AI is not for the nerds.
The biggest shift in human history doesn't belong on your IT department's desk. It belongs on yours – where strategy meets execution.
Three years ago, I started building AI systems the way I thought everyone would build them: fast, minimal dependencies, straight to users. Then I looked around. Enterprise teams were still writing governance frameworks. Consultancies were still delivering PowerPoints. Offshore teams were still treating AI like another software project.
They're not slow because they lack expertise. They're slow because their structures make speed impossible.
So I keep building. Each project teaches me what theory cannot: what actually works, what doesn't, and why small teams have leverage right now, before that window closes.
AI is the fastest-adopted economically significant technology in human history. When a billion people hold this kind of power, everything changes at once. Education, work, communication, entire industries – already shifting. Much more coming.
This site is for founders who'd rather build than wait for certainty.
The window is 2025–2028.
Year of the Agent
Automation cliff. AI agents handle knowledge work at scale. Companies that move now capture the advantage.
Year of the Robot
Physical automation follows. What started in software extends into the physical world.
ASI Emergence
Artificial superintelligence. The questions shift from "can AI do this?" to "what should AI do?"
Societal Response
Policy catches up. By then, the early movers have already reshaped their industries.
The companies that understand this timeline are moving now—not waiting for certainty.
Where small companies win
→ Knowledge work niches
- • Customer needs are standardized
- • Distribution is digital
- • Trust barriers are low
- • No regulatory moats protecting incumbents
Here, speed and AI fluency matter more than size.
— Where scale still wins
- • Heavily regulated industries
- • Physical goods and logistics
- • Anything requiring institutional trust
- • Complex multi-stakeholder environments
Large organizations maintain advantages here—for now.
145+
Repositories. I learn by shipping—across healthcare, property, brand education, social monitoring. Real products, real users, real patterns.
250+
Deep collaboration with AI. I see multi-agent orchestration where others see chatbots—and I think about what this means for work, economics, and society.
<30 days
From concept to working product. Speed compounds—every week of building is learning that accumulates.
What I Believe
These aren't abstract opinions—they're patterns I've observed building across industries. This is the foundation for how I think about AI and business.
AI is not IT
This isn't a technology upgrade to delegate. It's a fundamental shift in leverage that belongs on the founder's desk. Understanding it yourself changes how you see every decision.
Speed is the advantage that compounds
Every week you're learning and building is a week your knowledge compounds. Waiting for certainty means falling behind those who are figuring it out in motion.
Small teams have a window
Right now, a small team that moves fast can do what used to require departments. This window exists because the technology is new and the playbooks haven't been written.
Outcomes over process
What did you ship this week? Who's using it? These questions matter more than roadmaps and governance frameworks. Results teach faster than planning.
The bigger questions matter too
Post-labor economics, AGI control, the nature of intelligence itself—these aren't distractions from building. They're context for why this moment matters so much.
"I'm looking for founders and leaders who sense this shift and want to understand it—both the opportunity and the deeper implications."
What I've Built
This isn't a portfolio—it's proof. I've validated patterns across healthcare, property, brand education, and social monitoring. Each project taught me something that theory couldn't.
I'm not a consultant who advises from the sidelines. I'm a builder who's shipped and seen what works—repeatedly, at speed.
MedHubAI
Empathic, controlled, and compliant AI conversational platform now running at 10 clinics. Built to understand that healthcare isn't just about answers—it's about how you deliver them.
Lesson learned: Compliance and empathy aren't at odds. The best healthcare AI feels human while remaining auditable.
Property Analysis System
8 API integrations pulling satellite imagery, planning data, flood risk, transport links, and market intelligence. Turns any address into a comprehensive investment thesis.
Lesson learned: The power isn't in one data source—it's in orchestrating many. Real insight comes from synthesis.
BrandGuide.me/AI
RAG-based brand education platform that teaches marketing strategy through conversation. Your brand, your data, your personalised AI mentor.
Lesson learned: Education scales better through AI than through human hours. But the human expertise has to exist first.
claude-code-design-skill
Claude Code skill that provides AI-powered UI/UX design assistance to developers from the command line, automating design suggestions. Simplifies the frontend UI implementation process by integrating design decisions directly into the developer workflow. The skill also incorporates my own 20 years of design, UI, and UX experience. After publication, Anthropic (owner of Claude Code) was inspired to write their own skill. Not sure if mine inspired them, but who knows?
Lesson learned: The best developer tools don't remove creativity from the designer's hands—they accelerate iteration so more time can be spent on decisions that truly matter. AI as a collaborator, not a replacement for human workforce.
AGI Detector
Early warning system monitoring 7 leading AI sources (OpenAI, DeepMind, Anthropic, Microsoft AI, arXiv, TechCrunch, VentureBeat) for real AGI signals, with cross-referencing and severity classification (low, critical, etc.). Uses historical trend tracking and pattern recognition to distinguish normal AI progress from potential AGI breakthroughs, based on signals like recursive self-improvement, meta-learning, or cross-domain generalization.
Lesson learned: Weak signal detection isn't about perfect accuracy—it's about ensuring no critical signal goes unnoticed. With AGI, Type II error (missed detection) is far more dangerous than Type I (false alarm).
Foreign Currency Loan Copilot
AI assistant for Hungarian foreign currency loan victims, providing personalized advice based on legal and financial data for analyzing loan constructions and exploring compensation options. Democratizes legal assistance by making complex financial information understandable and accessible. Must be installed locally to ensure data security.
Lesson learned: The information gap is the deepest form of inequality in the modern era. AI's true social value isn't in efficiency gains—it's in democratizing expert knowledge for those who were previously vulnerable, like foreign currency loan victims against banks.
Anna, the Oncopsychology Assistant
AI assistant developed with oncopsychologist Dr. Ágnes Riskó, providing emotional support to oncology patients from the onkopszichologia.hu content. Anna was presented at the XXXV Congress of the Hungarian Society of Oncologists. Specifically serves the purpose that a language model can better convey complex professional material through a conversational interface than if patients or their relatives had to search for minutes on a complex website. Works with full anonymity but does not replace medical consultation.
Lesson learned: Communicates humanly while remaining transparent—clearly defining boundaries (not medical advice) doesn't weaken the value of help, it protects both the user and the technology. True innovation isn't in AI replacing the specialist, but in functioning as a 24/7 first line while consciously redirecting to human specialists when needed.
Regional Automation Processes
Dashboard measuring automation vulnerability of regional labor markets, combining employment data, land registry data, and automation methodology with AI analysis. Monthly-updated early warning system broken down by cities, where job automation and real estate market instability could trigger dangerous processes.
Lesson learned: Macroeconomic changes don't happen in isolated sectors—labor market, real estate market, and technological development form interconnected feedback loops. The power isn't in one data source—it's in analyzing the connections between many data sources. Real insight comes from synthesis.
The Pattern I Keep Seeing
Speed compounds
Every week of shipping is learning that compounds. Every week of planning is learning that doesn't.
Orchestration wins
Single AI models aren't the breakthrough. Multi-agent systems that orchestrate context, tools, and memory—that's where magic happens.
Domain depth matters
The AI isn't the hard part. Understanding the domain deeply enough to know what to build—that's the unfair advantage.
The Conversation
These aren't marketing channels. They're distribution points for a belief system. Join the founders and leaders who want to understand what's actually happening—and why it matters.
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