If you hear SOI SOI SOI, you better RUN RUN RUN ’cause the ROFLCOPTER ASCII army is coming for you!
ROFL:ROFL:ROFL:ROFL
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| #!/bin/bash | |
| set -e # Exit on any error | |
| # Check if required arguments are provided | |
| if [ -z "$REGION" ] || [ -z "$SECURITY_GROUPS" ] || [ -z "$KEY_PAIR" ] || [ -z "$SUBNET" ]; then | |
| echo "Error: You must provide REGION, SECURITY_GROUPS, KEY_PAIR, and SUBNET as environment variables." | |
| echo "Example:" | |
| echo " export REGION=us-east-1" | |
| echo " export SECURITY_GROUPS=sg-12345678,sg-87654321" | |
| echo " export KEY_PAIR=my-key-pair" |
| import SwiftUI | |
| #if os(iOS) || os(tvOS) | |
| public typealias PlatformView = UIView | |
| public typealias PlatformViewRepresentable = UIViewRepresentable | |
| #elseif os(macOS) | |
| public typealias PlatformView = NSView | |
| public typealias PlatformViewRepresentable = NSViewRepresentable | |
| #endif |
| import cbor from 'cbor'; | |
| import jsrsasign from 'jsrsasign'; | |
| import { parseAuthenticatorData } from '@simplewebauthn/server/helpers'; | |
| import { ParsedAuthenticatorData } from '@simplewebauthn/server/dist/helpers'; | |
| // ... | |
| // const inputKeyId = get keyId from the app - this is a base64 of the sha256sum of the public key in uncompressed point format | |
| // const attestation = get attestation from the app | |
| const validateAttestation = async (inputKeyId: string, challenge: string, attestation: string): Promise<boolean> => { |
| // | |
| // Created by Chiharu Nameki @Ridwy on 2021/07/30. | |
| // | |
| import UIKit | |
| import AVFoundation | |
| /* | |
| Using MTAudioProcessingTap, you can touch raw audio samples playing with AVPlayer. | |
| This sample code shows how to use MTAudioProcessingTap in Swift 5. |
| # DO NOT PUT THE WIFI DONGLE IN THE DEVICE BEFORE MENTIONED EXPLICITLY BELOW | |
| # Brief note, after this the UI will not show the usb dongle, | |
| # the wifi does work and I get an IP address, so all works, | |
| # but I don't go into detail of making it show on the Raspbian UI. | |
| # (for this purpose I don't care about the UI) | |
| # For the use of this I connected my device to an ethernet connection and through the Router could see the IP which I can SSH into. | |
| ## STEP 1: Prepare machine and install packages needed |
| # Creating a model with TuriCreate | |
| import turicreate as tc | |
| data = tc.image_analysis.load_images('image/train', with_path=True) | |
| data['label'] = data['path'].apply(lambda path: 'healthy' if '/healthy' in path else 'fast food') | |
| model = tc.image_classifier.create(data, target='label') | |
| model.save("LunchImageClassifier.model") | |
| model.export_coreml('LunchImageClassifier.mlmodel') |
This doc explains the nuances of using the Datadog Ansible Integration with Ansible Tower.
In a directory of your choosing, run the following commands:
vagrant init ansible/towervagrant up --provider virtualboxvagrant sshThis lets you set the preload headers for your assets, so that the browser can start fetching them before it begins parsing HTML.