Lessons Learned from Algorithmic Impact Assessments in Practice
tl;dr: "Understanding algorithmic impact is critical to building a platform that serves hundreds of millions of listeners and creators every day. Our approach includes a combination of centralized and distributed efforts, which drives adoption of best practices across the entire organization — from researchers and data scientists to the engineer pushing the code."featured in #358
How Do One-Time Passwords Work?
- Serge Zaitsev tl;dr: "That’s just 16 lines of code! If you call totp() function passing it your secret key – you should get a 6-digit number that matches the one in the Google Authenticator"featured in #344
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featured in #340
Real World Recommendation System - Part 1
- Nikhil Garg tl;dr: "FAANG and other top tech companies have independently converged on a common architecture for production grade recommendation systems." This architecture is domain / vertical agnostic and can power all sorts of applications — from e-commerce and feeds to search, notifications, etc... Nikhil starts from the basics, explains nuances and describes this universal architecture.featured in #310
Introducing Swift Async Algorithms
- Tony Parker tl;dr: Part of Swift’s move toward safe, simple, and performant asynchronous programming, Swift have launched a package of algorithms, which has 3 main goals: (1) Integration with async/await. (2) Provide a home for time-based algorithms. (3) Be cross-platform and open source.featured in #305
Understanding Layout Algorithms
- Josh Comeau tl;dr: "The key realization I had is that CSS is so much more than a collection of properties. It's a constellation of inter-connected layout algorithms. Each algorithm is a complex system with its own rules and secret mechanisms." Josh looks at how this new lens can help make sense of what's happening in CSS. And use that lens to "solve a surprisingly-common mystery."featured in #304
Algorithms For Decision Making
- Mykel Kochenderfer Tim Wheeler Kyle Wray tl;dr: "This book provides a broad introduction to algorithms for decision making under uncertainty. We cover a wide variety of topics related to decision making, introducing the underlying mathematical problem formulations and the algorithms for solving them."featured in #299
Algorithms For Modern Hardware
- Sergey Slotin tl;dr: "This is an upcoming high performance computing book. Its intended audience is everyone from performance engineers and practical algorithm researchers to undergraduate computer science students who have just finished an advanced algorithms course and want to learn more practical ways to speed up a program."featured in #297
Self-Parking Car In 500 Lines Of Code
- Oleskii Trekhleb tl;dr: "Step-by-step we're going to break down a high-level task of creating the self-parking car to the straightforward low-level optimization problem of finding the optimal combination of 180 bits (finding the optimal car genome)."featured in #257