I'm a Senior SWE at Microsoft and a few years ago I failed an Uber interview on an async concurrency control problem. The question was implementing asyncMapLimit - process an array with a maximum number of concurrent async operations while preserving result order.

I understood the algorithmic challenge: queue management, concurrency limiting, result ordering. But I struggled with JavaScript-specific patterns: how closure capture affects callback timing, when the event loop can cause race conditions and why naive Promise-based solutions create memory pressure.

That failure stuck with me. After conducting JavaScript interviews myself, I realized this pattern is everywhere. Brilliant developers hitting invisible walls not because they lack algorithmic thinking, but because they don't understand how JavaScript's runtime changes implementation strategies.

    const processNext = () => {
        if (currentIndex < collection.length) {
            const index = currentIndex++;  // Shared state mutation
            inProgress++;
            
            eachItemFn(collection[index], itemResult => {
                result[index] = itemResult;
                inProgress--;
                // When exactly does this callback execute?
                // What happens if callbacks finish out of order?
                if (currentIndex === collection.length && inProgress === 0) {
                    allDone(result);
                } else processNext();
            });
        }
    }

The edge cases around callback timing, closure capture and memory management require deep JavaScript knowledge that generic algorithm prep doesn't cover. So I built Memoized to help developers avoid the same trap I fell into.

Memoized teaches algorithms through JavaScript's lens rather than as translations from Python/Java. Built with Next.js, TypeScript and Prisma, it covers 450+ problems focusing on JavaScript-specific patterns: when Map vs {} vs WeakMap matters for performance, prototype chain gotchas in hash table implementations, call stack optimization for recursive algorithms, async-aware algorithm implementations…

Uses MDX for interactive lessons and provides JavaScript-specific feedback. Links to LeetCode problems but adds the missing "why JavaScript developers struggle with this" layer.

Try it: https://www.memoized.io (entire first sections are free)

Students or those facing financial difficulties get free access, just reach out.

What JavaScript async patterns have surprised you in algorithmic contexts? Would this have helped you in interviews?