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.
Don't chase Reddit, search, or press visibility just to be recommended by AI. Separate genuine usefulness, platform fit, and technical SEO from attempts to manipulate metrics.
Before investing in a voice agent, test whether the audience will engage by phone, whether the conversation completes useful work, and whether the result justifies the full cost.
If AI reduces the specialist hours behind agency execution, what remains? An analysis of strategic capability, staffing, and accountability for outcomes.
How should companies prepare employees when AI changes their work? I examine training, role design, and internal transitions together.
When the EU AI Act can cover companies outside the EU, how legal scope differs from practical enforcement, and why the EU connection matters.
Time saved with AI does not always need to become more output. Quality, lower overload, and management visibility are also real business value.
What Casey Newton’s LLM wiki reveals about using AI to connect new articles with an existing archive, research notes, and editorial decisions.
When is a healthcare chatbot an administrative tool, a high-risk triage system, or a medical device? A practical guide to the EU AI Act, medical-device rules, and data protection.
Risk classification is only the beginning under the EU AI Act. Company role, purpose, data flows, transparency, and other laws still need separate assessment.
What GDPR and Türkiye's KVKK require when chatbot conversations are sent to OpenAI, Anthropic, Gemini, or another third-party model provider.
Why I publish my publishing data, how an AI-assisted monthly report connects traffic, search, and publishing evidence, and why editorial responsibility still stays with me.
LLMs and AI search systems do not measure expertise directly. They infer it from visible online evidence. This article examines the gap between visibility, repetition, and tested expertise.
When does using AI become business value? I examine the links between model capability, completed work, operating capability, and measurable results.
A company-specific AI system is more than a model trained on company data. The real difference often lives in data, rules, tools, permissions, and measurement.
The EU AI Act explained: who it covers, prohibited practices, high-risk systems, transparency duties, application dates, and penalty ceilings.
When an AI system uses several models, should requests be assigned by explicit rules, predictive routing, cascades, or fallback paths?
PeşinTaksit was a neglected side project that still required four containers. With AI, I moved it to Cloudflare Workers in about an hour.
When should an AI system begin with one model, and what improvement in quality, cost, speed, or risk would justify adding another?
OpenAI's Codex research shows how AI use is shifting from questions to delegated work—and what companies should measure before calling it value.
AI can make employees and service providers more productive without leaving them with the economic gain. An analysis through salaries, hourly billing, and competition.
Whether data is sufficient depends on the decision it must support, how current it needs to be, and how the outcome can be verified.
A good model response, a completed task, and a measurable business result are three different outcomes. Here is how to evaluate each one.
When should an AI system prepare a recommendation, and when should it be allowed to execute a real transaction? A practical boundary for authority, approval, and verification.
When should enterprise AI use RAG, direct context, a database query, or an API? A practical way to choose the right source for each question.
Customer support, lead routing, REDAR, and incident diagnosis show the practical difference between software-defined workflows and AI agents.
When is providing a document enough, when does RAG help, and when does fine-tuning actually make sense? The decision starts with information, rules, and patterns.
Lead routing, financial disclosures, and bank integrations show where AI adds value—and where rules or conventional software are the better choice.
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.
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.
Why keeping technology running and using it to transform the business should not be treated as the same responsibility.
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.
Do AI visibility tools really work? They track selected prompt outputs and trends, not real-user brand visibility or commercial impact on their own.
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.
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.
AI is rapidly changing team capacity and software economics. That calls for a fair view of the past—and new expectations for today.
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.
I built ProductLog so founders can record their work as it progresses. The platform is live, and it remains a meaningful part of my work. But its premise also creates a tension: a well-kept public record makes a product’s story easier to follow and understand than scattered blog posts do. When AI can gather and process those records more quickly, that same clarity may become a useful shortcut for competitors.
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?
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.
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'.
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.
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.
How I moved from WordPress to a Nuxt and Cloudflare Pages setup, using AI to reduce publishing friction while retaining editorial and technical review.
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.
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.
On social media I usually share technical or work-related topics. But behind that image is also a dad with a 9-year-old son.
A year of running VaniBot taught us why healthcare chatbots depend on human handoff, internal APIs, multichannel integration, and continuous ownership.
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.