Some notes/resources for bypassing anti-bot/scraping features on Cloudflare, Akamai, etc.
Due to unknown reasons, apps you had previously purchased can no longer be downloaded from the App Store or iTunes. However, it turns out these applications can still be accessed. Originally, this tutorial was written to use iMazing's app downloading feature. This is because iMazing downloads apps using a different endpoint from what iTunes uses. However, I have also decided to write the tutorial to use ipatool-py, because it also uses the different endpoint when entering the Apple ID and Password into the command line.
This app must have been purchased on your Apple ID beforehand. You cannot download any app ever made. Apple only lets you download apps you had bought or downloaded in the past.
I reccomend using the ipatool-py method, because it is easier. However, if you are unable to use it, I have left the original iMazing method for you to follow.
- App ID (Number such as 1053533457) of app to download
#!/bin/bash | |
gdb -p "$1" -batch -ex 'set {short}$rip = 0x050f' -ex 'set $rax=231' -ex 'set $rdi=0' -ex 'cont' |
Install, build and debug a react native app in WSL2 (Windows Subsystem for Linux) and Ubuntu.
- Install all required software:
docker
,nvidia-docker
,gitlab-ci-multi-runner
- Execute: curl -s http://localhost:3476/docker/cli
- Use that data to fill devices/volumes/volume_driver fields in /etc/gitlab-runner/config.toml
runonce = RunOnceBranchOperator( | |
dag=dag, | |
task_id='runonce_example', | |
run_once_task_id='downstream_task_id_to_run_once', | |
skip_task_id='dummy_task_id_used_to_skip_other_task', | |
) | |
runonce.set_downstream(downstream_to_run_once) | |
runonce.set_downstream(dummy_task_used_to_skip_other_task) | |
from airflow.operators.python_operator import PythonOperator | |
from airflow.utils.db import provide_session | |
class RunOnceBranchOperator(PythonOperator, SkipMixin): | |
def __init__( | |
self, | |
run_once_task_id=None, | |
skip_task_id=None, | |
*args, **kwargs): | |
kwargs['python_callable'] = lambda x: x |
In the first post I explained how we generate a torch.Tensor
object that you can use in your Python interpreter. Next, I will explore the build system for PyTorch. The PyTorch codebase has a variety of components:
- The core Torch libraries: TH, THC, THNN, THCUNN
- Vendor libraries: CuDNN, NCCL
- Python Extension libraries
- Additional third-party libraries: NumPy, MKL, LAPACK
import grpc | |
import helloworld_pb2 | |
import helloworld_pb2_grpc | |
# you need to use secure port, | |
# otherwise call credentials won't be transmitted | |
def run(): | |
with open('server.crt', 'rb') as f: | |
trusted_certs = f.read() |