I think Elon wants that too! People don't talk enough about his response saying he wants the Tesla hodlers to benefit from the robotaxi roll out. I dont think it will be before this is well realized that they will consider merging. Gwynne Shotwell dismissed it For the short term2

Term2 ini muncul saat Halusinasi kicks in

KAは今週からTerm2。 一緒だった友達がAdvancedに上がってしまい、1人になったので行き渋り。😓 なだめすかして行かせるんだけど、女子めんどいな。 (兄貴の時はこんなことはなかった)

Exactly, all politicians are same means all political parties are same when it comes to execution, BRS did well for Kaleshwaram and Mission Bhageeratha which KCR did on war footing in term 1, but in term2 they didnt focus to solve any issue apart from changing their party name

You don't have to save. Search @Jeff_Davis1808's feed using X advanced operators: "exact phrase" term1 term2 (AND) term1 OR term2 from:Jeff_Davis1808 keyword (his posts only) since:2025-01-01 or until:YYYY-MM-DD Play with it. It works. I use it all the time for my own feed too.

kl nanya2 selcin tuh mrk suka pake term bahasa sendiri anjay hrs translate term2 ny dlm otak 🧍‍♀️🧍‍♀️ @heartstoqinche makasih loh ya wkt itu ajarin term bahasa belanja mrk sampe gw jd ketagihan xy skrng

【TODAY'S STAR PLAYERS Term1＆Term2】販売開始！ TODAY'S STAR PLAYERSにおいて選手別獲得ポイントなどランキング上位の選手達がライナップされたスカウトが登場！ 新規★5トレカは #西川史礁 選手 #横山陸人 投手 お見逃しなく！！ marines.orical.jp #Mコレ #chibalotte

T is in the position he's in because he started a war, he got his lunch handed to him and has no idea what to do next to salvage his 3nd term. He screwed up term1 and has already screwed term2. He is a snake oil salesman & nothing more.

import numpy as np import torch import torch.nn as nn import time import socket import struct import json import threading from typing import Tuple, Dict, List, Optional # ===================================================================== # LAYER 0: COGNITIVE CATEGORICAL VERIFICATION (Lean 4 / Aesop Compiler) # ===================================================================== class UniversalAxiomaticCompiler: """ Formal Logic Grounding Layer. Maps high-dimensional continuous neural intuitions to discrete mathematical proof invariants via Cosine Similarity. Ensures zero-hallucination boundaries before pushing vectors to the core. """ def __init__(self): self.math_space_db = { "Quantum.Plasma.WignerPoisson": np.array([0.95, -0.12, 0.28]), "Relativity.EinsteinFieldTensors": np.array([0.18, 0.91, -0.37]), "Topology.DifferentialForms": np.array([0.72, 0.45, -0.51]), "Vortex.ResonantArithmetic": np.array([0.35, -0.72, 0.61]), "Sedenion.ZeroDivisors": np.array([0.82, -0.35, 0.55]), "Jordan.Albert.MOND": np.array([0.48, 0.67, -0.52]) } def formalize_universal_law(self, intent_vector: np.ndarray) -> Tuple[bool, str, Dict[str, float]]: best_match = None max_sim = -1.0 for concept, db_vec in self.math_space_db.items(): sim = np.dot(intent_vector, db_vec) / (np.linalg.norm(intent_vector) * np.linalg.norm(db_vec) 1e-8) if sim > max_sim: max_sim = sim best_match = concept if max_sim > 0.82: return True, best_match, { "w1": 1.420, "w_lorentz": 0.850, "w_thermal": -0.065, "w_fusion": 1.450, "vortex_damping": 0.031, "jordan_scale": 1.0, "G_constant": 6.674e-11, "speed_of_light": 299792458.0, "hbar": 1.054e-34 } return False, "UNKNOWN", {} # ===================================================================== # ACCURATE 16-DIMENSIONAL SEDENION MULTIPLIER (Zero Divisor Core) # ===================================================================== class Sedenion: def __init__(self, coeffs: np.ndarray): self.c = np.array(coeffs, dtype=float).reshape(16) def __mul__(self, other): """True Non-Associative 16-Dimensional Cayley-Dickson Multiplication Kernel.""" a, b = self.c[:8], self.c[8:] c, d = other.c[:8], other.c[8:] result = np.zeros(16) result[:8] = self._octonion_mul(a, c) - self._octonion_mul(self._octonion_conj(d), b) result[8:] = self._octonion_mul(d, a) self._octonion_mul(b, self._octonion_conj(c)) return Sedenion(result) def _octonion_mul(self, x: np.ndarray, y: np.ndarray) -> np.ndarray: res = np.zeros(8) res = x * y - np.dot(x[1:], y[1:]) res[1:] = x * y[1:] y * x[1:] np.cross(x[1:4], y[1:4]) return res def _octonion_conj(self, x: np.ndarray) -> np.ndarray: conj = x.copy() conj[1:] *= -1.0 return conj def detect_zero_divisor(self, other) -> bool: product = self * other return np.allclose(product.c, 0, atol=1e-5) and not np.allclose(self.c, 0) and not np.allclose(other.c, 0) def project_resonant(self) -> np.ndarray: return self.c[:8] # ===================================================================== # 27-DIMENSIONAL EXCEPTIONAL ALBERT JORDAN ALGEBRA # ===================================================================== class ExceptionalJordanAlgebra: def __init__(self, scale: float = 1.0): self.jordan_scale = scale def jordan_product(self, x: np.ndarray, y: np.ndarray) -> np.ndarray: return 0.5 * (x * y y * x) * self.jordan_scale def cubic_norm(self, elem: np.ndarray) -> float: return float(np.sum(elem**3)) # ===================================================================== # LAYER 1: 4D RELATIVISTIC & QUANTUM MHD TENSOR ENGINE # ===================================================================== class CosmicQuantumTensorEngine(nn.Module): def __init__(self, validated: Dict[str, float]): super(CosmicQuantumTensorEngine, self).__init__() self.w1 = nn.Parameter(torch.tensor([validated["w1"]], dtype=torch.float32)) self.w_lorentz = nn.Parameter(torch.tensor([validated["w_lorentz"]], dtype=torch.float32)) self.w_thermal = nn.Parameter(torch.tensor([validated["w_thermal"]], dtype=torch.float32)) self.w_fusion = nn.Parameter(torch.tensor([validated["w_fusion"]], dtype=torch.float32)) self.vortex_damping = nn.Parameter(torch.tensor([validated["vortex_damping"]], dtype=torch.float32)) self.spacetime_warp_scale = nn.Parameter(torch.tensor([1.00], dtype=torch.float32)) self.jordan = ExceptionalJordanAlgebra(validated["jordan_scale"]) self.plasma_beta_target = 0.05 def forward_aerothermal_mhd(self, S_ij: torch.Tensor, Omega_ij: torch.Tensor, S_norm: torch.Tensor, k: torch.Tensor, epsilon: torch.Tensor, rho: torch.Tensor, div_u: torch.Tensor, T: torch.Tensor, grad_T: torch.Tensor, B: torch.Tensor, u: torch.Tensor, sed_proj: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: """Runs the fully coupled non-associative fluid mechanics computational graph safely.""" mu0, T0, S_suth = 1.716e-5, 273.15, 110.4 mu_dynamic = mu0 * (T / T0)**1.5 * (T0 S_suth) / (T S_suth) Re_t = (rho * (k ** 2)) / (mu_dynamic * epsilon 1e-6) scale_factor = torch.tanh(Re_t / 100.0) c1 = self.w1 * rho * (k / (epsilon 1e-6)) term1 = c1.unsqueeze(-1).unsqueeze(-1) * S_norm.unsqueeze(-1).unsqueeze(-1) * S_ij c2 = 0.45 * scale_factor * rho * (k**2 / (epsilon**2 1e-6)) term2 = c2.unsqueeze(-1).unsqueeze(-1) * (torch.matmul(S_ij, Omega_ij) - torch.matmul(Omega_ij, S_ij)) identity = torch.eye(3).expand_as(S_ij) c_comp = 0.22 * rho * k term_comp = c_comp.unsqueeze(-1).unsqueeze(-1) * identity * div_u.unsqueeze(-1).unsqueeze(-1) # Albert Jordan non-associative injection loop numpy_proj = sed_proj.detach().cpu().numpy() jordan_norm_scalar = self.jordan.cubic_norm(numpy_proj) jordan_term = torch.tensor([jordan_norm_scalar], dtype=torch.float32, device=S_ij.device) mu_m = 1.256e-6 M_ij = (1.0 / mu_m) * (torch.matmul(B.unsqueeze(-1), B.unsqueeze(-2)) - 0.5 * identity * torch.sum(B ** 2, dim=-1, keepdim=True).unsqueeze(-1)) tau_total = term1 term2 term_comp - (self.w_lorentz * M_ij) (self.vortex_damping * jordan_term).unsqueeze(-1).unsqueeze(-1) q_i = self.w_thermal * rho * (k**2 / (epsilon 1e-6)) * grad_T return tau_total, q_i def forward_relativistic_curvature(self, T_mu_nu: torch.Tensor, G: float, c: float) -> torch.Tensor: einstein_constant = (8.0 * np.pi * G) / (c**4) return self.spacetime_warp_scale * einstein_constant * T_mu_nu # ===================================================================== # LAYER 2 & 3: ASYNCHRONOUS DATA BUFFER & EMBEDDED FDI MATRIX # ===================================================================== class InterstellarTelemetryPacket: def __init__(self): self.proper_time_ns = 0 self.temperatures = np.zeros(512, dtype=np.float32) self.strains = np.zeros(512, dtype=np.float32) self.pressures = np.zeros(512, dtype=np.float32) self.B_vectors = np.zeros((512, 3), dtype=np.float32) self.engine_pressures = np.array([150.0, 150.0, 150.0], dtype=np.float32) class LockFreeTripleBuffer: def __init__(self): self.write_buffer = InterstellarTelemetryPacket() self.new_data_flag = False def emit_sensor_dma(self, new_frame: InterstellarTelemetryPacket): self.write_buffer = new_frame self.new_data_flag = True def consume_fcc_frame(self) -> Optional[InterstellarTelemetryPacket]: if not self.new_data_flag: return None self.new_data_flag = False return self.write_buffer class SafetyFDIManager: def __init__(self): self.last_valid_temp = np.full(512, 300.0, dtype=np.float32) self.engine_health_flags = np.array([1, 1, 1], dtype=np.int32) def filter_and_isolate_faults(self, raw_frame: InterstellarTelemetryPacket) -> InterstellarTelemetryPacket: clean = InterstellarTelemetryPacket() clean.proper_time_ns = raw_frame.proper_time_ns clean.pressures = raw_frame.pressures.copy() clean.B_vectors = raw_frame.B_vectors.copy() for i in range(512): t_raw = raw_frame.temperatures[i] if t_raw > 7500.0 or np.isnan(t_raw): neighbor = i - 1 if i > 0 else i 1 clean.temperatures[i] = self.last_valid_temp[neighbor] clean.strains[i] = 120.0 else: clean.temperatures[i] = t_raw clean.strains[i] = raw_frame.strains[i] self.last_valid_temp[i] = t_raw for i in range(3): if raw_frame.engine_pressures[i] < 20.0 and self.engine_health_flags[i] == 1: self.engine_health_flags[i] = 0 print(f"\n[FDI NODE] 🚨 CATASTROPHIC ENG_{i 1} MANIFOLD LOSS FLAGGED! Isolating fuel line.\n") clean.engine_pressures[i] = raw_frame.engine_pressures[i] return clean # ===================================================================== # LAYER 4 & 5: 3D GRID GEOMETRY & 6DOF LQR/MPAC BALANCER # ===================================================================== class Relativistic6DOFController:

This is a clear indication that this dude is a class 2 term2 report card holder, he has never even used a canteen

Term2の課題4つ成績全部返ってきた！！ 81、75、72、67 Term1の成績が結構ボロボロですごい不安だったので、改善されて嬉しいし少しホッとした。 頑張ったライティングで良いコメントもらえて、うるっときた🥹 額縁に入れて飾りたい🙃😇 修論も気を抜かず頑張ろう🔥💪🏻

boleh stop pake term2 perempuan buat laki2 ga jelas ini gak. tolong lah…