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8 Best Ways to Reduce Android App Size

With the increase in mobile storage spaces that have gone up to 256 GB, app size is also growing. App size is sure to grow as developers are adding new features, trying to meet customer needs, and also trying to support their apps on various screen sizes. Around 74% of the world uses Android, and […]

The post 8 Best Ways to Reduce Android App Size appeared first on Mantra Labs.

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With the increase in mobile storage spaces that have gone up to 256 GB, app size is also growing. App size is sure to grow as developers are adding new features, trying to meet customer needs, and also trying to support their apps on various screen sizes. Around 74% of the world uses Android, and 70% of users consider app size before installing them. Moreover, as humans are getting accustomed to instant gratification, they ponder on ways to download apps as they take up storage spaces. Despite the cloud support for photos, videos, and files, android users face issues, such as mobile hanging due to app size. As customer expectations are increasing, android app developers are considering other ways to reduce app size while still incorporating significant additional features and keeping in mind the customer experience.

Below are the 8 best ways to reduce android app size:

1. Use Android App Bundle to Reduce App Size

When generating the release version of your app, you can choose between APK and Android App Bundle.  The second option will make Google play to generate the APK with only those features a specific user need. 

Use Android App Bundle

App Bundle Vs APK

Android App Bundle

  • It is a publishing format that includes compiled code and resources of your app, and delays APK generation and signing to Google Play.
  • With Android App Bundles, the compressed download size restriction is 150 MB. The app bundle cannot be used with APK expansion files.
Android App Bundle
Important: In the second half of 2021, new apps will be required to publish with the Android App Bundle on Google Play. New apps larger than 150 MB must use either Play Feature Delivery or Play Asset Delivery.

How to build android app bundles?

To build app bundles, follow these steps:

  1. Download Android Studio 3.2 or higher—it’s the easiest way to add feature modules and build app bundles.
  2. Add support for Play Feature Delivery by including a base module, organizing code and resources for configuration APKs, and, optionally, adding feature modules.
  3. Build an Android App Bundle using Android Studio. You can also deploy your app to a connected device from an app bundle by modifying your run/debug configuration and selecting the option to deploy APK from app bundle. Keep in mind, using this option results in longer build times when compared to building and deploying only an APK.
  4. If you’re not using the IDE, you can instead build an app bundle from the command line.
  5. Test your Android App Bundle by using it to generate APKs that you deploy to a device.
  6. Enroll into app Play App Signing. Otherwise, you can’t upload your app bundle to the Play Console.
  7. Publish your app bundle to Google Play.

Please note: Android Package Kit – As per developer console, by the mid of 2021, developers won’t be able to upload apk on play store)

  • Android operating system uses APK which is the package file format for distribution and installation of mobile apps, games and middleware. APK is similar to other software packages such as APPX in Microsoft Windows or a Debian package in Debian -based operating systems.
  • Google Play requires that the compressed APK downloaded by the users should not exceed 100 MB.
  • The expansion files for your app are hosted by Google Play which serves them to the device at no cost to you. The expansion files are saved to the device’s shared storage location (the SD card or USB-mountable partition).

2. Use Proguard

Proguard is probably one of the most useful tools to reduce your APK size. It reduces the source code files to a minimum and can reduce the APK file size upto 90%.

  • Use it in all variants whenever using “Proguard”
  • Helps to avoid conflict at the of generate apk or bundle if will use in all the variants.
  • We cannot let ProGuard rename or remove any fields on these data classes, as they have to match the serialized format. It’s a safe bet to add a @Keep annotation on the whole class or a wildcard rule on all your models.

3. Use Android Size Analyzer Plugin

This Android Studio plugin will provide you recommendations to reduce the size of your application.

With the APK Analyzer, you can accomplish the following:

  • View the absolute and relative size of files in the APK, such as the DEX and Android resource files.
  • Understand the composition of DEX files.
  • Quickly view the final versions of files in the APK, such as the AndroidManifest.xml file.
  • Perform a side-by-side comparison of two APKs.

There are three ways to access the APK Analyzer when a project is open:

  • Drag an APK into the Editor window of Android Studio.
  • Switch to the Project perspective in the Project window and then double-click the APK in the default build/output/apks/ directory.
  • Select Build > Analyze APK in the menu bar and then select your APK.

More details at: Jetbrains

4. Optimize Your App’s Resources

Whether used or not, every resource takes up memory. It is therefore necessary to have only those resources that you need, and to use those in a memory efficient way. In other words, you should consider the resolution of the image before finalizing on it.

5. Optimize Libraries

As large libraries consume huge amounts of space, it is advisable to remove parts of it in case you do not need them and if it is permitted by the license of the library. Proguard can aid you in this process but it is not always able to remove large internal dependencies.

6. Use Vector Graphics Wherever Possible

They are sharp and do not consume much space as they rely on mathematical calculations and not on pixels that need to be saved. However, they cannot be used for photography.

7. Compress Your Images

By using tools such as pngcrush, you can reduce the file size of PNG images. It is advisable to do this images as they still look the same. 

8. Only Support Specific Densities

If only a small portion of users use a specific density, it might be better to let Android scale your other densities for them as it would reduce your APK size.


As mobile storage space is growing, people are installing a large number of apps to meet a wide range of needs. But as app size is increasing, people are continuing to struggle with storage issues. With provisions such as Proguard, one can compress the APK file size and optimize libraries easily. Compressing images and using vector graphs are also useful in reducing app size.

About the author: Anand Singh is Tech Lead at Mantra Labs. He is integral to the company’s Android-based projects and enterprise application development.

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Source: https://www.mantralabsglobal.com/blog/reduce-android-app-size/

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How does it know?! Some beginner chatbot tech for newbies.

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Wouter S. Sligter

Most people will know by now what a chatbot or conversational AI is. But how does one design and build an intelligent chatbot? Let’s investigate some essential concepts in bot design: intents, context, flows and pages.

I like using Google’s Dialogflow platform for my intelligent assistants. Dialogflow has a very accurate NLP engine at a cost structure that is extremely competitive. In Dialogflow there are roughly two ways to build the bot tech. One is through intents and context, the other is by means of flows and pages. Both of these design approaches have their own version of Dialogflow: “ES” and “CX”.

Dialogflow ES is the older version of the Dialogflow platform which works with intents, context and entities. Slot filling and fulfillment also help manage the conversation flow. Here are Google’s docs on these concepts: https://cloud.google.com/dialogflow/es/docs/concepts

Context is what distinguishes ES from CX. It’s a way to understand where the conversation is headed. Here’s a diagram that may help understand how context works. Each phrase that you type triggers an intent in Dialogflow. Each response by the bot happens after your message has triggered the most likely intent. It’s Dialogflow’s NLP engine that decides which intent best matches your message.

Wouter Sligter, 2020

What’s funny is that even though you typed ‘yes’ in exactly the same way twice, the bot gave you different answers. There are two intents that have been programmed to respond to ‘yes’, but only one of them is selected. This is how we control the flow of a conversation by using context in Dialogflow ES.

Unfortunately the way we program context into a bot on Dialogflow ES is not supported by any visual tools like the diagram above. Instead we need to type this context in each intent without seeing the connection to other intents. This makes the creation of complex bots quite tedious and that’s why we map out the design of our bots in other tools before we start building in ES.

The newer Dialogflow CX allows for a more advanced way of managing the conversation. By adding flows and pages as additional control tools we can now visualize and control conversations easily within the CX platform.

source: https://cloud.google.com/dialogflow/cx/docs/basics

This entire diagram is a ‘flow’ and the blue blocks are ‘pages’. This visualization shows how we create bots in Dialogflow CX. It’s immediately clear how the different pages are related and how the user will move between parts of the conversation. Visuals like this are completely absent in Dialogflow ES.

It then makes sense to use different flows for different conversation paths. A possible distinction in flows might be “ordering” (as seen here), “FAQs” and “promotions”. Structuring bots through flows and pages is a great way to handle complex bots and the visual UI in CX makes it even better.

At the time of writing (October 2020) Dialogflow CX only supports English NLP and its pricing model is surprisingly steep compared to ES. But bots are becoming critical tech for an increasing number of companies and the cost reductions and quality of conversations are enormous. Building and managing bots is in many cases an ongoing task rather than a single, rounded-off project. For these reasons it makes total sense to invest in a tool that can handle increasing complexity in an easy-to-use UI such as Dialogflow CX.

This article aims to give insight into the tech behind bot creation and Dialogflow is used merely as an example. To understand how I can help you build or manage your conversational assistant on the platform of your choice, please contact me on LinkedIn.

Source: https://chatbotslife.com/how-does-it-know-some-beginner-chatbot-tech-for-newbies-fa75ff59651f?source=rss—-a49517e4c30b—4

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Who is chatbot Eliza?

Between 1964 and 1966 Eliza was born, one of the very first conversational agents. Discover the whole story.

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Frédéric Pierron

Between 1964 and 1966 Eliza was born, one of the very first conversational agents. Its creator, Joseph Weizenbaum was a researcher at the famous Artificial Intelligence Laboratory of the MIT (Massachusetts Institute of Technology). His goal was to enable a conversation between a computer and a human user. More precisely, the program simulates a conversation with a Rogérian psychoanalyst, whose method consists in reformulating the patient’s words to let him explore his thoughts himself.

Joseph Weizenbaum (Professor emeritus of computer science at MIT). Location: Balcony of his apartment in Berlin, Germany. By Ulrich Hansen, Germany (Journalist) / Wikipedia.

The program was rather rudimentary at the time. It consists in recognizing key words or expressions and displaying in return questions constructed from these key words. When the program does not have an answer available, it displays a “I understand” that is quite effective, albeit laconic.

Weizenbaum explains that his primary intention was to show the superficiality of communication between a human and a machine. He was very surprised when he realized that many users were getting caught up in the game, completely forgetting that the program was without real intelligence and devoid of any feelings and emotions. He even said that his secretary would discreetly consult Eliza to solve his personal problems, forcing the researcher to unplug the program.

Conversing with a computer thinking it is a human being is one of the criteria of Turing’s famous test. Artificial intelligence is said to exist when a human cannot discern whether or not the interlocutor is human. Eliza, in this sense, passes the test brilliantly according to its users.
Eliza thus opened the way (or the voice!) to what has been called chatbots, an abbreviation of chatterbot, itself an abbreviation of chatter robot, literally “talking robot”.

Source: https://chatbotslife.com/who-is-chatbot-eliza-bfeef79df804?source=rss—-a49517e4c30b—4

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FermiNet: Quantum Physics and Chemistry from First Principles

Weve developed a new neural network architecture, the Fermionic Neural Network or FermiNet, which is well-suited to modeling the quantum state of large collections of electrons, the fundamental building blocks of chemical bonds.

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Unfortunately, 0.5% error still isn’t enough to be useful to the working chemist. The energy in molecular bonds is just a tiny fraction of the total energy of a system, and correctly predicting whether a molecule is stable can often depend on just 0.001% of the total energy of a system, or about 0.2% of the remaining “correlation” energy. For instance, while the total energy of the electrons in a butadiene molecule is almost 100,000 kilocalories per mole, the difference in energy between different possible shapes of the molecule is just 1 kilocalorie per mole. That means that if you want to correctly predict butadiene’s natural shape, then the same level of precision is needed as measuring the width of a football field down to the millimeter.

With the advent of digital computing after World War II, scientists developed a whole menagerie of computational methods that went beyond this mean field description of electrons. While these methods come in a bewildering alphabet soup of abbreviations, they all generally fall somewhere on an axis that trades off accuracy with efficiency. At one extreme, there are methods that are essentially exact, but scale worse than exponentially with the number of electrons, making them impractical for all but the smallest molecules. At the other extreme are methods that scale linearly, but are not very accurate. These computational methods have had an enormous impact on the practice of chemistry – the 1998 Nobel Prize in chemistry was awarded to the originators of many of these algorithms.

Fermionic Neural Networks

Despite the breadth of existing computational quantum mechanical tools, we felt a new method was needed to address the problem of efficient representation. There’s a reason that the largest quantum chemical calculations only run into the tens of thousands of electrons for even the most approximate methods, while classical chemical calculation techniques like molecular dynamics can handle millions of atoms. The state of a classical system can be described easily – we just have to track the position and momentum of each particle. Representing the state of a quantum system is far more challenging. A probability has to be assigned to every possible configuration of electron positions. This is encoded in the wavefunction, which assigns a positive or negative number to every configuration of electrons, and the wavefunction squared gives the probability of finding the system in that configuration. The space of all possible configurations is enormous – if you tried to represent it as a grid with 100 points along each dimension, then the number of possible electron configurations for the silicon atom would be larger than the number of atoms in the universe!

This is exactly where we thought deep neural networks could help. In the last several years, there have been huge advances in representing complex, high-dimensional probability distributions with neural networks. We now know how to train these networks efficiently and scalably. We surmised that, given these networks have already proven their mettle at fitting high-dimensional functions in artificial intelligence problems, maybe they could be used to represent quantum wavefunctions as well. We were not the first people to think of this – researchers such as Giuseppe Carleo and Matthias Troyer and others have shown how modern deep learning could be used for solving idealised quantum problems. We wanted to use deep neural networks to tackle more realistic problems in chemistry and condensed matter physics, and that meant including electrons in our calculations.

There is just one wrinkle when dealing with electrons. Electrons must obey the Pauli exclusion principle, which means that they can’t be in the same space at the same time. This is because electrons are a type of particle known as fermions, which include the building blocks of most matter – protons, neutrons, quarks, neutrinos, etc. Their wavefunction must be antisymmetric – if you swap the position of two electrons, the wavefunction gets multiplied by -1. That means that if two electrons are on top of each other, the wavefunction (and the probability of that configuration) will be zero.

This meant we had to develop a new type of neural network that was antisymmetric with respect to its inputs, which we have dubbed the Fermionic Neural Network, or FermiNet. In most quantum chemistry methods, antisymmetry is introduced using a function called the determinant. The determinant of a matrix has the property that if you swap two rows, the output gets multiplied by -1, just like a wavefunction for fermions. So you can take a bunch of single-electron functions, evaluate them for every electron in your system, and pack all of the results into one matrix. The determinant of that matrix is then a properly antisymmetric wavefunction. The major limitation of this approach is that the resulting function – known as a Slater determinant – is not very general. Wavefunctions of real systems are usually far more complicated. The typical way to improve on this is to take a large linear combination of Slater determinants – sometimes millions or more – and add some simple corrections based on pairs of electrons. Even then, this may not be enough to accurately compute energies.

Source: https://deepmind.com/blog/article/FermiNet

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