DEV Community: edwardjoseph

Improving Access to Public Domain Audiobooks with Libridex

edwardjoseph — Tue, 10 Oct 2023 05:00:42 +0000

The Genesis of Libridex

The Libridex app was born out of a love for reading and an understanding of the challenges that many people face when trying to engage with books in today's fast-paced world. As someone who cherished reading in my youth but now struggles to find the time, I've come to appreciate the convenience of audiobooks, which transform mundane tasks into opportunities for learning and entertainment. However, classic public domain books can often be lengthy and dense, making them less enticing for modern readers. Libridex draws inspiration from music streaming platforms like Pandora and Spotify, which use algorithms to recommend songs based on user preferences. Similarly, Libridex aims to make classic literature more accessible and enjoyable by offering curated samples of public domain audiobooks, guiding users to discover different sections of these timeless works. While there's still much work ahead, the goal of Libridex remains to bridge the gap between classic literature and contemporary audiences, enriching lives through the gift of timeless stories.

The Recommendation Problem

I quickly noticed a glaring gap in the audiobook ecosystem—lackluster recommendation systems. This frustration became the driving force behind Libridex. Most audiobook platforms rely on rudimentary recommendation algorithms that often miss the mark. Users find themselves with audiobooks that don't align with their tastes, resulting in a less-than-optimal reading experience. Libridex was conceived to tackle this issue head-on by harnessing the power of machine learning to deliver tailored recommendations.

Existing Projects

There are some great existing projects focused on improving access to public domain audiobooks, including:

LibriVox - A non-profit that relies on volunteers to record public domain texts. They offer a large free catalogue of audiobooks.
Lit2Go - Provides free public domain audiobooks, plus text and resources for teachers. Based out of the University of South Florida.
Loyal Books - Also offers free public domain audiobooks, focused on literature. Read by volunteers.

Building the Foundation

Every great project needs a strong foundation, and for Libridex, that meant assembling a diverse and extensive dataset. To achieve this, I embarked on an ambitious data-gathering mission, scraping data from LibriVox—a UK-based nonprofit that provides public domain audiobooks. This dataset, comprising approximately 15,000 unique URLs, would serve as the cornerstone of Libridex's recommendation engine, driven by cosine similarity. This intricate algorithm breathes life into the platform, analyzing audiobook descriptions to measure their similarity. It's a solution that conquers the challenge of repetitive terms and phrases in book descriptions, ensuring that the recommendations it serves are genuinely aligned with users' unique preferences.

The Development Process

Building Libridex required addressing both the frontend interface and the backend recommendation engine. For the frontend, I utilized React with Semantic UI for layout and styling. This provided components like the audio player, book info popups, user account management, and more.

I initially built my own user authorization system from scratch using JSON web tokens, hashing, encryption etc. However, this became complex quickly. I learned it's better to rely on a service like Auth0 that handles user auth out-of-the-box.

For the recommendation engine, I scraped over 15,000 public domain audiobook metadata records from LibriVox using Python and Selenium. To analyze these and generate recommendations, I employed a vector space modeling technique called TF-IDF (term frequency–inverse document frequency) to convert text into numeric vectors.

A challenge was that common words like "the", "and", etc skewed results. To account for this, I used cosine similarity to compare word vectors based on the angle between them rather than magnitude. This measures semantic similarity more meaningfully.

Lessons Learned

Here are some valuable lessons from this project:

Authentication: Don't reinvent the wheel when it comes to authentication. Instead, leverage a reliable provider like Auth0.
Static Analysis: Prioritize static analysis before dynamic deployment. It's essential to establish a strong foundation from the start.
Machine Learning Pipeline: Building a robust machine learning pipeline is crucial. It facilitates iterative development, version control, and continuous monitoring. Tools like AWS SageMaker are well-suited for this purpose.
Filtering Strategies: Begin with content-based filtering and then progressively refine recommendations based on user behavior over time. Consider transitioning to collaborative filtering for more personalized results.
AI Narration: Explore the potential of AI narration services like Anthropic and ElevenLabs to expand content accessibility rapidly and efficiently.

Potential Next Steps

If I were to continue building:

Establish a Vector Database
Implement a continuously-running ML pipeline with SageMaker
Containerize services (Flask API, React frontend, etc)
Incorporate AI narration and enhance recommendations
Develop collaborative filtering as user data comes in

The Future is AI Narration

While the human narration provided by the above projects is great, AI voice synthesis holds enormous potential for rapidly expanding access to audiobooks. Companies like Anthropic and ElevenLabs are already creating high-quality AI narration that could be applied to public domain books. Rather than relying solely on volunteers, AI narration could help bring even more classic literature to life in audio form. Libridex may want to explore incorporating these technologies as it develops.

Conclusion

By harnessing the capabilities of modern technology, Libridex aspires to redefine how users discover and immerse themselves in the world of audiobooks. It's not a trivial project, but Libridex holds the promise of making reading more accessible, engaging, and tailored to each individual in today's digital age. Overall, Libridex aims to build on the work of existing public domain audio projects to make classic literature more fun and accessible using an AI-powered recommendation engine. There is clearly both a need and an opportunity here to open up these timeless works to new audiences.

Streamlining Email Workflows: The Power of Language Models for Email Response

edwardjoseph — Mon, 09 Oct 2023 03:41:58 +0000

The Overwhelming Burden of Email Overload

Most people today spend a substantial portion of their day dealing with work email. A recent study found that the average office worker sends 121 emails per day and receives even more at 206. With this deluge of messages, critical information often gets buried or overlooked.

The constant pressure to promptly respond leads to hours lost in email composition. Even brief messages require focused thought to craft the perfect reply. Finding the right tone and language for sensitive topics proves especially challenging. This inefficient process drains time that could be better spent on high-value projects.

Language Models to the Rescue

Language models leverage massive datasets and machine learning algorithms to generate amazingly human-like text. Tools like GPT-3 have mastered the nuances of written communication. When applied to email, they can deliver significant advantages:

Lightning Fast Responses

Language models can instantly create well-formed email replies after analyzing the content and context of incoming messages. Whereas manually drafting thoughtful responses requires extensive effort and time, language models can produce them in seconds. This allows dramatically faster turnaround on all email communication.

Consistent and Professional Tone

Maintaining a consistent tone in all outgoing emails is challenging but critical for professionalism. Language models reliably adhere to the desired voice and style guidelines. This builds trust and authority by presenting a unified brand image across all electronic correspondence.

Personalized Communication

With the proper customization, language models can generate emails tailored to each recipient. Personalized email performs better across metrics like open rate, click rate, and conversion rate. Language models make incorporating personal touches easy.

Overcoming Writer's Block

Even experienced writers occasionally struggle to get started with a dreaded blank page. Language models can provide pre-written introductions, bulleted outlines, or sample paragraphs to kickstart the creative process and overcome writer's block.

Summarizing Long Conversations

When replying to a lengthy email chain, relevant context can be lost. Language models excel at summarizing main discussion points and history to keep everyone on the same page.

Multilingual Capabilities

For global businesses, language models can translate correspondence or suggest region-specific phrasing. This facilitates relationship building across geographies and cultures.

Suggesting Improvements

Language models can rephrase sentences to enhance clarity, provide vocabulary recommendations, and ensure proper grammar and spelling. This polish elevates the quality of outgoing communication.

Categorizing Inbound Emails

Language models can organize inbound emails by priority, required action, sender, topic, and more. This allows efficiently routing messages to the appropriate staff.

Automating Repetitive Replies

Language models shine at handling high volumes of repetitive inquiries, such as shipping status requests or password resets. This frees up worker bandwidth for complex issues.

Best Practices for Implementation

To maximize the power of language models for email, consider these tips:

Provide clear instructions on the desired tone, length, and purpose of the generated response.
Thoroughly review all automated replies before sending to catch any errors.
Start slowly by targeting high volume repetitive tasks to build trust.
Adjust confidence thresholds to balance automation with human oversight.
Integrate language models directly into existing tools like Gmail for seamless adoption.
Continuously train the models on company email data to improve accuracy over time.

The Future of AI Email Assistants

Leading technology providers like Microsoft and Google are racing to incorporate language model capabilities into their popular email platforms. The rise of AI promises to fundamentally transform email productivity.

With their ability to completely automate mundane tasks, apply complex contextual understanding across exchanges, and deliver consistently high-quality responses, language models offer immense potential to streamline workflows. Adoption is increasingly a competitive necessity.

By leveraging these AI tools, both individual users and organizations can reclaim hours lost in managing overflowing inboxes. The future of intelligent and automated email response is quickly emerging. Language models promise to revolutionize how we communicate.

How to use Google Apps Script with OpenAI for Email Autoreply

Google Apps Script is a powerful scripting language that can be used to automate tasks in a variety of Google products, including Gmail. OpenAI is a research lab that has developed a number of powerful AI models, including GPT-3, which can be used to generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way.

By combining Google Apps Script and OpenAI, you can create an autoreply for emails that is personalized and informative. To do this, you will need to create a Google Apps Script project and then write a script that calls the OpenAI API to generate an email response.

Here are the steps involved:

Create a new Google Apps Script project.
In the script editor, paste the following code:

   function generateAutoreply(email) {
     // Get the OpenAI API key from the script properties.
     var apiKey = ScriptApp.PropertiesService.getScriptProperties().getProperty('OPENAI_API_KEY');

     // Create a new OpenAI request.
     var request = UrlFetchApp.fetch('https://api.openai.com/v1/engines/davinci/completions', {
       method: 'post',
       headers: {
         'Authorization': 'Bearer ' + apiKey
       },
       payload: JSON.stringify({
         prompt: 'Write an email autoreply for the following email:\n\n' + email.getBody()
       })
     });

     // Parse the OpenAI response.
     var response = JSON.parse(request.getContentText());

     // Return the generated autoreply.
     return response.choices[0].text;
   }

   function onMessageReceived(e) {
     // Get the email message.
     var email = e.message;

     // Generate an autoreply for the email.
     var autoreply = generateAutoreply(email);

     // Send the autoreply.
     GmailApp.sendEmail(email.getFrom(), 'Autoreply', autoreply);
   }

Replace the OPENAI_API_KEY placeholder with your OpenAI API key.
Click the Save button.
Click the Run button to test the script.

Once the script is working, you can set it up to run automatically whenever you receive a new email. To do this, click the Triggers tab and then click the Add Trigger button. Select the On New Message trigger and then click the Save button.

That's it! You have now created an autoreply for emails using Google Apps Script and OpenAI.

Here are some additional tips for using this autoreply system effectively:

Personalize the autoreply: You can personalize the autoreply by including the recipient's name in the greeting and by using information from the email body in the body of the autoreply.
Tailor the autoreply to different types of emails: You can create different autoreplies for different types of emails, such as customer inquiries, marketing emails, and meeting invitations.
Use the autoreply as a starting point: The autoreply can be used as a starting point for a more personalized response. If you have time, you can review the autoreply and make any necessary edits before sending it.

Example:

Here is my repo with an example of a google script:
https://github.com/josephedward/GPT_autoreply

Keep in mind:

You will need to grant your Apps Script the necessary permissions in appscript.json.
You will be forced to utilize only Google Script libraries in your JavaScript code.
You will have to enable your trigger/schedule for operation, which will probably require some dialing in for your use case.

Harnessing the Combination of Tailscale and Osquery

edwardjoseph — Sun, 08 Oct 2023 04:00:16 +0000

TLDR: Tailscale's peer-to-peer VPN seamlessly connects your devices to a private network, while Osquery offers a high-performance relational database view of your OS. Together, they form a potent combination for sysadmins to monitor and maintain machines efficiently.

Tailscale: Revolutionizing VPNs

Tailscale is not your conventional VPN. Utilizing the zero-trust and zero-config principles, Tailscale provides global accessibility to your devices and applications without the overhead that traditional VPNs bring.

It achieves this via a unique peer-to-peer mesh network approach, termed "tailnet." Unlike traditional VPNs that funnel all traffic through a central server, Tailscale leverages the WireGuard protocol to establish direct, encrypted connections between devices on your private network.

One of its standout features is the automatic assignment of a distinct 100.x.y.z IP address to every device. This ensures stable connections globally, irrespective of network switches or firewalls. From connecting to an existing network to routing traffic through specific devices, Tailscale offers a plethora of features, all managed from a unified console.

The Inner Workings of Tailscale

Tailscale is fundamentally different from traditional VPNs. Instead of routing traffic through a centralized server, it establishes a mesh network using WireGuard. This decentralized approach offers superior performance, with WireGuard's efficient key exchange and low session costs.

The central server in Tailscale's architecture is solely responsible for authentication, key exchange, and coordination. By adopting this hybrid centralized-distributed model, Tailscale ensures that there's no central bottleneck, while still offering centralized control.

Osquery: Your OS as a Relational Database

Osquery is a unique tool that exposes an operating system as a high-performance relational database. This innovative design enables users to utilize SQL commands to gather and interpret data from their devices, making it an invaluable tool for security compliance and system monitoring. By allowing SQL queries to explore operating system data, osquery facilitates efficient and intuitive low-level OS analytics and monitoring.

Originating from Facebook in 2014, osquery has emerged as a versatile, open-source solution for organizations keen on optimizing data collection. The platform's capabilities are not restricted to any particular OS; it harmoniously integrates with Windows, macOS, and various Linux distributions, such as Ubuntu and Debian.

Osquery leverages SQLite for query parsing, optimization, and execution, which enables the project to concentrate on pinpointing the most pertinent data sources. Moreover, its adaptability and wide-ranging functionalities, from querying system information to threat detection, make it an essential tool for IT professionals and system administrators.

Osquery transforms your operating system into a high-performance relational database. With it, you can write SQL queries to explore intricate OS data, from running processes to browser plugins.

Virtual Tables lie at the heart of Osquery, dynamically generated during query execution. This OS instrumentation framework breaks down traditional barriers, offering a unified approach to collecting and normalizing data across different systems.

Osqueryi and Osqueryd: Dual Modes of Operation

Osquery provides two distinct modes:

Osqueryi: An interactive shell to draft queries and inspect your OS's current state. It operates independently, without any daemon communication.

Example: osqueryi --json "SELECT * FROM routes WHERE destination = '::1'"
Osqueryd: This daemon mode lets you schedule queries, log state changes, and monitor OS events, providing a comprehensive view of your system's activities.

By synergizing Tailscale and Osquery, sysadmins gain a holistic view of their network and devices. While Tailscale ensures a secure, fast, and direct connection between devices, Osquery provides a deep dive into the operating system's state.

🌐 Sources

Maximizing the Potential of GPT with Adversarial ML Techniques

edwardjoseph — Sun, 08 Oct 2023 02:11:11 +0000

Maximizing the Potential of GPT with Adversarial ML Techniques

As a machine learning enthusiast, I have always been fascinated by the capabilities of the Generative Pre-trained Transformer (GPT) language model. GPT has revolutionized the field of natural language processing by generating human-like text, completing sentences and paragraphs, and even writing articles. However, as powerful as GPT is, it is not immune to adversarial attacks. In this article, I will explore the concept of adversarial machine learning and how it can be used to maximize the potential of GPT.

Introduction to Adversarial Machine Learning

Adversarial machine learning (AML) is a subfield of machine learning that focuses on improving the robustness and security of machine learning models against adversarial attacks. Adversarial attacks are attempts to manipulate or deceive a machine learning model by introducing small perturbations to the input data. These perturbations are often imperceptible to humans but can cause the model to make incorrect predictions.

AML techniques can be used to defend against these attacks by introducing adversarial examples during the training process. Adversarial examples are input data that have been intentionally modified to cause the model to make incorrect predictions. By training the model on these examples, it becomes more robust to adversarial attacks.

Understanding Adversarial Deep Learning

Adversarial deep learning (ADL) is a subset of AML that focuses on improving the robustness of deep learning models against adversarial attacks. ADL techniques involve adding a regularization term to the loss function during training, which encourages the model to be more robust to adversarial examples.

One common ADL technique is adversarial training, which involves generating adversarial examples during the training process and including them in the training set. This forces the model to learn features that are robust to these examples and improves its overall robustness.

What is Adversarial Learning and its Role in Maximizing the Potential of GPT?

Adversarial learning is a general term that encompasses both AML and ADL. The goal of adversarial learning is to improve the robustness and security of machine learning models against adversarial attacks.

In the context of GPT, adversarial learning can be used to improve the model's ability to generate coherent and contextually relevant text. By training the model on adversarial examples, it becomes more robust to variations in the input text and can generate more diverse and creative text.

Adversarial learning can also be used to improve the privacy of GPT by making it more difficult for an attacker to infer sensitive information from the generated text. This can be achieved by introducing perturbations to the input text that preserve the overall meaning but make it more difficult to identify specific individuals or entities.

Adversarial Training Machine Learning – How it Helps?

Adversarial training is an ADL technique that involves generating adversarial examples during the training process and including them in the training set. This forces the model to learn features that are robust to these examples and improves its overall robustness.

In the context of GPT, adversarial training can be used to improve the model's ability to generate coherent and contextually relevant text. By training the model on adversarial examples, it becomes more robust to variations in the input text and can generate more diverse and creative text.

Adversarial training can also be used to improve the privacy of GPT by making it more difficult for an attacker to infer sensitive information from the generated text. This can be achieved by introducing perturbations to the input text that preserve the overall meaning but make it more difficult to identify specific individuals or entities.

OpenAI GPT-4 – What Does it Mean for Adversarial ML?

OpenAI recently announced the development of GPT-4, the next iteration of the GPT language model. GPT-4 is expected to be even more powerful than its predecessors and will likely push the boundaries of what is possible in natural language processing.

For adversarial ML, the development of GPT-4 means that there will be even more opportunities to explore the use of adversarial learning techniques in language processing. GPT-4 is expected to be more robust to adversarial attacks, which will make it more difficult for attackers to manipulate the generated text.

Advantages of Using Adversarial ML in GPT

The use of adversarial learning techniques in GPT has several advantages. First, it improves the robustness of the model against adversarial attacks, which makes it more reliable in real-world applications. Second, it can improve the diversity and creativity of the generated text by exposing the model to a wider range of input data. Finally, it can improve the privacy of the generated text by making it more difficult for attackers to infer sensitive information.

Challenges Faced in Implementing Adversarial ML in GPT

Despite the advantages of using adversarial learning techniques in GPT, there are also several challenges that must be overcome. One of the main challenges is the difficulty in generating high-quality adversarial examples. Adversarial examples must be carefully crafted to preserve the overall meaning of the input text while introducing subtle perturbations that cause the model to make incorrect predictions.

Another challenge is the computational overhead of training on adversarial examples. Adversarial training can be computationally expensive, which can make it difficult to scale to larger datasets or more complex models.

Conclusion

In conclusion, the use of adversarial machine learning techniques can help maximize the potential of GPT in natural language processing applications. Adversarial learning can improve the robustness, diversity, and privacy of the generated text, making it more reliable and secure in real-world applications. However, there are also challenges that must be overcome, such as the difficulty in generating high-quality adversarial examples and the computational overhead of training on these examples. As GPT continues to evolve and improve, the use of adversarial learning techniques will likely become even more important in ensuring its robustness and security.

Libraries

If you're like me and interested in exploring this exciting field, you're going to need access to some cool libraries that can help you get started. Luckily, I've done the research for you and compiled a list of some of the best libraries available across a range of languages and subfields.

Python

For Python, the most popular language for machine learning, there are several libraries that can help you get started with adversarial machine learning. One of the most popular is cleverhans, a library that provides a set of tools for implementing and testing attacks and defenses for machine learning models. Another great option is Adversarial Robustness Toolbox (ART), which provides a comprehensive set of tools for testing and developing adversarial machine learning models.

Java

For those of you who prefer to work in Java, there are also several great options available. The most popular is the adversarial-robustness-toolbox-java, which is a port of the ART library for Python. This library provides a comprehensive set of tools for testing and developing adversarial machine learning models in Java.

adversarial-robustness-toolbox-java

R

If R is your language of choice, you can check out the AdversarialML library, which provides a set of tools for testing and developing adversarial machine learning models in R. The library is actively maintained and includes a range of features such as data preprocessing, model building, and evaluation.

AdversarialML

TypeScript

For TypeScript, you can check out the adversarial-robustness-toolbox-js library, which is a port of the ART library for JavaScript/TypeScript. This library provides a comprehensive set of tools for testing and developing adversarial machine learning models in TypeScript. Another great option is the Foolbox library, which provides a set of tools for testing and developing adversarial machine learning models in TypeScript.

Golang

For Golang, one of the most popular libraries for adversarial machine learning is Adversarial-Go. This library provides a set of tools for implementing and testing adversarial machine learning models in Golang. Another great option is the CleverGo library, which is a port of the CleverHans library for Python and provides a set of tools for testing and developing adversarial machine learning models in Golang.

Rust

For Rust, one popular library for adversarial machine learning is the Adversarial Library. This library provides a set of tools for testing and developing adversarial machine learning models in Rust. Another great option is the ART-Rust library, which is a port of the ART library for Python and provides a comprehensive set of tools for testing and developing adversarial machine learning models in Rust.

Examples

FGSM attack visualization: This website offers an interactive visualization of the Fast Gradient Sign Method (FGSM) attack, which is a popular method for generating adversarial examples. Users can select an image and see how the attack modifies it to fool a classifier: FGSM Attack Visualization
ImageNet adversarial examples: The ImageNet dataset is commonly used for evaluating adversarial machine learning algorithms. This website provides a gallery of images from the dataset along with their corresponding adversarial examples generated using different attack methods: ImageNet Adversarial Examples
CIFAR-10 adversarial examples: CIFAR-10 is another popular dataset used for adversarial machine learning research. This website provides a gallery of CIFAR-10 images and their corresponding adversarial examples generated using different attack methods: CIFAR-10 Adversarial Examples
Adversarial patch attack: This website shows how an adversarial patch can be used to fool object detection models. Users can upload an image and select an object to be targeted by the patch, and the website generates the patch that can be printed and attached to the real-world object to fool the model: Adversarial Patch Attack
Adversarial distribution shift: This paper shows how an attacker can exploit distributional shifts in the data to craft adversarial examples that are difficult to detect. The paper includes several graphs and figures that illustrate the concepts discussed: Adversarial Distribution Shift

Virtualization for Practical Local Security

edwardjoseph — Mon, 14 Oct 2019 13:48:26 +0000

*https://en.wikipedia.org/wiki/Droste_effect*

Wikipedia:
In computing, virtualization refers to the act of creating a virtual (rather than actual) version of something, including virtual computer hardware platforms, storage devices, and computer network resources.

Writ large, virtualization is too wide of a subject to discuss within this article, so for our purposes I relegate my arguments to personal use cases. Type-1 virtual machine hosts such as Xen are referred to as native or bare-metal hypervisors because they do not live on top of the operating system and run at the hardware level. Type 1 Hypervisors are the purview of the tech behemoths; they are how the 'cloud' was created. This discussion is about Type-2 Hypervisors or hosted virtual machine monitors, which require an operating system to run on.

*Type 1 and Type 2 Hypervisors*

I think most developers (not just the paranoid security people) have a use case for a virtual machine, but many are too lazy to admit it. Virtualizing an operating system is the best way, that I am aware of, to create a portable workspace that allows you to save the exact state of you work. Tabs open, apps running, services started. This creates improvements in modularity, security, and research. More generally, virtualizing gives you the ability to treat an operating system as a canvas to experiment upon.

Efficiency
This is my personal go-to use case. I was first inspired by my email client to start using virtual machines. Random emails would pop up constantly, and even with tools and tricks to manage, they had a way of stealing cycles from what I was working on. There was no certainty they weren’t phishing attacks stealing information, or were literally stealing cycles (i.e. mining attacks). Of course, you can set a time to respond to emails, or disable your internet connection while you write responses. Simpler is to create a layer that requires deliberate action to view your mail. A virtualized OS adds another login to access, adds boot time, et cetera. Reply from your phone if you need to shoot your emails from the hip.

Security
Most notably you will see virtualization in the security world. It is highly common to see pentesters using a Kali Linux VM to provide a place to keep a toolkit that otherwise would be cumbersome to install and maintain on their day-to-day machines. It is also common to see Metasploitable VMs used for pen testing tutorials and examples as an intentionally vulnerable OS instance.

Privacy
You don’t know what you don’t know. You can never be entirely certain that you are working within a 100% secure system. Virtualization helps put layers in between your information and potential attacks. Nothing is foolproof, (I’m sure there are attacks designed for this specifically) but it is more unlikely that an attack cannot replicate itself through two layers or operating systems, particularly if they are of different architectures. Of course, each layer can have it’s own encryption, too. I think it is most typical to encrypt your host and leave the guest machines un-modulo'ed. Tails is the move if you think you’re sneaky.

Research
No one knows exactly how every single piece of software they install will act on their system. A virtual OS allows you to install carefree; if something breaks, your worst case scenario is deleting the machine. If you get caught in an infinite loop, you can just reboot. Important files in the host's RAM will not be lost. You will not lose the ability to google while rebooting. You won't have to worry about what services are, or are not, still running on your base machine. And if you think you got hit with malware, remove the machine! Or keep it for quarantine, weirdo.

*I have yet to find a good use case for dban, but don't push me*

Virtualization gives you options. Perhaps you've tested your app in multiple browsers, but have you tested it in multiple browsers across multiple operating systems? Shouldn't have an effect, but does it? I don't know. What about a native app? Don't you want to see how your Spring Boot app looks running in Windows/MacOS/Linux? With virtual machines, you can just go ahead and try it. Want to boot an offbeat operating system like FreeBSD or ReactOS? Here's your chance to do it without bricking your hardware. This article is not of the requisite scope to cover FreeBSD jail virtualization, or the entirely different subject of containerization (again, this discussion is about general-purpose user enablement).

Backup
It is also very simple to backup virtual machines. Just clone the VM. Again, this can be with browser tabs open, apps running, and services started. Most host virtualization applications automatically create snapshots. Virtual machines can be configured to be cross-platform, so that you can move your VM across different types of host operating systems.

Set Up
You should consider the go-to solution for virtualization to be Oracle's VirtualBox. Here's roughly the basic steps and parameters:

Download VirtualBox onto your Host machine.
Download the bootable .iso live disk image of the OS for your Guest machine.
Allocate memory - more than what VirtualBox recommends, but not the max - depending on:
- Number of simultaneous VMs you plan to run.
- What you plan on running on these machines.
Allocate number of processors - probably not worth it to use <2 cores as per modern hardware conventions.
Determine the size of the virtual disk storage.
- Dynamic vs. Fixed: choose fixed for performance, especially if you know you are going to need a certain amount of storage.
Attach the operating system as live install CD.
- Make sure you have a .iso file.
- Set it as your IDE controller.
- Settings -> Storage -> Attributes -> Optical Drive -> Live CD/DVD (pictured below)
Go through operating system install as you would on hardware.
- Install the Guest Additions disk image in the VirtualBox menu bar for added functionality.
- If you don't want to enable bidirectional drag-and-drop for security reasons, you can always create a shared folder between guest and host VM by navigating to the 'shared folders' tab.

*Click checkbox and CD icon to attach disk image*

Ubuntu
Ubuntu is my go-to distro for virtualization. Ubuntu Linux is open source, lightweight, general purpose, widely supported, secure by default, and has a diverse ecosystem. Current long term release is Bionic Beaver.

Windows
Windows is another option for a virtual OS. Windows is useful for gaming and many proprietary applications (although I wouldn't run a game in a VM without considerable compute power). I think these days, Microsoft will let you use Windows for free, without a licensing key. The catch is that Windows has a built in RAT that phones home and displays a warning popup, if you proceed without entering a key. It's nothing unmanageable though, and there are cheap enough options for purchasing the operating system through educational memberships.

MacOS
This is a tricky one. You can have a MacOS VM with paid solutions via Parallels and VMware hosts. However, I found a repo that offers a free QEMU image for MacOS. It was finicky but worked well enough, I was able to get a High Sierra VM running with a little time and effort. You will have to create the virtual disk image with terminal commands. I am not liable if Apple ninja lawyers rappel into your living room, when you go the free route. QEMU is an open source virtualization host that I've been toying with, it has a variety of fascinating operating modes (of which we also can't really discuss adequately here), and supports a wider range of processor architectures than VirtualBox. It is admittedly less configurable out-of-the-box for new users, and is generally intended for more advanced kernel developers.

Challenges
There are a few problems inherent to the use of hosted virtual machines. Processing power can be an issue, and using VMs on thinner machines can get buggy, often not being worth the effort. You will have to dial in your configuration some, based upon memory, processor cores, and other performance factors. Adding an additional core to your VM is usually enough to make a significant difference in functionality, but using too much of your machine's compute power can make the host crash on rare occasions. Another recurrent issue is resizing. It is very typical to get warnings about space running out on your machine's storage. You will need to get familiar with detaching your storage disk, resizing in megabytes with terminal commands, and reattaching when you start to run out of space:

C:\Users\User1\VirtualBox VMs\Windows 7\Windows 7.vdi” --resize 81920

You will also need to extend the logical partition inside of your guest vm if you want to incorporate the additional space into your initial virtual drive that the operating system is installed upon, instead of just using it as an auxiliary drive inside the guest.

(2023 Update)

How to Virtualize on an M1 ARM-based Mac

Apple's M1 ARM-based Macs are a new generation of computers that are incredibly powerful and efficient. However, they also present some new challenges for users who need to virtualize.

Two Ways to Virtualize on an M1 Mac

There are two main ways to virtualize on an M1 Mac:

Use a Type 1 Hypervisor: A Type 1 hypervisor runs directly on the hardware, without the need for an operating system. This provides the best performance, but it can be more complex to set up and manage.
Use a Type 2 Hypervisor: A Type 2 hypervisor runs on top of an operating system. This is easier to set up and manage, but it can have a negative impact on performance.

Using a Type 1 Hypervisor on an M1 Mac

There are currently two Type 1 hypervisors that support M1 Macs:

UTM: UTM is a free and open-source hypervisor that is available for macOS, Linux, and Windows.
Parallels Desktop Pro: Parallels Desktop Pro is a commercial hypervisor that is available for macOS.

To use a Type 1 hypervisor on an M1 Mac, you will need to install it on a USB drive. Once you have installed the hypervisor, you can create a new VM and install the operating system of your choice.

Using a Type 2 Hypervisor on an M1 Mac

There are currently two Type 2 hypervisors that support M1 Macs:

VirtualBox: VirtualBox is a free and open-source hypervisor that is available for macOS, Linux, and Windows.
VMware Fusion: VMware Fusion is a commercial hypervisor that is available for macOS.

To use a Type 2 hypervisor on an M1 Mac, you will need to install it on your Mac. Once you have installed the hypervisor, you can create a new VM and install the operating system of your choice.

Which Method Should You Use?

The best method for you will depend on your needs and requirements. If you need the best performance, then you should use a Type 1 hypervisor. However, if you are new to virtualization or you need a simpler solution, then you should use a Type 2 hypervisor.

Virtualizing on an M1 Mac can be a bit more challenging than virtualizing on an Intel Mac, but it is definitely possible. By following the tips above, you can ensure that your VMs run smoothly and efficiently.