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5 Best Kotlin Libraries/Packages for Building Native Apps

5 minutes, 7 seconds read

About Kotlin

Kotlin is a modern statically typed programming language that boosts productivity and increases developer happiness. It runs on the Java Virtual Machine and is completely interoperable with the Java programming language. It is an officially supported language for developing Android apps, along with Java. Developers are finding Kotlin libraries more reliable as compared to other open-source platforms as they improve productivity and make the overall code base more stable.

After Google officially launched Kotlin, several developers have started taking interest in this new language as it allows them to save hours of development time.

Reasons why Kotlin is gaining popularity over Java:

  • It is structured and presents a familiar development tooling that is meant to boost developers’ productivity.
  • It is a good compiler.
  • Kotlin enables seamless integration with the existing infrastructure as it is compatible with all Java frameworks and libraries. It is designed in a manner to integrate easily with Marven and Gradle build systems.
  • It provides an enhanced run-time performance.

Kotlin Libraries:

Below are some major Kotlin libraries that will help developers to make the right choice, as per their needs:

Anko

It is considered one of the popular Android libraries as it is written in Kotlin but maintained by JetBrains. Anko makes the code clean and easy to understand. It is lightweight and also helps to avoid Boilerplate code. The name Anko is derived from the first two letters of (An)droid and (Ko)tlin. The library has four diverse modules that include:

Layouts: Helps to write dynamic Android layouts and is fast and has type-safe approach;
SQLite: A Kotlin-specific query DSL and parser for Android SQLite with lot simpler way;
Commons: A lightweight library is full of helpers for intents, dialogs, logging, resources, and more;
Coroutines: Utilities based on the new kotlinx.coroutines library

Dynamic layout using Anko Layouts

Dynamic kotlin layout using Anko Layouts
Dynamic kotlin layout using Anko Layouts

It is best to make use of this library while trying to develop Kotlin projects.
For more details about Anko, refer to Github.

Kotlin Coroutines

Some of the APIs begin long-running operations like network IO, file IO, CPU or GPU-intensive work and need the caller to block until they finish. But Kotlin Coroutines helps to avoid blocking thread and replaces it with the more convenient operation known as suspension of coroutines which helps in writing cleaner and more concise app code. Kotlin Coroutines allows users to develop asynchronous programs in a very simple manner, which are primarily based on the concept of Continuation-passing style programming.
Coroutines is a recommended solution for asynchronous programming that includes:

Lightweight: Due to support for suspension,which doesn’t block the thread where the coroutine is running, it is possible to run many coroutines on a single thread. Suspending saves memory over blocking and also supports many concurrent operations.

Fewer memory leaks: to run operations within a scope, make use of structured concurrency.

Built-in cancellation support: by using the running coroutine hierarchy, Cancellation is automatically propagated.

Jetpack integration: the extensions included by several Jetpack libraries provide full coroutine support. Some libraries also provide their own coroutine scope that can be used for structured concurrency.

To begin with Coroutine, refer to the example below that is making use of the launch {} function:

Kotlin Coroutine using the launch{} function
Here we start a coroutine that waits for 1 second and prints Hello.

For more details about Kotlin Coroutines, refer to Github

KAndroid

KAndroid is a Kotlin for Android library that focuses on efficiency and delivers useful extensions to eliminate boilerplate code in Android SDK. This library can be of a huge help in various functions like Handler implementation, ViewPager Implementation, SearchView query text change, TextWatcher, SeekBar extension, using system services, Using Intents, Logging, loading animation from XML, etc. Making use of this library is helpful as much code is not needed to be written for common function.  

Refer to the example below:

KAndroid- Kotlin for Android library

RxKotlin

This is the most lightweight library as compared to other Android libraries because it adds convenient extension functions to RxJava, which allows it to utilize RxJava and Kotlin exceptionally. As it makes use of RxJava with Kotlin, it gathers the conveniences in one centralized library and standardized conventions. However, Kotlin has language features like extension functions, which streamlines usage of RxJava even more.

Refer to the example below:

RxKotlin


Klaxon

Klaxon is another lightweight android Kotlin library to parse JSON in Kotlin.

For example,

Klaxon
Klaxon code

The values extracted from a valid JSON file can be of the following type:

  • Int
  • Long
  • BigInteger
  • String
  • Double
  • Boolean
  • JsonObject
  • JsonArray

JsonObject and JsonArray behave differently. While JsonObject behaves like a Map, JsonArray behaves like a List. Once a file is analyzed, it can be cast to the type that one wants. 

For more details about klaxon, refer to Github.

Conclusion

To build a scalable Android application, above are the top recommended Kotlin libraries that Android developers can utilize for the development process. There is no need to develop everything from scratch as these libraries will help developers to save hours of time.

For more information, check out ktlint and KBinding.

About the author:

Burhanuddin Zummarwala is a Senior Software Engineer at Mantra Labs. Burhanuddin likes coding, travelling, trekking, sports (especially cricket and TT) and loves exploring new technologies.

Further reading:

  1. 8 Best Ways to Reduce Android App Size
  2. WWDC20: 6 Latest Additions in SwiftUI for iOS Developers
  3. 5 Key Takeaways for iOS Developers from WWDC20
  4. 5 Reasons Why Flutter Framework is Better than React Native

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Machines That Make Up Facts? Stopping AI Hallucinations with Reliable Systems

There was a time when people truly believed that humans only used 10% of their brains, so much so that it fueled Hollywood Movies and self-help personas promising untapped genius. The truth? Neuroscientists have long debunked this myth, proving that nearly all parts of our brain are active, even when we’re at rest. Now, imagine AI doing the same, providing information that is untrue, except unlike us, it doesn’t have a moment of self-doubt. That’s the bizarre and sometimes dangerous world of AI hallucinations.

AI hallucinations aren’t just funny errors; they’re a real and growing issue in AI-generated misinformation. So why do they happen, and how do we build reliable AI systems that don’t confidently mislead us? Let’s dive in.

Why Do AI Hallucinations Happen?

AI hallucinations happen when models generate errors due to incomplete, biased, or conflicting data. Other reasons include:

  • Human oversight: AI mirrors human biases and errors in training data, leading to AI’s false information
  • Lack of reasoning: Unlike humans, AI doesn’t “think” critically—it generates predictions based on patterns.

But beyond these, what if AI is too creative for its own good?

‘Creativity Gone Rogue’: When AI’s Imagination Runs Wild

AI doesn’t dream, but sometimes it gets ‘too creative’—spinning plausible-sounding stories that are basically AI-generated fake data with zero factual basis. Take the case of Meta’s Galactica, an AI model designed to generate scientific papers. It confidently fabricated entire studies with fake references, leading Meta to shut it down in three days.

This raises the question: Should AI be designed to be ‘less creative’ when AI trustworthiness matters?

The Overconfidence Problem

Ever heard the phrase, “Be confident, but not overconfident”? AI definitely hasn’t.

AI hallucinations happen because AI lacks self-doubt. When it doesn’t know something, it doesn’t hesitate—it just generates the most statistically probable answer. In one bizarre case, ChatGPT falsely accused a law professor of sexual harassment and even cited fake legal documents as proof.

Take the now-infamous case of Google’s Bard, which confidently claimed that the James Webb Space Telescope took the first-ever image of an exoplanet, a factually incorrect statement that went viral before Google had to step in and correct it.

There are more such multiple instances where AI hallucinations have led to Human hallucinations. Here are a few instances we faced.

When we tried the prompt of “Padmavaat according to the description of Malik Muhammad Jayasi-the writer ”

When we tried the prompt of “monkey to man evolution”

Now, if this is making you question your AI’s ability to get things right, then you should probably start looking have a checklist to check if your AI is reliable.

Before diving into solutions. Question your AI. If it can do these, maybe these will solve a bit of issues:

  • Can AI recognize its own mistakes?
  • What would “self-awareness” look like in AI without consciousness?
  • Are there techniques to make AI second-guess itself?
  • Can AI “consult an expert” before answering?

That might be just a checklist, but here are the strategies that make AI more reliable:

Strategies for Building Reliable AI

1. Neurosymbolic AI

It is a hybrid approach combining symbolic reasoning (logical rules) with deep learning to improve factual accuracy. IBM is pioneering this approach to build trustworthy AI systems that reason more like humans. For example, RAAPID’s solutions utilize this approach to transform clinical data into compliant, profitable risk adjustment, improving contextual understanding and reducing misdiagnoses.

2. Human-in-the-Loop Verification

Instead of random checks, AI can be trained to request human validation in critical areas. Companies like OpenAI and Google DeepMind are implementing real-time feedback loops where AI flags uncertain responses for review. A notable AI hallucination prevention use case is in medical AI, where human radiologists verify AI-detected anomalies in scans, improving diagnostic accuracy.

3. Truth Scoring Mechanism

IBM’s FactSheets AI assigns credibility scores to AI-generated content, ensuring more fact-based responses. This approach is already being used in financial risk assessment models, where AI outputs are ranked by reliability before human analysts review them.

4. AI ‘Memory’ for Context Awareness

Retrieval-Augmented Generation (RAG) allows AI to access verified sources before responding. This method is already being used by platforms like Bing AI, which cites sources instead of generating standalone answers. In legal tech, RAG-based models ensure AI-generated contracts reference actual legal precedents, reducing AI accuracy problems.

5. Red Teaming & Adversarial Testing

Companies like OpenAI and Google regularly use “red teaming”—pitting AI against expert testers who try to break its logic and expose weaknesses. This helps fine-tune AI models before public release. A practical AI reliability example is cybersecurity AI, where red teams simulate hacking attempts to uncover vulnerabilities before systems go live 

The Future: AI That Knows When to Say, “I Don’t Know”

One of the most important steps toward reliable AI is training models to recognize uncertainty. Instead of making up answers, AI should be able to respond with “I’m unsure” or direct users to validated sources. Google DeepMind’s Socratic AI model is experimenting with ways to embed self-doubt into AI.

Conclusion:

AI hallucinations aren’t just quirky mistakes—they’re a major roadblock in creating trustworthy AI systems. By blending techniques like neurosymbolic AI, human-in-the-loop verification, and retrieval-augmented generation, we can push AI toward greater accuracy and reliability.

But here’s the big question: Should AI always strive to be 100% factual, or does some level of ‘creative hallucination’ have its place? After all, some of the best innovations come from thinking outside the box—even if that box is built from AI-generated data and machine learning algorithms.

At Mantra Labs, we specialize in data-driven AI solutions designed to minimize hallucinations and maximize trust. Whether you’re developing AI-powered products or enhancing decision-making with machine learning, our expertise ensures your models provide accurate information, making life easier for humans

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