Interdum et malesuada fames ac ante ipsum primis in faucibus. Etiam eu nibh elementum, accumsan ona neque ac, aliquet nunc. In eu ipsum fringilla, accumsan purus vel, pellentesque risus. Vivamus vehicula nl purus at eros interdum, in dignissim nulla vestibulum. Nunc sit amet finibus felis, ut egestas lacus. Sedan pellentesque quis magna eu vestibulum. Ut sed commodo neque. Morbi erat nisi, vehicula quis faucibus il ut, hendrerit vel tortor. In pharetra lectus luctus ornare sollicitudin. Pellentesque at neque nec justo sokal porttitor egestas nec eget ex.Etiam suscipit neque elit, hendrerit laoreet quam ultrices id. Proin nec tolde lacinia ligula, sed laoreet ex. Sed nisl ligula, euismod vel justo scelerisque, vestibulum ultricies tellus. volv Pellentesque vel turpis vitae urna tincidunt hendrerit at ut est. Sed eget feugiat felis. Integer sed ornare sem, eget porttitor nisi. Nunc erat sapien, porta laoreet gravida ac, dictum eu tortor. Nulla faucibus leoren rhoncus, gravida ligula a, ultrices enim. Proin lacinia malesuada finibus. Proin sit amet arcu sem. dontami Phasellus ut pharetra purus, sed condimentum dui. Suspendisse potenti. Nam bibendum, augue europea ultricies semper, nisi lorem consectetur diam, nec dapibus nulla tortor at sem.

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This blog explains everything about ChatGPT, how it works, and why it’s becoming one of the most popular AI tools today.

Everything You Need to Know About the AI-Powered Chatbot

Finding the perfect YouTube video can be frustrating due to algorithm-driven suggestions that often miss the mark. To address this, YouTube is testing a new feature called “Your Custom Feed”.  Users in the experiment will see a new option next to the standard Home button on the homepage. Clicking it allows them to input prompts for topics they want to see more of, such as tech tutorials, cooking, or programming videos.  This proactive approach helps users avoid irrelevant recommendations and better control the type of content that appears in their feed. YouTube isn’t alone — other platforms like Threads and X are also testing customized feed features.  If adopted widely, “Your Custom Feed” could redefine how viewers interact with YouTube, making content discovery more relevant and personalized than ever before.

YouTube introduces a new experimental feature, “Your Custom Feed,” allowing users to customize their homepage with content tailored to their interests. This could change how we discover videos and manage algorithm-driven recommendations.

YouTube Launches “Your Custom Feed” to Fix Messy Home Recommendations

As a senior software engineer with years of experience in systems programming, I’ve seen how Rust’s type system can elevate code from functional to elegant. In this post, we’ll dive into best practices for using generics and traits, drawing from real-world patterns that reduce boilerplate and enhance flexibility. This isn’t beginner territory — we’ll assume you’re comfortable with Rust basics and focus on idiomatic usage for advanced designs.  Introduction Generics in Rust allow you to write functions, structs, and enums that operate on abstract types, parameterizing code to work across different data types without sacrificing type safety. For instance, a generic vector `Vec<T>` can hold integers, strings, or custom structs interchangeably.  Traits, on the other hand, define shared behavior or interfaces that types can implement. They’re like contracts: a type implementing `Iterator` promises to provide `next()` functionality.  Together, generics and traits matter because they promote code reuse, enforce compile-time guarantees, and enable polymorphic designs. Without them, you’d duplicate code for each type, leading to maintenance nightmares. In performance-critical systems like those I build, they ensure efficiency without runtime overhead, thanks to monomorphization — Rust’s way of generating specialized code at compile time.  Generics Improving Code Design Generics shine when they abstract away type-specific details, reducing duplication and improving API ergonomics. Consider a scenario where you need a function to find the larger of two values. Without generics, you’d write separate versions for `i32`, `f64`, etc.  Here’s a non-generic approach:  fn max_i32(a: i32, b: i32) -> i32 {  if a > b { a } else { b } }  fn max_f64(a: f64, b: f64) -> f64 {  if a > b { a } else { b } } This works but scales poorly. Now, with generics:  fn max<T: Ord>(a: T, b: T) -> T {//PartialOrd  if a > b { a } else { b } }  fn main() {  println!("{}", max(5, 3)); // 5  println!("{}", max(3.14, 2.71)); // 3.14 } (We’ll cover the `Ord` bound shortly.) This is a best practice because it eliminates redundancy — one function handles any comparable type. It also improves API design: users aren’t locked into specific types, making your library more flexible and easier to extend. In larger codebases, this abstraction hides implementation details, fostering better modularity.  Type Restrictions with Traits Unbounded generics are powerful but often too permissive. To restrict types, use trait bounds like `T: TraitName`, ensuring `T` implements the required behavior. This catches errors at compile time and documents intent clearly.  For example, our `max` function needs types that support comparison, so we bound `T` to `Ord` (which implies `PartialOrd` and `Eq`):  use std::cmp::Ord;  fn max<T: Ord>(a: T, b: T) -> T {  if a > b { a } else { b } }  #[derive(Ord, PartialOrd, Eq, PartialEq)] struct Point {  x: i32,  y: i32, }  fn main() {  let p1 = Point { x: 1, y: 2 };  let p2 = Point { x: 3, y: 0 };  let larger = max(p1, p2);  println!("Larger point: ({}, {})", larger.x, larger.y); // (3, 0) } Here, `Point` implements `Ord` via derive, allowing custom ordering (by default, lexicographical). The benefit? Compile-time safety: trying `max` on non-comparable types fails early. This practice enhances robustness — your generic code only accepts valid inputs — and promotes trait-oriented design, where behaviors are composable.  Using the `AddAssign` Trait The `AddAssign` trait defines the `+=` operator, allowing in-place addition. It’s relevant in generic code for mutable operations on accumulators or counters, ensuring types support efficient modification without unnecessary allocations.  Become a member When designing generics, bounding with `AddAssign` lets you write functions that mutate values generically. For instance, a generic summer:  use std::ops::AddAssign;  fn accumulate<T: AddAssign + Default + Copy>(values: &[T]) -> T {  let mut total = T::default();  for &value in values {  total += value;  }  total }  fn main() {  let ints = [1, 2, 3];  println!("{}", accumulate(&ints)); // 6 let floats = [1.5, 2.5, 3.5];  println!("{}", accumulate(&floats)); // 7.5 } (We use `Copy` for borrowing; more on that next.) This is useful in performance-sensitive code, like data processing pipelines, where in-place ops avoid cloning. Implementing `AddAssign` for custom types extends this to domains like vector math, making your APIs more operator-friendly.  Using the `Copy` Trait The `Copy` trait marks types that can be duplicated trivially via bit-wise copy, without invoking `Clone`. It’s for small, cheap-to-copy types like primitives (`i32`, `bool`) or structs/enums containing only `Copy` fields. Implement it when your type’s semantics allow implicit copying — e.g., not for resources like files or large arrays.  In generics, `Copy` interacts by allowing value semantics without ownership transfer. Without it, generics might require `Clone`, which is costlier.  Example: A generic stack that pushes copies:  #[derive(Debug, Copy, Clone)] struct Pair<T>(T, T);  fn push_copy<T: Copy>(stack: &mut Vec<T>, value: T) {  stack.push(value); // Copies value }  fn main() {  let mut stack: Vec<Pair<i32>> = Vec::new();  let p = Pair(1, 2);  push_copy(&mut stack, p);  push_copy(&mut stack, p); // p still usable  println!("{:?}", stack); // [Pair(1,2), Pair(1,2)] } Here, `Copy` shapes API design by enabling pass-by-value without moving, ideal for immutable data. Best practice: Derive `Copy` only for types under ~128 bytes; otherwise, use references or `Clone` to avoid hidden performance hits.  A Better Way to Call Generics Idiomatic Rust favors explicitness in generics. When defining, use `where` clauses for complex bounds to improve readability:  fn process<T>(data: T) where  T: std::fmt::Debug + Clone, {  let cloned = data.clone();  println!("{:?}", cloned); } For calling, infer types when possible, but use turbofish (`::<>`) for ambiguity:  let vec: Vec<i32> = (1..10).collect(); // Inferred let parsed = "42".parse::<i32>().unwrap(); // Turbofish needed Common mistakes: Over-bounding (e.g., requiring `Clone` when `&T` suffices) leads to unnecessary constraints. Avoid by preferring references. Another pitfall: Ignoring lifetimes in generics — always specify `’a` when borrowing. Test generics with multiple types early to catch edge cases.  Conclusion We’ve covered key best practices: Leverage generics for abstraction and reuse, constrain them with traits like `Ord` for safety, use `AddAssign` for mutable ops, apply `Copy` judiciously for efficient copying, and define/call generics idiomatically to avoid pitfalls.  These patterns aren’t just theory — they’ve streamlined my production codebases, reducing bugs and boosting maintainability. I encourage you to refactor a generic-heavy module in your next project. Experiment with bounds and see how they tighten your designs. Share your experiences in the comments — what’s your go-to trait bound?

Master Rust’s Generics and Traits to write cleaner, safer, and more flexible code in your projects.

Mastering Generics and Traits in Rust: Best Practices for Cleaner Code

In my first week exploring API Security, I dived into the fundamentals of how APIs can be vulnerable and how attackers exploit those weaknesses.  I completed the API Security Fundamentals course by APIsec University, where I learned about:  The structure of modern API security threats and how they differ from traditional web security issues. The OWASP API Security Top 10 and real-world implications of each category The importance of API authentication, authorization, proper rate limiting and input validation, avoiding exposure of internal objects, especially in error responses etc. The value of secure-by-design approaches in API development and deployment But the real learning came when I got hands-on.  Using a provided OpenAPI file (openapi3.yaml), I conducted a structured security assessment of a sample API. The goal was to simulate how attackers identify vulnerabilities and to learn how to spot and fix them.  What follows is a walkthrough of how I set up my testing environment, identified vulnerabilities like Excessive Data Exposure, Mass Assignment and Broken Object Level Authorization (BOLA) and what I learned along the way.  OpenAPI Spec Overview  Before diving into testing, here’s a quick look at the API definition I worked with:    "openapi": "3.0.1",   "info": {     "title": "VAmPI",     "description": "OpenAPI v3 specs for VAmPI",     "version": "0.1"   },   "servers": [     {       "url": "http://localhost:5000"     }   ] I. Setting Up the Testing Environment To begin my practical assessment, I needed to prepare a working API testing setup using the provided openapi3.yaml file from the VAmPI project. Since I wasn’t running the API locally, I used Postman along with mock servers to simulate and interact with the API endpoints.  Here’s how I set up everything:  a. Converting YAML to JSON The original API specification was in OpenAPI 3.0 YAML format. To make it Postman-compatible, I:  Opened the openapi3.yaml file in Swagger online editor. Exported the spec as JSON. Saved the converted file as openapi3.json. b. Importing into Postman In Postman, I used the “Import” feature to load the json file.  This automatically generated a collection with all the API endpoints defined in the spec.  c. Creating a Mock Server Since I couldn’t connect to a real backend (the API wasn’t hosted), I used Postman to create a mock server   Mock server created in Postman using the OpenAPI spec to simulate responses for VAmPI endpoints This allowed me to simulate the expected responses of the API.  I configured the mock server using examples from the spec and set it as the base URL in the collection settings.  II. Finding Vulnerabilities Excessive Data Exposure At first I identified the endpoint /createdb, which creates and populates the database with dummy data to provide some base data to start with.  Now proceed to register a new user using the Register new user endpoint /users/v1/register. Here’s the request body used:  {   "username": "name1",   "password": "pass123", } Press enter or click to view image in full size  Created new user ‘name1’ Then I logged this new user in using the Login to endpoint /users/v1/login.  After logging in, you receive an auth token, message and status.  Press enter or click to view image in full size  Login successful: the auth_token, message and status In the response body, the username, password and email of the user are returned.  You can see that a JSON Web Token (JWT) is provided in the response body.  I copied this JWT and pasted it into a new collection variable.  Press enter or click to view image in full size  Jwt token variable This is done by navigating to the Authorization tab. In the Type drop-down menu, selected Bearer Token and then set the variable I just created as the token value.  Press enter or click to view image in full size  Token for authorization Now, whenever I need to reissue a new token to this user, this token will be used in the Authorization request header for API calls.  Once this was set up, I tested the Retrieves all data for all users’ endpoint /users/v1/_debug.  This endpoint is typically used for debugging purposes and may have been left behind unintentionally by the developer before releasing the production version of the API.  Press enter or click to view image in full size  Excessive data exposure As shown in the response, this endpoint returns the email, password and username of all users in the database.  Become a member It also reveals a parameter called admin, which is a boolean and can be either true or false.  This is the Excessive Data Exposure through the debug endpoint vulnerability.  2. Mass Assignment  Now that I have found this admin parameter, I attempted to see whether I could use it to create an admin user.  Returned to the Register new user endpoint and created a new user.  This time, I added an “admin”: true line to the request body  Press enter or click to view image in full size  creating an admin user The request was accepted successfully.  To verify this, I sent another request to the Retrieves all data for all users’ endpoint /users/v1/_debug.  In the response body, the newly created user appears at the bottom of the list with an admin status set to true.   This confirms a Mass Assignment vulnerability, the application allowed arbitrary modification of internal properties during user registration.  3. Broken Level Object Authorization  Rgistered and Logged in as the new user I just created and used this user’s JWT in the Authorization request header.  Press enter or click to view image in full size  user created Since I will be switching between the JWTs of multiple users, I created a collection variable for this second user’s JWT and used that variable in the Token field of the Authorization tab in the collection.  Next, I went to the Add new book endpoint POST /books/v1. As user Rachael, I created a new book.  Then I added a book successfully using her JWT.  Press enter or click to view image in full size  Added a book for the new user successfully After that, I retrieved all books using the appropriate endpoint.  In the response body, the list of all books in the database is returned with their titles and corresponding user, but the secret is not displayed.  Press enter or click to view image in full size  Retrieves all books but secret is not displayed Each book is unique to its respective user and only the owner should be allowed to view the secret.  Then, I accessed the Retrieves book by title along with secret endpoint /books/v1/:book_title and switched to user1’s JWT in the collection’s Authorization tab.  The request was successful and returned the book’s title, the submitting user and the secret.  Press enter or click to view image in full size  Secret is now returned This confirms a Broken Object Level Authorization (BOLA) vulnerability. A user is able to access sensitive resources of other users by simply switching JWTs and targeting objects not owned by them.  III. Mitigation Strategies After identifying vulnerabilities, it’s important to understand the risks they pose and how they can be effectively mitigated. Below is a summary of the main issues discovered, along with practical recommendations to improve API security.  Excessive Data Exposure  Remove or secure debugging endpoints (/users/v1/_debug) from production environments. Use response filtering on the server side to return only the minimum necessary data based on user role or request context. Never return sensitive fields (passwords, admin status) unless absolutely required and never return passwords even in hash form Implement access controls that restrict who can view debug or administrative data. Mass Assignment  Explicitly whitelist allowed input parameters (only accept username, email, password) and reject all others. Use Data Transfer Objects or serializers that define which fields can be set by the client. Never bind request data directly to sensitive model attributes like admin, is_superuser, roles, etc. Implement validation logic that checks the legitimacy of role or privilege-related values before saving to the database. Broken Object Level Authorization  Enforce object ownership checks on every data access request, ensure that the user making the request is authorized to access the specific resource. Do not rely solely on client-provided tokens or object identifiers; always verify ownership on the server. Use contextual authorization: For example, match book.user_id == auth_user.id before returning data. Implement centralized authorization logic to standardize access checks across endpoints. Final Thoughts This hands-on exploration into API security reinforced how critical it is to anticipate and defend against common vulnerabilities like Excessive Data Exposure, Mass Assignment and Broken Object Level Authorization. Even seemingly small oversights, such as leaving debug endpoints enabled or failing to validate input, can open the door to serious security breaches.  The experience highlighted the importance of secure design principles, thorough testing and robust authorization checks to protect sensitive data and maintain user trust. By applying these mitigation strategies early in the development lifecycle, organizations can significantly reduce their attack surface and improve overall API security.  As APIs continue to be the backbone of modern applications, understanding these risks and defenses is essential for every developer and security professional. This week’s training was just the beginning and I look forward to diving deeper into API security challenges and solutions in the coming weeks.

A hands‑on guide examining common API vulnerabilities like excessive data exposure, mass‑assignment, and broken object‑level authorization using the VAmPI OpenAPI v3 specification

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