🚀🔍 JavaScript Interview Question Alert! 🌟 💡 Challenge: Take a look at this code snippet: 🧐 Can you predict the output for each `console.log` statement? Dive into the world of object property access and share your insights! 💬🚀 #JavaScriptInterview #100DaysOfCode

God bless the one who invented Ctrl/Cmd + Z

The lesson: Great systems don’t guess. They observe, learn, and evolve. Where else do you think “adaptive optimization” could unlock huge performance wins? 👇 — End 🧵 #buildinginpublic #javascriptlearning

That balance is why JIT changed modern computing. Your code doesn’t need to start optimized — it just needs a chance to adapt.

But for anything long-running — servers, browsers, mobile runtimes, real-time apps — JIT gives you a rare combo: speed + flexibility + intelligence.

But JIT isn’t perfect. There is a warm-up period before peak speed. And it uses more memory to store profiles + optimized code. For tiny scripts or short-lived processes? Interpretation or AOT can still win.

Real-world examples? Everywhere: - JVM → enterprise apps at global scale - Chrome V8 → JavaScript engine behind fast websites - Android Runtime → makes apps snappy without killing battery If your device feels fast… JIT is probably working behind the scenes.

And here’s where JIT gets truly powerful: adaptive optimization. It doesn’t just compile once. It learns from how your app behaves: - Common branches - Repeated inputs - Data type patterns Then it rewrites the machine code on the fly. Your program literally gets faster as it.

The JIT engine has four core components: 🏁 Interpreter → fast startup 👀 Profiler → watches code behavior ⚙️ JIT compiler → optimizes only what matters 🚀 Compiled code → replaces the slow version Efficiency through focus.

Think of it like this 👇 Interpretation = translate every line every time AOT = translate the entire book before reading JIT = translate as you go + remember the repeated sections for instant recall That’s why JIT approaches AOT performance without AOT rigidity.

Once something is a hot spot, JIT converts it into native machine code—the stuff your CPU loves to execute instantly. Result: - No delay at launch - Huge speed boost over time - Smarter optimization than static compilers

Here’s how it works: When your program starts, it runs through an interpreter. Meanwhile, a profiler quietly tracks what parts run the most. Those frequently executed chunks? They become hot spots 🔥.

JIT = Just-in-Time compilation. It doesn’t compile everything up front. Instead, it waits… watches… and optimizes code during execution ⚡ Like a translator who starts slow—then memorizes the repeated sentences.

Interpreters are flexible. Compilers are fast. JIT said: why not both? Here’s how modern apps get speed and smarts 🧵

I should be sleeping right now like the rest but here I am.

Why am I getting such a privilege?

The lesson: optimization often beats brute force. Where else have you seen small tweaks unlock massive scale gains? — End 🧵

✨ Takeaway: Scaling isn’t always brute force. Swiggy saved millions with smart math: - Observe real usage - Cluster intelligently - Optimize without hurting UX Sometimes, a few pixels of compromise = massive wins.

🔄 But it’s not “set it & forget it.” - New devices = new widths - New video formats = new requirements Swiggy reruns clustering periodically to keep buckets optimal.

🚀 Results: - Fewer video transformations → big cost savings 💸 - Higher CDN cache hits → faster loads ⚡ - Quality unaffected → users didn’t notice 👏

📲 Enter the Bucketizer inside the app: - Takes the original request - Maps it to nearest bucket width via MediaBucketMap - Requests video with this “bucketized” URL Now DAM + CDN only deal with 8 widths instead of hundreds 🔥