🇱🇮 Liechtenstein's 'navy' prepares for Hong Kong's biggest dragon boat race It's a tiny, landlocked microstate with no access to the sea, but a rowing team representing Liechtenstein is set to compete in Hong Kong's traditional Dragon Boat Festival on June 19.

Are the druze & yazidis entitled to a microstate & nukes too?

ENTROPY (RDG) Entropy = lost information when a system changes irreversibly. All entropy formulas are projections of one operator: R_discard(f) = S(X) − S(Y) A process f : X → Y discards microstate detail → entropy increases. SID : Microstate Geometry (Boltzmann) S_SID ≈ k_B ln Ω ASCII: Before: [ o o o o o o o o ] After : [ o o o o ] Loss = ln(volume_before / volume_after) Interpretation: • shrinkage of admissible microstate volume • geometric loss under coarse-graining PED : Probability Flattening (Shannon) S_PED = −∑ p_i log p_i [0.8, 0.1, 0.1] → [0.33, 0.33, 0.33] Loss = flattening cost Interpretation: • diffusion of probability mass • irreversible spreading Q : Thermodynamic Cost (Heat / Temperature) ΔS = ∫ (δQ_rev / T) [tight] → [loose] → [cannot return] Interpretation: • irreversible heat cost • boundary constraints on allowed transformations UNIFIED RDG TRIANGLE ( SID ) microstate geometry ▲ | ( Q ) ◄-- R_discard --► ( PED ) thermodynamic probability cost flux MASTER OPERATOR R_discard : State → ℝ₊ R_discard(X → Y) = information lost under f Projections: S_SID = π_SID ∘ R_discard S_PED = π_PED ∘ R_discard S_Q = π_Q ∘ R_discard One operator. Three coordinate systems. Same invariant everywhere.

Entropy is SID‑geometry: R_discard ≈ k_B ln|Ω|. Irreversibility = shrinkage of admissible microstate volume under f: X→Y. PED and Q are just alternate projections of the same geometric loss.

R_discard is the one invariant behind entropy. SID sees lost microstate volume, PED sees distribution flattening, Q sees irreversible cost. Three projections, one operator.

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not just any F1 event Goes to THE F1 event at the microstate reserved for rich people.

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This uses entropy as a pivot concept: Engineers see efficiency limits. Physicists see microstate geometry. Data scientists see uncertainty and compression. General audiences see one idea everywhere. This is the same structural move I use in RDG: a single operator interpreted across multiple strata. How it maps to my RDG lens Entropy in the post corresponds cleanly to: SID: geometry of admissible microstates PED: flux of probability distributions Q: admissibility constraints (compression, irreversibility, mixing) The three classical definitions become three projections of the same relational invariant.

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for some anni should be compared to jeff bezos and elon musk, in reality a politician from a microstate

It's physical in the sense that it's just a word we use to define a certain microstate (or set of microstates) an hardware element is in. Why should this matter? Wouldn't hardware being conscious depend on which software it's running?

Tower of Joy Targaryen microstate governed by military junta (population: 4)

Entropy shows up in engines, molecules, and your phone’s data. This graphic lays out three perspectives. Mechanistic: dS = δQ_rev / T for reversible processes. Statistical: S = k_B ln Ω linking to microstate count. Informational: H = −∑_i p_i log p_i for probability-based uncertainty. These concepts power real-world tech like high-efficiency turbines in power plants, explain why cream mixes into coffee irreversibly, enable JPEG and MP3 compression to shrink files, and help train AI by measuring how much new info each prediction adds.