That Podcast, with hosts and PHP community members Beau Simensen and Dave Marshall, have posted their latest episode: Episode #46: The One with the Last Jedi

Beau and Dave discuss Star Wars: The Last Jedi. There are spoilers here. And no tech talk. Consider yourselves warned!

Other topics mentioned include:

• the Disney/Star Wars canon
• "force meld"
• C1-10P

This one's basically just about The Last Jedi and Star Wars without the usual web development chatter. If you'd like to listen to this latest show, you can use the in-page audio player or download the mp3 directly. Also be sure to subscribe to their feed and follow them on Twitter for updates when new shows are released.

Sergey Zhuk has a new ReactPHP-related post to his site today showing you how to use the library to scrape content from the web quickly, making use of the asynchronous abilities the package provides.

Almost every PHP developer has ever parsed some data from the Web. Often we need some data, which is available only on some website and we want to pull this data and save it somewhere. It looks like we open a browser, walk through the links and copy data that we need. But the same thing can be automated via script. In this tutorial, I will show you the way how you can increase the speed of you parser making requests asynchronously.

In his example he creates a scraper that goes to a movie's page on the IMDB website and extracts the title, description, release date and the list of genres it falls into. Instead of creating a single-threaded process that can only fetch a single page at a time, he uses ReactPHP to speed things up and provide it a list of pages to fetch all at the same time. He starts by walking through the setup of the package and the creation of the browser instance. He then includes the code to make the request and crawl the contents of the result for the data. The post ends with the full code for the client and a way to add in a timeout in case the request fails.

The SitePoint PHP blog has posted the next part in their series about using Predictive.IO and Lumen to create a simple movie recommendation application (part one is here). In this second part of the series they build on the environment created in the previous article and start developing the actual application.

In the intro, we covered the basics of PredictionIO and installed its dependencies. In this part, we’re going to build the movie recommendation app.

The tutorial starts with a brief configuration section to ensure you have your API keys configured correctly. Then it gets into the code:

• Pulling the data from the Movie DB API
• Creating the endpoint to perform the endpoint
• Picking random movies to show the user and recording their reactions (like/dislike)
• Creating the endpoint to recommend movies

Finally they share the configuration to set up the application deployment and train it with some example content you provide through some basic interactions. Finally they help you set up a cron job to train and redeploy the application every five minutes with the latest interaction information.

On the SitePoint PHP blog they've posted a tutorial showing you how to use the Prediction.IO server to create a movie recommendation application. Prediction.io is "an open source Machine Learning Server built on top of state-of-the-art open source stack for developers and data scientists create predictive engines for any machine learning task".

In this tutorial, I’m going to walk you through PredictionIO, an open-source machine learning server, which allows you to create applications that could do the following: recommend items (e.g. movies, products, food), predict user behavior, identify item similarity and rank items.

You can pretty much build any machine learning application with ease using PredictionIO. You don’t have to deal with numbers and algorithms and you can just concentrate on building the app itself.

The tutorial, the first part of a series, refreshes some older instructions for getting the Prediction.IO system up and running. He walks you through the creation of an AWS instance for the server a few different ways (Vagrant, Docker, etc). He then talks about the use of the Movie API from MovieDB and the two parts of the application that will be implemented on top of it: a learning phase and a recommendation phase. They show how to use Prediction.io to create the recommendation engine and make the new application on top of it. He helps you install some dependencies to use in the PHP side of the application and briefly explains what they're for.

This wraps up part one of the series. In the second part he starts putting this all to use and creates the PHP functionality to lay on top of the machine learning engine and handle learning and recommendations for users.

The SitePoint PHP blog continues their series about creating a movie prediction engine with Prediction.io in this second part focusing on implementation. In the first part of the series they set up the server and configuration to make the jump into the code. This second part gets more into the application side and features working code linking the prediction engine with the TheMovieDB API.

He jumps right into the code, showing how to:

• Fetch the data from the TMDB (via Flight and Guzzle)
• Populate the data back into the Prediction.io database
• Picking a random movie from the list (and outputting it to a page)
• Get movies the engine predicts as recommendations

The recommendations are based on ratings on other movies in the database with most of that logic happening behind the scenes instead of in the PHP script. The results are then output to the page along with the other movie data.

On the SitePoint PHP blog today Wern Ancheta has posted the first part of a series about creating a recommendation engine with the help of PHP and a system called Prediction IO.

In this tutorial, I'm going to walk you through Prediction IO, an open-source machine learning server. It allows you to create applications that could do the following: recommend items (e.g. movies, products, food), predict user behavior, identify item similarity and rank items. You can pretty much build any machine learning application with ease using Prediction IO. You don't have to deal with numbers and algorithms and you can just concentrate on building the app itself.

He walks you through the download and install of the Prediction IO software, how to start up the server and how to access its web interface. He shows you how to create an "engine" that will be used to make the recommendations and some of the settings allowing you to tailor it to your needs. The script will hook into The Movie DB API for content. He starts in on the PHP packages that will be needed to make the API connection and recommendations, but the actual code will come in a later article.

On the SitePoint PHP blog today Wern Ancheta has posted the first part of a series about creating a recommendation engine with the help of PHP and a system called Prediction IO.

In this tutorial, I'm going to walk you through Prediction IO, an open-source machine learning server. It allows you to create applications that could do the following: recommend items (e.g. movies, products, food), predict user behavior, identify item similarity and rank items. You can pretty much build any machine learning application with ease using Prediction IO. You don't have to deal with numbers and algorithms and you can just concentrate on building the app itself.

He walks you through the download and install of the Prediction IO software, how to start up the server and how to access its web interface. He shows you how to create an "engine" that will be used to make the recommendations and some of the settings allowing you to tailor it to your needs. The script will hook into The Movie DB API for content. He starts in on the PHP packages that will be needed to make the API connection and recommendations, but the actual code will come in a later article.

Lorenzo Alberton has posted a new item to his blog with a code snippet showing the combination of FFmpeg and features in PHP's SPL to make a GIF preview.

About one year ago, I had to create animated GIFs as a preview of a video for a media portal. I recently stumbled upon the code I wrote and thought it was probably worth sharing. It makes a rather unconventional use of the SPL Iterators, proving how flexible they are.

His method combines Imagick, FFmpeg and a a Thumbnail_Extractor class that extends the SPL Iterator. The script runs through the frames of the movie file and allows you to define multiple places to pull thumbnails from. Complete code and output examples are included.

Davey Shafik has already gotten some of his experiences down in blog post form from the php|tek conference today, actually starting with the day previous in his Day 0 and Day 1 posts.

In the Day 0 post he just mentions arriving at the conference and getting things ready, but Day 1 sees him giving his "Future Deployment of PHP Applications" talk and his upcoming "Migration to PHP 5.1".

He also mentions videoing the presentation from today ("Deployment" talk) and is hoping to get a Flash video of the presentation up for all to see post-conference (a great idea for non-attendees).

Davey Shafik has already gotten some of his experiences down in blog post form from the php|tek conference today, actually starting with the day previous in his Day 0 and Day 1 posts.

In the Day 0 post he just mentions arriving at the conference and getting things ready, but Day 1 sees him giving his "Future Deployment of PHP Applications" talk and his upcoming "Migration to PHP 5.1".

He also mentions videoing the presentation from today ("Deployment" talk) and is hoping to get a Flash video of the presentation up for all to see post-conference (a great idea for non-attendees).