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The books worth starting with: they give you a threat model, a vocabulary, and the big picture before you open your first tool.

Pentesting, exploitation, and red team work: the books after which you have practice, not just vocabulary.

The hardest part to enter: assembly, operating system internals, and the analysis of malicious code.

The topic where reading the wrong book breeds overconfidence: how the primitives work, why they break, and why you do not write your own.

The block for developers: the vulnerabilities you introduce yourself, and the ways not to introduce them.

Blue team: monitoring, detection, incident investigation, and building systems that survive a compromise.

Social engineering, phishing, and the psychology of influence: why perimeter spending is bypassed with a single phone call.

Journalism and memoirs: real operations, real consequences, and names on the record.

Novels where the technology is described accurately enough that a professional does not wince, and which set the language of the industry.

Three generations of cards, different vendors, one failure pattern: 12VHPWR and 12V-2x6 examined through physics, safety standards and warranty terms.

The simulation hypothesis, who gets to be responsible, and how Formula 1 stopped treating driver deaths as normal.

Films where the tools on screen are real and the break-in follows the same steps it would follow in life.

Films with a primary source behind them: a court case, an investigation, or a memoir written by a participant.

The films that set the language the industry still uses when it talks to the public about hacking.

Blockbusters where security drives the plot, and individual scenes are useful as examples of what never happens.

Documentaries with no invented hacking scenes: interviews with participants, court material, and footage shot as events unfolded.

Series that can show an attack end to end: reconnaissance, preparation, mistakes, and consequences.

Why stories about "escaped" models keep appearing, what red teaming and ARA tests actually measure, and which problems are left outside the ring.

Why models produce excessive solutions by default, which natural brake on complexity disappeared, and how to use AI as an editor rather than a generator.

Why some companies take losses while others grow exponentially: a breakdown with examples from Shopify, Meta, Klarna, Duolingo, and others.

How honest dialogues with AI became a gift for corporations, and why the thinking should stay with the person while the routine goes to the model.

From a bitter late Soviet saying to corporations hunting for engineers, and to the new question raised by AI.

A protocol for working with several AI models as researcher, critic, and editor without outsourcing the thinking.

How TRIZ helps Samsung, Intel, and other companies identify contradictions, improve products, and solve complex problems systematically.

Sometimes a solution is perfectly logical, yet it fails because it never addresses the real problem. A look at why we solve the wrong task and how to see it more clearly.

A simple explanation of electricity through bicycles, water pressure, and an old sewing machine.

On AI costs, the limits of automation, and why effective AI use is becoming the new professional standard.

On writing, research, doubt, and conversation with artificial intelligence.

Why agentic AI systems need verifiable, time bound, action level access control.

A vision for a global trusted-contact network where people, specialists, and companies can be discovered through private trust paths.

Why knowledge is no longer the main advantage in the AI era.