All images in this article are AI-generated, free of cost, by the author on NightCafe Studio.

Rust is the King — AI Can Help You Learn It

Rust is acclaimed as the king of modern systems programming.

It is directly challenging the long-standing dominance of C and C++.

It achieves this by providing:

• The raw performance of C++

• Guaranteeing memory safety everywhere

• Providing compile-time concurrency safety

• Avoiding the majority of hackers’ loopholes, especially memory issues

• Providing the best package and configuration manager today in cargo.

Stack Overflow’s developer survey has crowned Rust the “most loved” programming language for eight consecutive years.

This unprecedented popularity stems from the fact that developers who overcome its initial learning curve, find the features and guarantees of Rust immensely rewarding and productive.

Killer Features of Rust for Systems Programming

Memory Safety without a Garbage Collector:

• Rust’s compiler statically guarantees memory safety

• This eliminates entire categories of common bugs like:

• null pointer dereferences

• buffer overflows

• dangling pointers.

// This code, which would cause a dangling pointer in C++, won't even compile in Rust.
fn get_dangling_reference() -> &String {
    let s = String::from("hello");
    &s // Error: `s` does not live long enough
}

Zero-Cost Abstractions:

Rust allows you to write high-level, expressive code using abstractions like:

• Iterators

• Closures

• async/await

• map/reduce patterns

• first-class functions

Without incurring a runtime performance penalty.

// This high-level iterator...
let numbers = vec![1, 2, 3, 4, 5];
let sum_of_squares: i32 = numbers.iter().map(|&x| x * x).sum();// ...compiles down to machine code as efficient as a manual C-style loop.

Fearless Concurrency:

• The same ownership and type system that guarantees memory safety:

• Prevents data races at compile time!

• This makes it significantly easier and safer to write concurrent, multi-threaded programs.

use std::thread;

fn main() {
    let mut data = vec![1, 2, 3];

    // This attempt to use the same mutable data from two threads is a compile-time error.
    // Rust forces you to use safe concurrency primitives like Arc and Mutex.
    // thread::spawn(|| { data.push(4); }); // Error: closure may outlive current function
    // thread::spawn(|| { data.push(5); }); // Error
}

Modern Tooling with Cargo:

• Cargo is Rust’s integrated build system and package manager

• It is praised for its simplicity and power

Cargo handles:

• project creation

• dependency management

• building

• testing

• And much more with simple commands.

# Create a new project
cargo new my_awesome_project

# Add a dependency by adding one line to Cargo.toml
# [dependencies]
# serde = "1.0"

# Build and run
cargo run

The Infamous Steep Learning Curve

• The primary hurdle for beginners is Rust’s compiler.

• The compiler is famously strict because:

• It must statically prove the correctness of your program’s memory management

• It must prevent any type of errors in concurrency at compile-time.

• This strictness means code that might run (and later crash) in other languages will not even compile in Rust until it meets the safety rules.

The Borrow Checker and Ownership Rules

• Rust’s memory management is governed by a set of rules that the compiler checks at compile time

• This system is called ownership.

• Rule 1: Each value in Rust has a single owner.

fn main() {
    // s is the owner of the String data "hello" allocated on the heap.
    let s = String::from("hello");
}

• Rule 2: There can only be one owner at a time.

• When a value is assigned to another variable, ownership is moved.rr

fn main() {
    let s1 = String::from("hello");
    let s2 = s1; // Ownership of the String data is moved from s1 to s2.
    // The line below will cause a compile-time error because s1 is no longer a valid owner.
    // println!("s1 is: {}", s1); // Error: value borrowed here after move
}

• Rule 3: When the owner goes out of scope, the value is dropped.

• Rust automatically calls a special drop function to free the memory.

fn main() {
    {
        let s = String::from("I live only within these curly braces");
    } // `s` goes out of scope here, and the memory is automatically freed.
}

To access data without taking ownership, you can borrow it.

This is done by creating a reference.

Immutable Borrows:

You can have multiple immutable references (&T) to a value simultaneously.

This is read-only access.

fn main() {
    let s1 = String::from("hello")
    let r1 = &s1; // Immutable borrow
    let r2 = &s1; // Another immutable borrow is fine
    println!("r1 = {}, r2 = {}", r1, r2); // This works perfectly.
}

Mutable Borrows: You can only have one mutable reference (&mut T) to a value in a particular scope.

This prevents data races. (Google it to know more)

fn main() {
    let mut s1 = String::from("hello");
    let r1 = &mut s1; // One mutable borrow
    // let r2 = &mut s1; // Error: cannot borrow `s1` as mutable more than once at a time
    r1.push_str(", world!");
    println!("{}", r1);
}

• You cannot have a mutable borrow while immutable borrows exist.

fn main() {
    let mut s = String::from("hello");
    let r1 = &s; // immutable borrow
    let r2 = &mut s; // Error: cannot borrow `s` as mutable because it is also borrowed as immutable
    // println!("{}, {}", r1, r2);
}

The borrow checker is the part of the Rust compiler that enforces all of these ownership and borrowing rules.

Beginner Rust programmers often spend a significant amount of time “fighting the borrow checker.”

This process involves learning to structure code in a way that satisfies Rust’s safety guarantees.

This initial struggle, while frustrating, is what builds the fundamental understanding required to write safe and efficient Rust code.

It’s like having a strict mentor who forces you to build good habits from day one.

Other Arcane Rules That Make Rust Difficult

Lifetimes:

• Lifetimes are the syntax Rust uses to tell the borrow checker how long references are valid.

• In many cases, the compiler can infer lifetimes, but sometimes you must annotate them explicitly using an apostrophe syntax, like ‘a.

• Lifetimes prevent dangling references, where a reference points to memory that has been deallocated.

// We must explicitly tell the compiler that the returned reference (`&'a str`)
// lives at least as long as the shortest-lived input reference (`'a`).
fn longest<'a>(x: &'a str, y: &'a str) -> &'a str {
    if x.len() > y.len() {
        x
    } else {
        y
    }
}

Traits:

• A trait tells the Rust compiler about functionality a type must provide.

• It’s similar to an interface in languages like Java or C#.

• It enables incredibly powerful polymorphism capabilities.

// Define a trait `Summary`
pub trait Summary {
    fn summarize(&self) -> String;
}

// Implement the trait for the `Tweet` struct
pub struct Tweet {
    pub username: String,
    pub content: String,
}

impl Summary for Tweet {
    fn summarize(&self) -> String {
        format!("{}: {}", self.username, self.content)
    }
}

Generics:

• Generics are abstract stand-ins for concrete types.

• They allow you to write flexible code that avoids duplication by operating on many different data types.

// This function can take any type `T` that implements the `PartialOrd` and `Copy` traits.
fn largest<T: PartialOrd + Copy>(list: &[T]) -> T {
    let mut largest = list[0];
    for &item in list.iter() {
        if item > largest {
            largest = item;
        }
    }
    largest
}

How AI Can Act as a Highly Reliable Free Tutor

Advanced prompt engineering is not required.

To learn Rust, you can actually speak in English-like language to the AI!

For example:

”Teach me the basic concepts of Rust.”

“Help me create a project in Rust.”

“Help me install Rust in my Windows/Linux/Mac system.”

“Write a program in Rust for …”

“Please debug this Rust program (paste the program below).”

“Help me understand this <Rust concept> with an example.”

“Explain this concept to me as if I were 10 years old.”

“Explain the borrowing checker and ownership model to me as if I were a teenager.”

“Explain this error message to me in simple terms and show me how to correct the code“

It is so simple that children can and are doing it!

An AI assistant like Google Gemini in Google AI Studio can act as a:

1. Tireless

2. Interactive

3. Personalized

4. Adjustable

5. Custmizable

6. Powerful

7. Gentle

8. Patient

9. Kind

And an infinitely patient tutor for learning Rust’s complex concepts.

Instead of just reading documentation:

1. You can have a conversation with the AI.

2. You can ask it to rephrase explanations or provide different examples:

Until a concept clicks.

This is a game-changer for developers who had to pore through 900-page textbooks.

An AI can take an intimidating compiler error and translate it into plain English.

It can explain why the error occurred and suggest multiple ways to fix it.

This is a superpower for anyone learning Rust on multiple levels!

You can use the following roadmap to learn Rust:

https://roadmap.sh/rust

You can use AI (LLMs) to understand every concept you do not understand.

Every doubt, every concept, and every difficult process can be explained with Google AI Studio.

Switch models to avoid running out of rate limits (**Gemini Flash, or Flash-Lite instead of Gemini Pro).

You can use multiple AI models if you run out of all free usage limits in Google AI Studio:

Claude:

https://claude.ai/

ChatGPT:

https://chatgpt.com/

Perplexity:

https://www.perplexity.ai/

DeepSeek:

https://chat.deepseek.com/

Grok:

https://grok.com/

I placed them in order of my use, and with so many other (Mistral, Qwen, Kimi) generative AI models available for free:

You do not need to pay for a subscription to learn Rust with AI.

How. Awesome. Is. That?

For Example

// User pastes this code and the resulting error:
fn main() {
    let mut v = vec![1, 2, 3];
    let first = &v[0];
    v.push(4); // Error: cannot borrow `v` as mutable...
    println!("The first element is: {}", first);
}

AI Explanation:

“The error happens because you have an immutable reference first to an element inside the vector v.

Then, you try to call v.push(4), which is a mutable operation.

Pushing an element might require the vector to reallocate its memory, which would invalidate the first reference.

To fix this, you should ensure the immutable reference first is no longer in use before you mutate the vector.

For example, you could move the println! before the push.”

Screen Sharing

This is why I highlighted Google AI Studio over other tools.

Practically none of the other AI tools I mentioned share this feature as well-developed as Google Gemini as of the date of writing this article.

• Gemini’s potential screen-sharing capability could be a lifesaver for Rust debugging.

• A learner could share their VS Code window:

• Highlight a block of code rejected by the borrow checker,

• And get a live, contextual explanation

• Along with refactoring suggestions

• It could be your lifesaver in critical situations, such as debugging a server outage.

• Screen sharing is not a paid feature.

• You can access it for free with Google AI Studio.

• There are rate limits for the free version, so use with discretion.

And of course, once you learn more about Rust, you can start asking Gemini to generate code for you.

Disclaimer: Ensure you thoroughly understand the code you generate, especially in production environments. Especially then!

For Example

User Prompt: “Generate a basic web server in Rust using the Axum framework that responds with ‘Hello, World!’ at the root URL.”

use axum::{routing::get, Router};
use std::net::SocketAddr;
async fn root() -> &'static str {
    "Hello, World!"
}
#[tokio::main]
async fn main() {
    let app = Router::new().route("/", get(root));
    let addr = SocketAddr::from(([127, 0, 0, 1], 3000));
    println!("listening on {}", addr);
    axum::Server::bind(&addr)
        .serve(app.into_make_service())
        .await
        .unwrap();
}

Advantages of Google Gemini AI as a Teacher

• Experiences from other educational AI tools, like Khan Academy’s tutor, show that AI can:

• Provide guided practice

• Answer silly repeated doubts

• Be tailored to your level

• Provide hints

• Be available at any time of the day

• Correct itself when fact-checked with the compiler

• Add encouragement

• Provide multi-domain knowledge such as AWS, GCP, Azure, and others

• Screen sharing can anticipate the required correction

• Provide company-saving advice during site outages

Applying this to Rust:

• An AI can create a personalized learning path.

• If you struggle with Lifetimes:

• It can generate a series of targeted exercises and code-reading challenges to strengthen that specific skill.

• Ditto for Ownership Rules

• And for Traits

• And for Third Party Libraries Documentation

• And any concept you do not understand

• An AI excels at explaining complex, real-world code that would be intimidating to a beginner.

• You can examine the explanation below:

Example: Foreign Function Interface (FFI)

// Opaque C struct
struct CComplexObject;// Functions imported from a C library
extern "C" {
    fn c_complex_new() -> *mut CComplexObject;
    fn c_complex_process(obj: *mut CComplexObject, value: i32);
    fn c_complex_destroy(obj: *mut CComplexObject);
}
// Idiomatic and safe Rust wrapper
struct SafeWrapper {
    obj: *mut CComplexObject,
}

impl SafeWrapper {
    fn new() -> Self {
        Self { obj: unsafe { c_complex_new() } }
    }
fn process(&mut self, value: i32) {
        unsafe { c_complex_process(self.obj, value) };
    }
}
impl Drop for SafeWrapper {
    fn drop(&mut self) {
        unsafe { c_complex_destroy(self.obj) };
    }
}

AI Explanation:

• extern “C” tells Rust you are defining functions that follow the C Application Binary Interface (ABI), allowing Rust to link with C libraries.

• \mut CComplexObject is a raw pointer.*

• It’s unsafe because the Rust compiler cannot guarantee what it points to is valid.

• The unsafe keyword is required to call these C functions or dereference raw pointers, as you are telling the compiler, “I have read the rules and I guarantee this operation is safe.”

• The SafeWrapper struct is an idiomatic way to manage the raw pointer.

• It holds the pointer internally.

• By implementing the Drop trait, you ensure that the C object’s memory is always cleaned up (c_complex_destroy) when the SafeWrapper goes out of scope, preventing memory leaks.

• This is a core Rust pattern called RAII (Resource Acquisition Is Initialization).

Mitigating the Risk of Hallucinations

All LLMs, even Gemini, can hallucinate sometimes.

But we have an advantage: the Rust compiler!

The most powerful advantage of using AI to learn Rust is the feedback loop with the compiler.

You can treat the compiler as the ultimate source of truth.

How to check for AI Hallucinations:

1. Ask Gemini to write Rust code for a specific task.

2. The AI generates the code.

3. Paste this code directly into your local main.rs file or the online Rust Playground

4. Run cargo check.

5. This command checks your code for errors without producing an executable.

6. If the AI’s code was incorrect (a “hallucination”):

7. The Rust compiler (rustc) will almost certainly catch it.

8. It will produce a high-quality, specific error message.

9. You can then take this error message, feed it back to the AI, and ask:

10. “The compiler gave me this error. Can you fix the code?”

This process of validating AI output with the compiler is an incredibly effective learning tool.

The Huge Value of Learning Rust Today

Rust is in huge demand today for systems programming.

The compile-time safety for concurrency has caught the eye of numerous companies that scale to billions of users.

Perhaps the only language in greater demand is Python for automation and agents.

And Rust is catching up in the agentic space, too!

Companies Pivoting to Rust

• Microsoft:

• Actively rewriting core Windows components, like parts of the kernel and the Win32 API, in Rust to reduce memory safety vulnerabilities.

• Amazon Web Services (AWS):

• Built Firecracker, the virtualization technology powering AWS Lambda and Fargate, entirely in Rust for security and speed. They also use it in parts of S3 and CloudFront.

• Google:

• Supports Rust for Android OS development and is funding efforts to write new Linux kernel modules in Rust, particularly for drivers, to improve kernel safety.

• Meta (Facebook):

• Rewrote their Mononoke source control server from C++ to Rust, citing performance and reliability improvements. They also use it heavily in their blockchain development.

• Apple:

• Apple is investing heavily in Rust for Robotics, AR/VR code, and Neural Engine processing. Hiring for Rust is at an all-time high.

• Cloudflare:

• Rust is used extensively across its product stack for performance-critical services, including its firewall, CDN, and Workers platform.

• Discord:

• Replaced a Go service with Rust to solve latency spikes in a real-time service, demonstrating Rust’s superior performance for low-latency applications.

The Critical Fundamental Step: Real Projects

You can’t learn a language just by studying its features.

You need to get your hands dirty with real projects.

You need to build high-quality projects that solve real business problems.

And if possible, open-source them and gain public traction.

If you do that, companies will come to you instead of you having to go to them!

This GitHub project below is an excellent guide for systems programming details:

https://github.com/codecrafters-io/build-your-own-x

There are even tutorials focused on Rust in this fantastic repository.

This type of inbound recruitment is the best possible chance you have of standing out in today’s killer market.

Thousands of AI-generated resumes are hitting recruiters.

An open-source project that companies are already using could be your back door into MAANG.

There are many choices available!

Finally, the key moment you’ve been waiting for!

The foundation has been firmly set!

15 Project Ideas That Could Get You Into MAANG

Artificial Intelligence and Machine Learning

1. A “Zero-Trust” Federated Learning Framework:

• Implement a secure multi-party computation (MPC) framework in Rust for training machine learning models on decentralized data.

• Provide a production-ready, performant, and memory-safe alternative to current Python-based frameworks, which often struggle with security and efficiency in real-world federated learning scenarios.

2. High-Performance Inference Engine for Edge AI:

• evelop a lightweight, high-performance inference engine in Rust, optimized for resource-constrained edge devices.

• Create a runtime that is significantly faster and more memory-efficient than existing solutions like TensorFlow Lite, enabling complex AI models to run on a wider range of IoT devices and sensors.

3. A Verifiable and Reproducible ML Pipeline Tool:

• Build a tool that ensures the entire machine learning pipeline, from data preprocessing to model training, is cryptographically verifiable and reproducible.

• Leverage Rust’s performance to create a tool that can handle large-scale datasets and complex models, addressing the critical need for trust and auditability in AI systems.

Blockchain and Web3

4. A Scalable and Interoperable Blockchain Sharding Implementation:

• Design and implement a novel sharding mechanism for a blockchain that addresses the trilemma of scalability, security, and decentralization.

• Create a Rust-based solution that is more performant and secure than existing sharding approaches, a major hurdle for mainstream blockchain adoption.

5. A Privacy-Preserving Smart Contract Platform with Zero-Knowledge Proofs:

• Build a blockchain platform that allows for confidential smart contracts using zero-knowledge proofs.

• Create a developer-friendly platform in Rust that simplifies the creation of privacy-preserving decentralized applications (dApps), a significant gap in the current Web3 ecosystem.

6. A High-Throughput, Cross-Chain Communication Protocol:

• Develop a secure and efficient protocol for interoperability between different blockchain networks.

• Build a Rust-based solution that is significantly faster and more reliable than existing bridge protocols, which are often bottlenecks and security risks in the Web3 space.

Generative AI and Transformers

7. An Optimized Inference Server for Large Language Models (LLMs):

• Create a highly optimized serving framework for LLMs that minimizes latency and maximizes throughput.

• Leverage Rust’s concurrency and low-level control to build a server that can handle massive-scale inference for generative AI applications, a major operational challenge for companies deploying these models.

8. A Memory-Efficient Transformer Architecture:

• Implement a novel Transformer architecture in Rust that significantly reduces the memory footprint during training and inference.

• Address the quadratic complexity of the self-attention mechanism, a major bottleneck for working with long sequences, making large models more accessible and cost-effective to train and deploy.

9. A Framework for Fine-Tuning and Deploying Generative AI Models with Strong Security Guarantees

• Develop a framework that focuses on the secure fine-tuning and deployment of generative AI models, addressing concerns like data privacy and model inversion attacks.

• Provide a Rust-based solution that integrates privacy-enhancing technologies directly into the MLOps lifecycle for generative AI.

Quantum Computing

10. A High-Performance Quantum Circuit Simulator:

• Build a quantum circuit simulator in Rust that can handle a larger number of qubits and more complex quantum algorithms than existing simulators.

• Leverage Rust’s performance and memory management to push the boundaries of classical simulation of quantum computers, a critical tool for quantum algorithm development.

11. A Post-Quantum Cryptography Library for Secure Communication:

• Develop a comprehensive and easy-to-use library for post-quantum cryptography algorithms.

• Provide a production-ready Rust library that is highly performant and resistant to attacks from both classical and quantum computers, a critical need as the threat of quantum computing to current encryption standards grows.

12. A Compiler for a High-Level Quantum Programming Language:

• Create a compiler that translates a high-level, expressive quantum programming language into low-level quantum assembly code.

• Build this compiler in Rust to ensure its correctness and performance, enabling developers to write complex quantum algorithms more easily and with greater confidence.

DevOps and MLOps

13. A Blazing-Fast, Cross-Platform Build and Deployment Tool:

• Develop a next-generation build and deployment tool in Rust that is significantly faster and more efficient than current solutions like Jenkins or Travis CI.

• Create a tool with a minimal footprint and first-class support for containerization and modern cloud-native environments, addressing the need for faster and more reliable CI/CD pipelines.

14. A Secure and Observable Microservices Framework:

• Build a microservices framework in Rust that prioritizes security and observability from the ground up.

• Provide a framework with built-in features for service-to-service authentication, authorization, and detailed telemetry, addressing the growing complexity and security challenges of microservices architectures.

15. An MLOps Platform for Rust-Based Models:

• Create an MLOps platform specifically designed for the lifecycle management of machine learning models written in Rust.

• Provide a seamless workflow for training, deploying, monitoring, and retraining Rust-based models, filling a gap in the current MLOps tooling which is heavily focused on Python.

This is So Complex! I am a Beginner! (AI to the Rescue)

Zoom image will be displayed

Remember, this is your pathway to a MAANG company.

And AI is here to help you!

For a beginner, tackling a large, complex project is a marathon, not a sprint.

AI assistants like Google AI Studio can act as your personal tutor and coding partner.

Open-source collaboration provides the community and support to see it through.

Your Logical Thought Process (Baby Steps)

Understand the Core Problem:

• Before writing a single line of code, use AI to explain the project’s domain.

• Ask it, “Explain federated learning in simple terms”.

• Or, “What is the blockchain scalability trilemma?”

Break It Down:

• Ask your AI assistant, “I want to build a quantum circuit simulator in Rust. What are the main components I need to build?”

• Break this down into smaller, manageable tasks.”

Generate the Skeleton:

• For a small task like “create a struct for a blockchain block,” ask the AI to generate the initial Rust code.

• This gives you a starting point to build upon.

Code, Test, Refine, Repeat:

• Write your code for one small part.

• If you hit an error, paste the code and the error message into the AI assistant and ask for help debugging.

Go Public Immediately:

• Create a project on a platform like GitHub from day one.

• This signals your intent to collaborate and makes it easier for others to join.

Document Your Journey:

• Use the AI to help you write a clear README.md file explaining your project’s goal and how others can help.

• A good project description is critical for attracting collaborators.

How AI Assistants Help

• Concept Explanation: Ask for simple explanations of complex topics like “What is a zero-knowledge proof?”.

• Code Generation: Generate boilerplate code, functions, and data structures to get you started.

• Debugging: Paste your broken code and the error message to get suggestions for a fix.

• Writing Documentation: Ask the AI to generate comments for your functions or to write a project README.md file.

• Learning: Use it as an interactive tutor that can answer your specific questions as they arise.

The Power of Open Source

Every project you build should be open-source.

You don’t need to be an expert; you need an idea and the willingness to collaborate.

• Shared Workload: You don’t have to build it all yourself.

• Learn from Others: Reviewing code from contributors is a powerful way to learn.

• Get Feedback: More eyes on your code means better quality and faster bug-finding.

• Build a Network: Connect with other developers who could become mentors or future colleagues.

Critical First Steps for Beginners

Learn Rust Fundamentals:

• Before starting a big project, complete a basic course.

• You can’t use an AI assistant effectively without understanding the basics of the programming language.

Make sure you understand Git

• Git is fundamental.

• You should be familiar with the basics of Git and GitHub.

Choose a Single Project:

• Pick the idea that excites you the most and stick with it.

• Focus is an underrated superpower.

Create a Public Repository:

• Use GitHub to host your project.

• Blog about your product on Medium or HackerNoon.

Define a Clear Goal:

• Write a one-paragraph description of what your project aims to achieve.

• Use AI if necessary, but ensure you understand everything you are discussing.

• AI can help you in that, too!

Find “Good First Issues”:

• Look for beginner-friendly tasks in other open-source projects to get a feel for the contribution process.

• This gives you credibility and experience.

• Also, actual merged open-source contributions are a dopamine hit!

Actively Seek Collaborators:

• Share your project on platforms like the /r/rust subreddit or Discord communities.

• Create awareness about your project online with HashNode and LinkedIn as well.

Use Perplexity.ai for resources for learning just about anything!

Blockchain, AI Agents, and Generative AI are currently killer applications for Rust!

Conclusion

• Rust stands as one of the most powerful but challenging languages to learn today.

• It has a syntax and a set of rules unlike any other mainstream language.

• This steep learning curve can be flattened significantly by leveraging AI tools like Google AI Studio as a tutor.

• Integrations like screen sharing will make debugging complex ownership issues a guided, conversational process.

• Rust is the definitive future of secure and performant systems programming, and its adoption by every major tech company proves its value.

• The current shortage of high-quality Rust developers, combined with the growing demand, makes learning Rust an invaluable career investment.

• Therefore, there has never been a more opportune moment to begin your journey with Rust.

• With AI as your guide, the path is more straightforward than ever before.

I found the article below remarkably helpful for effectively preparing for a successful Rust career:

https://medium.com/@CodeOps/i-spent-2-years-learning-rust-and-still-got-rejected

All the best, and follow the advice in the article above if you are preparing for a Rust development career!

References

1. Stack Overflow. (2023). 2023 Developer Survey. https://survey.stackoverflow.co/2023/#most-loved-dreaded-and-wanted-language-love-dread

2. Khan Academy. (2023). Harnessing AI for education. https://www.khanmigo.ai/

3. The Rust Language Revolution: Why Are Companies Migrating?
   https://stefanini.com/en/insights/news/the-rust-language-technology-revolution-why-are-companies-migrating

4. 2022 Review | The adoption of Rust in Business
   https://rustmagazine.org/issue-1/2022-review-the-adoption-of-rust-in-business

5. Rust in 2025: Why Meta, Google, and Apple Are All In | Rustaceans

6. https://medium.com/rustaceans/why-meta-google-and-apple-are-secretly-betting-on-rust-in-2025

7. Microsoft joins Rust Foundation — Microsoft Open Source Blog
   https://opensource.microsoft.com/blog/2021/02/08/microsoft-joins-rust-foundation

8. Google Adopts Rust for Android: A Game-Changer for Mobile OS Security & Performance
   https://coinsbench.com/googles-rust-adoption-in-android-a-game-changer-for-mobile-os-development

9. Amazon Web Services. (2020). Why AWS loves Rust. https://aws.amazon.com/blogs/opensource/why-aws-loves-rust-and-how-wed-like-to-help/

10. Discord Blog. (2020). Why Discord is switching from Go to Rust. https://discord.com/blog/why-discord-is-switching-from-go-to-rust

11. General Rust Learning

• The Rust Programming Language (“The Book”)

• https://doc.rust-lang.org/book

• Rustlings Exercises

• https://github.com/rust-lang/rustlings

• Comprehensive Rust (by Google)

• https://google.github.io/comprehensive-rust/

12. AI/ML in Rust

• Linfa (A scikit-learn-like ML Framework)

• https://github.com/rust-ml/linfa

• https://rust-ml.github.io/linfa/

• Tutorial: Build a Machine Learning Model in Rust

• https://www.freecodecamp.org/news/how-to-build-a-machine-learning-model-in-rust/

13. Blockchain & Web3 in Rust

• Substrate (Blockchain Framework)

• https://substrate.io/

• Soroban (Smart Contract Platform on Stellar)

• https://soroban.stellar.org/

• Tutorial: Building a Blockchain in Rust

• https://blog.logrocket.com/how-to-build-a-blockchain-in-rust/

14. Generative AI in Rust

• Hugging Face Candle (A minimalist ML framework)

• https://github.com/huggingface/candle

15. DevOps in Rust

• Book: Command-Line Applications in Rust

• https://rust-cli.github.io/book

Google AI Studio was used in this article for ideation, outlining, code, and research. You can access it here:

https://aistudio.google.com/

All images were generated by the author using NightCafe Studio, available here:
https://creator.nightcafe.studio/explore