phantomjs VS Previous Serverless Version 0.5.x

The points you make aren't unreasonable.

It is necessary to establish clear boundaries of what can and can be provided by the maintainers. If not done at an earlier stage of the project, the support burden becomes too much to bear at which point the maintainer transfers ownership, and the project suffers from catastrophic consequences such as the xz backdoor we're talking about here, or other cases where the project mostly stalls and serves as an ego-boosting platform for the new maintainer, as was the case with PhantomJS[6].

This can also happen in your life, where a "friend" sees that you possess a certain skill, and then gradually tries to push an inordinate amount of their personal work related to this field onto you.

Personally, I think it's best to use an approach with extremely clear communication as to what the maintainer can and cannot provide. This can be seen, for example, in yt-dlp[1], where the consumer is clearly informed upfront that not providing detailed information as requested will lead them to block said consumer; or sqlite where their position regarding contributed patches[2] and support[3] is similarly made clear.

Having a shouty BDFL like Torvalds can also help improve code quality[4] and questionable contributions[5], though it is better that the shouty BDFL makes statements that are professional and do not show as much aggression; so for example, "Mauro, shut the fuck up"[7] would become "Mauro, your response is completely unbecoming for a Linux kernel maintainer, and is not in line with the promise of not breaking userspace."

[1] https://github.com/yt-dlp/yt-dlp/issues/new?assignees=&label...

[2] https://www.sqlite.org/copyright.html

[3] https://www.sqlite.org/support.html

[4] https://www.theregister.com/2024/01/29/linux_6_8_rc2/

[5] https://cse.umn.edu/cs/linux-incident

[6] https://github.com/ariya/phantomjs/issues/14541

[7] https://lkml.org/lkml/2012/12/23/75

Last commit was in 2019, and it uses PhantomJS to query a page, which shutdown development in 2018

https://github.com/ariya/phantomjs/issues/15344

Automated Browser Detection. Cloudflare queries the browser for properties that only exist in automated web browser environments. For example, the existence of the window.document.__selenium_unwrapped or window.callPhantom property indicates the usage of Selenium and PhantomJS, respectively. For obvious reasons, you're getting blocked if this is detected.

Regarding youtube-dl, I remember someone mentioning they needed an external helper program called phantomjs to download from some sites. I really wouldn't recommend using phantomjs as it hasn't been updated since 2018 and I see it has known vulnerabilities too.

For this project we will need some extra binaries ( PhantomJS in particular) to take the screenshots. We’ll also use ImageMagick, but that is provided by AWS by default in the Lambda image, so we don’t package it separately.

ERROR: [iq.com] apvtge3eng: PhantomJS executable not found in PATH, download it from http://phantomjs.org

I want to use PhantomJS in Python. I googled this problem but couldn't find proper solutions.

In our project, we’ve been running system tests (then called rather "Feature tests") since around 2016. System tests use a real browser in the background and test all layers of a Rails application at once: from the database all the way up to the nuances of JavaScript loaded together with the web pages. Back then, we wrote our system tests using Capybara with Poltergeist, a driver that ran a headless Phantom JS browser. Since this browser stopped being actively developed, we migrated our test suite to the Selenium / Webdriver wrapper around Chrome browser around ~2018. Chrome was itself fine for tests automation but the Selenium API was quite limited and we had to rewrite several Poltergeist features using 3rd party gems and tools.

Github | Website

- With Lambda, you manage creating and building the container yourself, as well as updating the Lambda function code. There are tools out there such as sst or serverless.com which help streamline this.

If you'd like to use Lambda, usually you need to engineer FOR it, from day one, you don't (often) get to choose some other framework and shoehorn it into Lambda and Serverless. There's some great frameworks to help deploy code into Lambda easily and create REST endpoints for things, one such frameworks is serverless.com that helps easily deploy to it, but it lacks a framework for doing REST that also supports local emulation (as easily). For that, I recommend a framework by AWS called Chalice. This is an amazing REST framework that runs a proxy that works locally and deploys exactly the same on Lambda, it is Python however.

After knocking out a README with a set of goals and a list of TODOs to check off as I made progress, I spent about 10 hours over a weekend trying to get something to work. I used serverless for making Lambda easier, Github Actions for the deploy pipeline and store my credentials; and sadly I rolled my own access_token refresh logic because I couldn't find a helper that just did that for me! wtf!?

The backend is built with serverless.com (lambda, dynamodb, sqs, appsync). The good thing is that all the backend is stored in a file and you can deploy multiple stacks on the same account using seed.run . You don't really need EC2/Fargate when you have lambdas and you know that most of the time will be idle time. The same with cache I wouldn't think of it right now until you see the workload you are facing. Dynamodb once you understand it and have a proper design it's the fastest thing you can have. On my appsync calls I'm using Dynamodb as a cache because it's cheaper...

Apache Kafka allows for asynchronous communication in a distributed ecosystem. It allows producers to publish messages on topics that are then ingested by consumers interested in those topics. As a concept, pub-sub models have been around for ages. However, the beauty of Kafka is in the how — using partitions and consumer groups, Kafka can scale the rate of consumption of messages with minimal dev and economic overhead. In this tutorial, I’ll take you through how to provision a managed Kafka cluster using the AWS Managed Stream for Kafka (MSK) service. We’ll use the serverless framework to create and maintain the infrastructure for MSK and the supporting VPCs, subnets, etc.

I run my personal project on AWS. I has been running for 4+ years now and I never had a local environment. I took the serverless route. That is appsync, lambda, dynamodb, sqs to build the stack. I'm using serverless.com to have all the resources defined in a yaml files which will deploy multiple stacks. I'm using seed.run to manage that part because it's much more simple than to do it manually.

The solution is deployed using serverless.com