Synchronous Operations
📌 Synchronous execution involves a process having a default single thread that performs work; when this thread makes a request (e.g., network call, disk read), it becomes blocked while waiting for the operation to complete.
💿 When a thread is blocked waiting for I/O operations (like disk access handled by the disk controller), the entire application code execution is halted, even if no actual computation is being done by the thread.
🛑 This blocking state prevents the execution of subsequent code or the servicing of user interface (UI) interactions, making the application unresponsive.

Multi-threading
⚠️ A thread is an execution unit within a process, allowing multiple tasks to run concurrently, sharing the process's resources.
💥 The primary challenge with multi-threading is resource sharing, which can lead to issues like locking and race conditions if thread safety is not rigorously maintained.
🔒 Achieving thread safety requires mechanisms like mutexes to ensure only one thread accesses shared process resources at a time, which the speaker notes is very difficult to implement correctly.

Asynchronous Execution
💡 Asynchronous execution (popularized in Node.js) uses a single-threaded, non-blocking model where I/O requests are sent, and the thread immediately continues executing other code instead of waiting.
📞 When an I/O operation finishes, a designated function, known as a callback, is triggered to handle the result, managed by an event loop.
✨ The evolution of asynchronous code involved moving from complex callback chains to more readable structures like Promises and, finally, the `async/await` syntax, which makes asynchronous code appear synchronous.

Multi-processing
⚙️ Multi-processing involves spinning up entirely unique processes, each with its own dedicated memory and resources, rather than sharing resources like threads do within one process.
↔️ Communication between these separate processes is achieved using Inter-Process Communication (IPC) methods, such as using a centralized database like Redis or standard network sockets (e.g., localhost ports).
💪 This model is highly effective for dividing large, parallelizable tasks, such as brute-forcing a password or reversing an MD5 hash, as tasks can be distributed across multiple independent processes, potentially scaling across multiple machines.

Key Points & Insights
➡️ Synchronous code blocks execution entirely while waiting for I/O, contrasting sharply with the non-blocking nature of asynchronous code.
➡️ Multi-threading offers concurrency but introduces high complexity due to the need for thread safety when managing shared resources.
➡️ Multi-processing is the preferred approach for massively parallel tasks by ensuring complete resource isolation between execution units.

📸 Video summarized with SummaryTube.com on Nov 13, 2025, 03:31 UTC