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Rough notes on 10 books: 5 of philosophy, 2 of fiction, and 3 technical. These are guides to what each book argues, not reviews. Links go to catalog pages.
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- Attacks the idea that philosophers find truth neutrally. Each system, he says, is a confession of the temperament that wrote it.
- Splits morality into two types: master morality, which starts from what is noble, and slave morality, which starts from resentment of the strong.
- The will to power is the working idea: drives want to grow and overcome, not only to survive.
- 296 short sections and aphorisms, so it reads fast and argues loosely. Easy to quote badly; the context of each section matters.
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- A student, Raskolnikov, reasons his way into murder with a theory that extraordinary people may step over the law.
- The crime takes 1 part of the book; the other 5 are the theory failing inside his own head.
- Porfiry, the investigator, has almost no evidence. He wins by modeling how his suspect thinks and waiting.
- Best read as a test of an idea against a person. The epilogue is the weakest stretch and the most argued about.
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- A murder mystery wrapped around 3 brothers who stand for body, intellect, and faith.
- Ivan’s chapters, Rebellion and The Grand Inquisitor, make the strongest case against God that Dostoevsky could build, and he was a believer.
- The answer comes as a life, not an argument: Zosima and Alyosha are meant to outweigh Ivan by how they act.
- Long and digressive. The payoff is that no side is strawmanned.
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- Opens with the claim that the one serious philosophical problem is suicide: whether life is worth living without a given meaning.
- The absurd is the gap between a mind that wants reasons and a world that gives none.
- Rejects both exits, suicide and the leap into faith, and argues for revolt: living fully with the gap in view.
- Short, and closer to an essay than a proof. The last line about imagining Sisyphus happy is asserted more than it is argued.
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- A private notebook by a Roman emperor, 12 books, never meant to be published.
- The core move is Stoic: separate what is up to you (judgment, action) from what is not (outcomes, other people).
- Repetitive on purpose. He is drilling the same few ideas because he keeps failing at them.
- Thin on argument, so it works as practice more than as philosophy to debate.
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- A miner from the lowest color caste on Mars is remade as a Gold and sent into the ruling class’s academy.
- The Institute is the engine: students are split into 12 houses and told to conquer each other, with almost nothing explained.
- Reads as a study of how hierarchies justify themselves, and of what infiltration costs the person doing it.
- First of a series. Fast and brutal, and the sequels widen the scope a lot.
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- A noble family is handed the desert planet that produces the one resource the empire runs on, and is destroyed for it.
- A warning about heroes: Paul sees the holy war his own legend will cause and cannot find a path that avoids it.
- Politics, ecology, and religion are treated as one system. Each faction plans on a scale of generations.
- Dense with invented terms at the start; the glossary helps.
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- Asks how a self can come out of meaningless symbols, using formal logic, Escher’s drawings, and Bach’s fugues as the same pattern.
- The key idea is the strange loop: a system that, by moving through its levels, ends up back where it started.
- Walks through Gödel’s incompleteness theorem from scratch with an invented formal system.
- Very long, with a dialogue before each chapter. Its guesses about AI are from 1979 and show it.
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- Argues that fixed goals can block the path to breakthroughs, since the stepping stones often do not resemble the goal.
- Grew out of novelty search, where a system is rewarded for being different rather than for hitting a target.
- A short, general-audience read. Good for the idea behind open-ended and quality-diversity search.
- Limit: it is a thesis, not a method manual, and the claims reach well past the lab results behind them.
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- A compact, clear intro to genetic algorithms: the algorithm, example applications, and some theory.
- Includes genetic algorithms in machine learning, modeling of evolution, and a look at how well the theory holds up.
- Suits newcomers who find the larger classics heavy. A quick way to get the core ideas.
- Dated: written in the mid 1990s, so nothing on newer methods or deep learning era work.