Richard Golian

1995-born. Charles University alum. Head of Performance at Mixit. 10+ years in marketing and data.

#myjourney #myfamily #health #cognition #philosophy #digital #artificialintelligence #darkness #security #finance #politics #banskabystrica #carpathians

Castellano Slovenčina

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AI sales forecast: 9 traps so far

Yesterday I could not tear myself away from the computer. When I lifted my head, it was half past eight in the evening. I had been sitting alone upstairs for about three hours.
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Will AI take my job?

Will AI take my job? A certified Google trainer told me in June 2024 that my profession would cease to exist. Twenty-two months later, my job title has not changed — but ninety percent of what I do during the day is different. I have delegated more of my thinking to AI agents than I thought possible. I am not afraid. This is why, and what it means for anyone asking the same question.
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One hour. Fifty-five minutes. That is how long it took to build what a Czech software firm had quoted at over €50,000. I built it with Claude Code. Not a prototype. Not a proof of concept. A working tool — the one the company actually needed. By the evening of the same day, it was running on staging. This is not about Claude Code. It is about what Claude Code exposes.
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Richard Golian

Hi, I am Richard. On this blog, I share thoughts, personal stories — and what I am working on. I hope this article brings you some value.

I make mistake after mistake

Decision-making, probability and self-critique

By Richard Golian

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I often talk about my mistakes, wrong approaches, and lessons learned in my blog posts. Whether it is making purely result-oriented decisions, juggling too many tasks at once, misunderstanding AI, or delaying investments, I have made plenty of errors. I have also stumbled in more profound areas, like my worldview and understanding of our ability to know things. And let us not forget my social skills and communication blunders. As you can see, the list is long.

When it comes to dealing with mistakes publicly, I see people generally fall into two camps, with some in between but tending towards one side or the other.

One extreme is the person who tries to appear flawless. When they make a mistake, they look for blame everywhere but themselves and only admit fault when they have no other choice. I know several people like this, both professionally and personally. Honestly, during my teenage years, I was closer to this type. I wrote about this a few years ago.

On the other end is the person who, when faced with a mistake, first asks what they could have done better. They look inward. For example, if a new team member does not meet expectations, the department head might reflect on whether they could have provided better guidance, improved onboarding, or made a better hiring choice. The point of this post could be that now i tend to think more like this.

Summary

Every non-optimal decision is a mistake — regardless of the outcome. Even a successful decision can be suboptimal. This framework, inspired by poker player Martin Kabrhel, forces constant self-examination. My growth comes not from talent but from energy, perseverance, and the willingness to look at every decision honestly.
Richard Golian

If you have any thoughts, questions, or feedback, feel free to drop me a message at mail@richardgolian.com.

#artificialintelligence #performancemarketing

AI sales forecast: 9 traps so far

Yesterday I could not tear myself away from the computer. When I lifted my head, it was half past eight in the evening. I had been sitting alone upstairs for about three hours.
25 April 2026 291
#artificialintelligence #performancemarketing

Will AI take my job?

Will AI take my job? A certified Google trainer told me in June 2024 that my profession would cease to exist. Twenty-two months later, my job title has not changed — but ninety percent of what I do during the day is different. I have delegated more of my thinking to AI agents than I thought possible. I am not afraid. This is why, and what it means for anyone asking the same question.
23 April 2026 168
#digital #artificialintelligence

€50,000 Quote vs. Two Hours with Claude Code

One hour. Fifty-five minutes. That is how long it took to build what a Czech software firm had quoted at over €50,000. I built it with Claude Code. Not a prototype. Not a proof of concept. A working tool — the one the company actually needed. By the evening of the same day, it was running on staging. This is not about Claude Code. It is about what Claude Code exposes.
18 April 2026 484
#artificialintelligence #performancemarketing

Is AI Making Us Dumber?

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Common questions on this article's topic

Why treat every non-optimal decision as a mistake?
Because even a decision with a good outcome could have been better. In the article, this framework — inspired by poker player and mathematician Martin Kabrhel — redefines what counts as a mistake. The standard is not whether the result was positive, but whether the decision was as close to optimal as possible given the available information. By this measure, most decisions qualify as mistakes, which creates a powerful engine for continuous learning.
What is the difference between judging decisions by outcomes versus by process?
Judging by outcome means calling a decision good if things worked out and bad if they did not. Judging by process means evaluating whether the decision was optimal at the time it was made, regardless of the result. Annie Duke calls the outcome-based approach resulting and argues it is one of the most common thinking errors. In the article, the process-based approach is taken further: even successful decisions are scrutinised for how they could have been better.
Who is Martin Kabrhel and why is his thinking relevant?
Martin Kabrhel is a five-time World Series of Poker bracelet winner and the highest-earning poker player in Czech history, with over 18 million dollars in tournament earnings. He studied mathematics at Charles University and applies game theory and probability to decision-making. In the article, his perspective — that every non-optimal play is a mistake regardless of the outcome — is adopted as a framework for professional and personal growth.
How does treating mistakes this way accelerate personal growth?
By dramatically increasing the number of learning opportunities. If only decisions with bad outcomes are considered mistakes, most of the learning potential is lost. In the article, treating every suboptimal decision as a mistake — even those that happened to produce good results — forces constant self-examination. This approach, combined with energy and perseverance, is described as more effective than relying on talent alone.
Is this approach consistent with Carol Dweck's growth mindset research?
Yes. Dweck's research shows that people who believe abilities can be developed through effort outperform those who believe abilities are fixed — regardless of initial talent. The willingness to acknowledge mistakes and learn from them is a core feature of the growth mindset. In the article, this is expressed as: I do not think I am super talented, but I tackle things with great energy, perseverance, and a critical eye on myself.
Is this mindset realistic or does it lead to excessive self-criticism?
In the article, this approach is presented not as self-punishment but as a tool for clarity. The goal is not to feel bad about every decision but to maintain a habit of asking what could have been better. This is distinguished from two other approaches: trying to appear flawless (which prevents learning) and only looking inward when things go wrong (which misses opportunities hidden in successes). The key is honesty without paralysis.