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Would you use a backend where you just define schema, access policy, and functions?

Basically something like making smart contracts on EVM, but instead they run on a hyperscaler, and have regular backend fundamentals.

Here's a mock frenchie made me, was thinking something like this:

schema User {
  email: string @private(owner)
  name: string @public

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#!/usr/bin/env python3
"""
Simple CLI for PersonaPlex - talk and listen via terminal.
"""

import argparse
import os
import sys
import time
import threading

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// ==UserScript==
// @name         Auto-Authorize Claude OAuth (claude.ai)
// @match        https://claude.ai/oauth/authorize*
// @run-at       document-end
// ==/UserScript==
(() => {
  const clickAuthorize = () => {
    const btn = [...document.querySelectorAll("button")]
      .find(b => /authorize/i.test(b.textContent || ""));
    if (btn) btn.click();

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#!/usr/bin/env bash
set -e

usage() {
  echo "usage: $0 <command> -a <writable_path> [-a <writable_path>...]"
  exit 1
}

if [ "$#" -lt 3 ]; then
  usage

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Summary

txt2img sdxl 1024x1024

65.417351÷65.623950=0.996851774

img2img sdxl 1024x1024 (input 1024x1024 image)

54.623950÷55.738662=0.980001099

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import numpy as np
import torch
import cv2
from PIL import Image
from transformers import AutoImageProcessor, AutoModelForDepthEstimation
from transformers import DPTForDepthEstimation, DPTImageProcessor


def depth_estimation(model, feature_extractor, image):
    inputs = feature_extractor(images=image, return_tensors="pt").to("cuda")

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from math import inf
import torch
from torch import tensor, device
import torch.fx as fx
import torch._dynamo
from torch._dynamo.testing import rand_strided
from torch._dynamo.debug_utils import run_fwd_maybe_bwd

import torch._dynamo.config

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#!/bin/bash

set -e

# Check if zsh is installed
if ! command -v zsh &> /dev/null; then
    echo "zsh is not installed. Please install zsh first."
    exit 1
fi

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import numpy as np
import torch
import cv2
from PIL import Image
from transformers import DPTForDepthEstimation, DPTImageProcessor

model = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas", low_cpu_mem_usage=True).to("cuda")
feature_extractor = DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas")

# Start capturing video from the first camera device

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How to get this

Edit init.rc as following:

on early-init
    # Prepare early debugfs
    mount debugfs none /sys/kernel/debug
    chmod 0755 /sys/kernel/debug/tracing

    # Enable i2c tracer