The hottest Concurrency Substack posts right now

Experience and age often bring wisdom, knowledge, and a unique perspective.
In technology, while tools and capabilities have evolved, fundamental principles like people dynamics, math, and physics remain constant.
Despite advancements, people still struggle with basic math, concurrent programming, and effective communication in group settings.

Ensuring correctness in multi-threaded programs is crucial; use locking and atomic instructions to prevent race conditions.
For optimality, ensure fairness among threads and efficient logic to avoid bottlenecks.
Divide workload evenly among threads or use a global variable to track progress for efficient results.

In C++, a RAII class like MutexLocker can automate locking and unlocking mutexes, avoiding manual calls.
With MutexProtected in C++, combining mutexes and data types ensures safe mutex access through a callback method.
Combining multiple fields under a single mutex in C++ simplifies concurrency management.

The development of a custom runtime is crucial for safely running AI-generated code, focusing on simplicity and user control.
Key features like pausing processes, green threads with backtracking, syntactic continuations, and fair resource management are essential for efficient operation.
Building a reliable runtime for AI-generated code involves creating APIs that prevent potential user errors, allowing for undoable actions and permission-seeking processes.

Software now needs to handle multiple tasks at once due to hardware limitations
Old programming languages may not be efficient for handling concurrency
Async programming allows tasks to take turns, making it easier to handle concurrency

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Understanding Swift actors is crucial for managing re-entrancy and interleaving in your code.
Building an optimal authentication service involves utilizing Swift actors to minimize duplicate work and network overhead.
Swift concurrency model utilizes cooperative threading, executors, and actors to create an illusion of single-threadedness and prevent data races.

Unstructured concurrency introduces a different approach to handling asynchronous code compared to structured concurrency like async/await.
When dealing with unstructured concurrency like Tasks, testing can become challenging and may require using XCTestExpectation to handle closure-based asynchronous operations.
To overcome testing challenges with unstructured concurrency, leverage mocks, the defer keyword, and XCTestExpectation to ensure precise test execution.

Combine and async/await can be used together to write robust and maintainable software with modern language features.
Testing @ObservableObject view models, Combine publishers in async methods, and Combine publishers converted to AsyncSequence are key in achieving unit test mastery.
Understanding the interoperation between Combine and async/await is a powerful skill for writing unit tests and marrying the two concurrency approaches.

A distributed system is a collection of components on multiple computers that appear as a single, unified system to users. They are commonly used in database and file systems.
Key characteristics of distributed systems include concurrency, scalability, fault tolerance, and decentralization, enabling efficient operation across multiple machines.
In distributed systems, concepts like fault tolerance, recovery & durability, the CAP theorem, and quorums & consensus are crucial for maintaining reliability, consistency, and coordination among nodes.