Here's How You Can Use AI to Teach You Rust - 15 Projects To Impress MAANG Recruiters

Why have one monitor when you can have five? (I have three…)

Rust is the King Today - And AI Can Help You Learn It

It is not just challenging but also overpowering the long-standing dominance of C and C++.

It achieves this by providing:

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.

These lucky few find the features and guarantees of Rust both advantageous and productive.

Killer Features of Rust for Systems Programming

Rust has taken over the entire systems programming industry.

This is why.

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.

• 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.

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.

It’s every learner’s steep climb.

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

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

For example:

It is so simple that children can do it 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 energetic tutor for learning Rust's complex concepts.

You can use the following roadmap to learn Rust:

https://roadmap.sh/rust?embedable=true

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

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:

Screen Sharing

Practically none of the other AI tools I mentioned have 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, especially in production.

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.

AI-Generated Code

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

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

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:

Mitigating the Risk of Hallucinations

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.

How to Check for AI Hallucinations:

Other AI and Some Traditional Tools to Master Rust

Several AI and non-AI tools help to assist your Rust learning journey.

RustCoder

A free and open-source tool called RustCoder has been effectively used as a backend for modern Vibe coding tools like Cursor.

It is trained extensively on Rust code and can handle most of the Rust challenges that exist today.

It is open to all and free.

More details are available below:

https://www.cncf.io/blog/2025/01/10/rustcoder-ai-assisted-rust-learning/?embedable=true

The Rust Programming Language Online Documentation

The definitive guide to everything about Rust.

This is a classic resource that should not be omitted.

https://doc.rust-lang.org/stable/book/?embedable=true

The Companion Exercises - Rustlings

These exercises will help you work through the Rust online book systematically.

If you get stuck, you can ask an AI for the answer!

https://rustlings.rust-lang.org/?embedable=true

The Huge Value of Learning Rust Today

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.

Even today, many more companies are joining the bandwagon.

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.

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

https://github.com/codecrafters-io/build-your-own-x?embedable=true

There are so many choices available!

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:

   • Develop 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)

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.

Your Logical Thought Process (Baby Steps)

1. 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?"

2. 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."

3. 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.

4. 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.

5. 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.

6. 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

• 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

1. 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 language.

2. Make sure you understand Git:

   • Git is fundamental.

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

3. Choose a Single Project:

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

   • Focus is an underrated superpower.

4. Create a Public Repository:

   • Use GitHub to host your project.

   • Blog about your product on Medium or HackerNoon.

5. Define a Clear Goal:

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

   • Use AI if necessary, but make sure you understand everything you are talking about.

   • AI can help you in that, too!

6. 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 open source contributions are a dopamine hit!

7. 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.

Learning Resources

• Comprehensive Rust (by Google)

https://google.github.io/comprehensive-rust/?embedable=true

• Linfa (ML Library) User Guide

https://rust-ml.github.io/linfa/?embedable=true

• Build a Blockchain in Rust Tutorial

https://blog.logrocket.com/how-to-build-a-blockchain-in-rust/?embedable=true

• Command-Line Applications in Rust

https://rust-cli.github.io/book/?embedable=true

Conclusion

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-856b3f914520?embedable=true

Please take a moment to read it; you could save yourself two years of preparation that ends badly.

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

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

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

7. 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

8. Amazon Web Services. (2020). Why AWS loves Rust.

   https://aws.amazon.com/blogs/opensource/why-aws-loves-rust-and-how-wed-like-to-help/

9. Discord Blog. (2020). Why Discord is switching from Go to Rust.

   https://discord.com/blog/why-discord-is-switching-from-go-to-rust

10. 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/

    11. 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/

    12. Blockchain & Web3 in Rust

    • Tutorial: Building a Blockchain in Rust

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

      • https://dev.to/iamzubin/how-to-build-a-blockchain-from-scratch-in-rust-38d6

    13. Generative AI in Rust

    • Hugging Face Candle (A minimalist ML framework)

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

    14. Quantum Computing in Rust

    • Qiskit Rust (IBM's Quantum Framework Bindings)

      • https://github.com/Qiskit/qiskit-rust

    15. DevOps in Rust

    • Book: Command-Line Applications in Rust

      • https://rust-cli.github.io/book/