Android Leftovers

• Android OEMs Shamed by Devs Fed up with App-Breaking Features

• Navigation Editor Highlights Android Studio 3.3 Update

• Android Studio 3.5 Canary adds “Apply Changes,” an Instant Run replacement

• Huawei pushes Android Pie-based EMUI 9 update to P20 Pro, Nova 3

• Huawei MediaPad M5 Pro Android Pie tablet surfaces on Geekbench

• Samsung Galaxy A40 spotted on Geekbench with Android Pie onboard, Exynos 7885 and 4 GB RAM in tow

• Android Pie-based EMUI 9.0 to be rolled out to 150 million users by 2019

• What are you hoping to see in Android Q?

• Stable version of Android x86 8.1 Oreo now available

• Lenovo reportedly close to releasing new Motorola RAZR with folding display for $1,500

• Hands-on: Elegant bucket list app 'Soon' launches on Android

• The best add-on lenses for iPhone and Android phones in 2019

Programming: Flask, Agile, Rust and Python

• How to build an API for a machine learning model in 5 minutes using Flask

  As a data scientist consultant, I want to make impact with my machine learning models. However, this is easier said than done. When starting a new project, it starts with playing around with the data in a Jupyter notebook. Once you’ve got a full understanding of what data you’re dealing with and have aligned with the client on what steps to take, one of the outcomes can be to create a predictive model. You get excited and go back to your notebook to make the best model possible. The model and the results are presented and everyone is happy. The client wants to run the model in their infrastructure to test if they can really create the expected impact. Also, when people can use the model, you get the input necessary to improve it step by step. But how can we quickly do this, given that the client has some complicated infrastructure that you might not be familiar with?

• What is Small Scale Scrum?

  Agile is fast becoming a mainstream way industries act, behave, and work as they look to improve efficiency, minimize costs, and empower staff. Most software developers naturally think, act, and work this way, and alignment towards agile software methodologies has gathered pace in recent years. VersionOne’s 2018 State of Agile report shows that scrum and its variants remain the most popular implementation of agile. This is in part due to changes made to the Scrum Guide’s wording in recent years that make it more amenable to non-software industries.

• This Week in Rust 269

• Async IO in Python: A Complete Walkthrough

  Async IO is a concurrent programming design that has received dedicated support in Python, evolving rapidly from Python 3.4 through 3.7, and probably beyond. You may be thinking with dread, “Concurrency, parallelism, threading, multiprocessing. That’s a lot to grasp already. Where does async IO fit in?” This tutorial is built to help you answer that question, giving you a firmer grasp of Python’s approach to async IO.

Security: Updates, Reproducible Builds and More

• Security updates for Wednesday

• Reproducible Builds: Weekly report #194

  Here’s what happened in the Reproducible Builds effort between Sunday January 6 and Saturday January 12 2019...

• ES File Explorer Has A Hidden Web Server; Data Of 500 Million Users At Risk

• The Evil-Twin Framework: A tool for testing WiFi security

  The increasing number of devices that connect over-the-air to the internet over-the-air and the wide availability of WiFi access points provide many opportunities for attackers to exploit users. By tricking users to connect to rogue access points, hackers gain full control over the users' network connection, which allows them to sniff and alter traffic, redirect users to malicious sites, and launch other attacks over the network.. To protect users and teach them to avoid risky online behaviors, security auditors and researchers must evaluate users' security practices and understand the reasons they connect to WiFi access points without being confident they are safe. There are a significant number of tools that can conduct WiFi audits, but no single tool can test the many different attack scenarios and none of the tools integrate well with one another. The Evil-Twin Framework (ETF) aims to fix these problems in the WiFi auditing process by enabling auditors to examine multiple scenarios and integrate multiple tools. This article describes the framework and its functionalities, then provides some examples to show how it can be used.

• KDE Plasma5 – Jan ’19 release for Slackware

  Here is your monthly refresh for the best Desktop Environment you will find for Linux. I just uploaded “KDE-5_19.01” to the ‘ktown‘ repository. As always, these packages are meant to be installed on a Slackware-current which has had its KDE4 removed first. These packages will not work on Slackware 14.2. It looks like Slackware is not going to be blessed with Plasma5 any time soon, so I will no longer put an artificial limitation on the dependencies I think are required for a solid Plasma5 desktop experience. If Pat ever decides that Plasma5 has a place in the Slackware distro, he will have to make a judgement call on what KDE functionality can stay and what needs to go.

MongoDB "open-source" Server Side Public License rejected

MongoDB is open-source document NoSQL database with a problem. While very popular, cloud companies, such as Amazon Web Services (AWS), IBM Cloud, Scalegrid, and ObjectRocket has profited from it by offering it as a service while MongoDB Inc. hasn't been able to monetize it to the same degree. MongoDB's answer? Relicense the program under its new Server Side Public License (SSPL). Open-source powerhouse Red Hat's reaction? Drop MongoDB from Red Hat Enterprise Linux (RHEL) 8. Red Hat's Technical and Community Outreach Program Manager Tom Callaway explained, in a note stating MongoDB is being removed from Fedora Linux, that "It is the belief of Fedora that the SSPL is intentionally crafted to be aggressively discriminatory towards a specific class of users." Debian Linux had already dropped MongoDB from its distribution.