI Made Code Cheap. Verification Is Still Expensive.
AI can produce an implementation in hours. If its author cannot explain the decisions behind it, the real engineering work has not disappeared — it has moved to the reviewer.
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The Threshold Collapsed: What ProductLog Taught Me About Building in Public
In April 2026, Arvid Kahl revisited the argument that had helped define build in public for many founders. He had once seen a practical threshold: companies became more guarded when they reached roughly $20,000–$30,000 in monthly recurring revenue. In his view, agentic coding changed that calculation. The old threshold had, as he put it, “effectively collapsed to zero.”
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Build in Public in the AI Era: What to Share and What to Keep Private
Building in public does not require choosing between silence and publishing an operating manual for your competitors. The useful question is narrower: for each thing you could share, will the feedback, trust, or connection it creates be worth the risk of making it public?
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AI Visibility Dropped Before Search: The Google Data That Changed My Theory
Google's Generative AI report made me revise my live-fetch theory. The observed lead over search held, but the data does not prove a separate AI index.
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The Era of the "Previous Vibe Coder" Begins: The Invisibility of Clean Code and the Technical Debt Bill of AI
An analysis of the false speed illusion of AI coding, DRY violations, the devaluation of invisible quality using Akerlof's Market for Lemons, and the upcoming crisis of the 'Previous Vibe Coder'.
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The Hiring AI Makes Invisible
Layoffs are counted. The role a team never opens is not. A CTO and founder's view of how AI can change the hiring decision before it appears in the data.
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Comprehension Debt: The Bill Comes Due Alone
I ship with AI coding agents daily. The hidden risk is the gap between what the system can do and what I can still explain, debug, and safely change.
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From WordPress to Nuxt: Building an AI-Powered Content Pipeline
How I moved from WordPress to a Nuxt and Cloudflare Pages setup, using AI to reduce publishing friction while retaining editorial and technical review.
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The AI Visibility Illusion: What Bing's Citation Share Data Actually Reveals
AI Visibility tools model a selected set of prompts. Bing's first-party Citation Share data gave me a way to compare that model with what I was seeing across the sites I manage.
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The End of Coding or a New Renaissance? The Invisible Crisis of AI
AI may reduce the routine work through which junior developers learned. The more consequential question is how organisations will preserve practice, feedback, and the path to senior capability.
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Raising Children in the Age of Artificial Intelligence
On social media I usually share technical or work-related topics. But behind that “tech guy” image there’s also a dad with a 9-year-old son at home. Most…
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What We Learned Building a Healthcare Chatbot for International Patients
A year of running VaniBot taught us why healthcare chatbots depend on human handoff, internal APIs, multichannel integration, and continuous ownership.
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Building camiler.org: A Programmatic SEO Experiment with Google Maps and OpenAI
How I built camiler.org with Diyanet data, Google Maps, OpenAI, Fastify, and Nuxt, and why the automated SEO experiment became a human review workflow.
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