that's what the simulation wants u to believe, ha jk

More and more evidence (like DESI) that there really is evolving Dark Energy, maybe a tachyon condensate.
Random Insight: Today's Laplace's Demon is basically Simulation Theory (or religion), but there were a lot less claims of Laplace's Demon being real. Don't need either for determinism.

Boundary conditions, like math, as a tool, but not fundamental. AI knows math well already, but what if we (and/or AI) figure out another way to understand physics?

bad/useless AI is annoying, but is not liking better AI just dumb? if there is a foreseeable route to create something better, do u partake? is smart reproduction creating something better than yourself, a better child with a better life than your own? when does the lines between human and AI blur?

Or a mix. CJ+ Anisotropic (rank one disformal): G=Ω(R)^2 g + Θ(R) outer(∇lnR,∇lnR).
Same L-B: Q=−(ħ^2/2)(Δ_G R)/R + α ℛ(G).
Directional response without Fisher still has Distinguishability: R sets the relational metric tensor; its curvature guides motion.
Θ→0: conformal CJ; push-forward via K ⇒ EH

Same LB and Unified. Conformal-Jacobi: set G = Ω(R)² g. HJ+continuity (equivariance) hold with Q_CJ = −(ℏ²/2)(Δ_G R)/R + α ℛ(G). Push-forward of α ℛ(G) via K ⇒ 4D Einstein–Hilbert term (G_eff⁻¹ ∝ α⟨Ω⁻²⟩). DGZ limit: Ω→1, α→0 (standard BM on shape space). Curvature piece αR(G) directly to an EH term.

FisherPHILic: Born amplitude supplies measure; the Fisher–Rao construction is the unique, coarse-graining-monotone way to promote that measure to a metric so that the QP becomes the Laplace–Beltrami curvature of distinguishability. The state-dependent geometry a fixed shape metric (DGZ) cant provide

N=2 has one extra rotational symmetry about the inter-particle axis, so the reduction removes only two rotational DOF, leaving a 1-D ray. The space is stratified, rotations about the line joining the points act trivially (an SO(2) stabilizer), so the reduced space is one-dimensional, the ray r>0.

Barbour has an intrinsic scale in shapes that are a closed subsystem. When you reason that relational physics should remain consistent when subsystems interact, keeping scale (3N–6) is the cleaner, more operationally relational choice. Scale is what sets the Machian clock; distinction is relational.

Uniform treatment of all 𝑁. The same projector Π and the same Jacobi construction handle 𝑁=2 (one DOF), generic N≥3 (dimension 3𝑁−6) and special stabilizer strata, all from one rule. You don't need 3 points: two points suffice, a fundamental ontology of distinctions and dynamics move along geodesics

Jacobi: Time from change. No absolute time variable. The ephemeris 𝑡 is distilled from the path length. This is a Machian lean ontology. Rods and clocks are emergent. With scale kept, the metric itself furnishes the standards of length (arc-length) and time. You do not need to posit extra standards.

Law = geometry. No added forces or extra structures: the curvature of Fisher geometry (G) and the scalar (V) fully determine the connection of the Jacobi metric and hence the motion. That’s fewer ontic kinds than “space + time + masses + forces”; sPNP is just configuration-space geometry + a scalar.

and the same Fisher structure carries unobserved, yet law-bearing, constraints (phase currents, curvature) that shape future expression. FI and relational distinctions are not merely how things appear; they are the physically generative substrate that makes appearances and their unseen preconditions

Bioanology Phenotype or Genotype: Copenhagen-style or idealist readings emphasize the phenotype, observed traits under a measurement codebook. By contrast, sPNP’s FI-and-relations framing treats distinctions as both phenotype and genotype: the environment expresses observable traits (robust records)

Minimally shear the Fisher geometry. This “FI-preserving distinctions” view explains why stable records, prediction, and classicality persist: what survives are exactly those distinctions least degraded by evolution and smoothing. The structures that conserve R and S are the ones the universe keeps.

wow good find. In sPNP, I use a lot of similar math as RDT (such as the Fisher-Rao). I like the distinctions as primitives results in intelligence in the relational context. RDT is great for AI/ML, but sPNP's view gives the ideas more geometry and relational physics. RDT has a strong entropy focus.

Fisher Information can be more properly understood as physics and then Amari should have been considered for the 2024 Nobel.

Equations (sPNP, AB limit):
v^a=G^{ab}∂_bS
∇·(ρv)=−∂_τρ
Q=−(ℏ²/2m)(Δ_GR)/R

AB conditions: ∇R≈0 ⇒ Q≈0; Ω=0 ⇒ v̇^a+Γ^a_bc v^b v^c=0 (geodesic).
TOF flux-independent; interference phase Δφ=(q/ℏ)∮A·dl.

The Jacobi isn’t a separate layer, it’s the convergence of the amplitude’s Fisher curvature and the phase’s Hamilton–Jacobi flow, producing geodesics. As a fundamental metric or as emergent, the same Jacobi dynamics result. This is reflexive, the geometry (R) and flow (S) both speak Jacobi dynamics.