Bharat Simha Reddy Rikkala

You are in a system design interview at Amazon for an SDE-3 role and the interviewer has given you the question to Design Netflix. He then asks a follow up to you: How does Netflix knows when to show: Are you still watching? Is it just time-based? If it were time-based, you would see it every time. So what is actually going on under the hood? Both look like a simple pop-up. Underneath, it is a mix of product thinking, client logic, and backend events. Btw, if you’re preparing for system design/coding interviews, check out our mock interview tool. You can use it for free here: https://lnkd.in/gpCn7t2T [1] Start from the product goals The feature is not only about nagging people. - Save bandwidth and CDN cost if the viewer has slept or walked away - Avoid autoplaying potentially sensitive content in an empty room - Protect kids if parents start a show and leave the TV on - Do all this without annoying active binge watchers So the design must answer one question: "When is the user probably not here any more" [2] Naive design - pure timer Simplest idea: - If playback has been running for 2 hours, show Are you still watching - If user clicks "Yes", reset the timer Why this is weak: - Someone can binge 5 episodes in a row and gets interrupted in the middle of an intense scene - Someone can start a show, walk away after 5 minutes, and the platform will keep streaming for the next 2 hours - It ignores how many episodes were auto played, device type, time of day, user habits  [3] Realistic design - session and engagement signals Think in terms of a "watch session" and "engagement events". Signals the client can track: - Play, pause, seek, volume change - Episode finished and next episode auto started - Remote or keyboard input, UI navigation - Screen on or off events from the device - For mobiles: app background or locked state A common heuristic could be: - Count how many episodes have auto played without any user interaction - Track how long it has been since the last button press or navigation - Only trigger the prompt when both are high enough Example rule: - If 3 episodes in a row have auto played - And there has been no interaction for 45 minutes - Then, before starting episode 4, show Are you still watching This feels much smarter: - Active viewers who pause, skip intro, change volume keep resetting the "engagement clock" - Sleeping viewers do not touch anything, so the next episode is blocked by the prompt  [4] Client heavy vs server heavy design You can talk about two design choices. Client first: - All logic runs inside the app on TV, mobile, or web - Client keeps an in memory session model and decides when to show the popup - It still sends telemetry events to backend for analytics and future tuning Pros: - Works even with flaky network - Highly responsive, no extra server round trip Cons: - Logic must be implemented and updated across many platforms - Harder to quickly roll out rule changes Continued ↓

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How I Made Decisions at Amazon In 5.5 years at Amazon, I made thousands of decisions. Some were small. Scope tweaks. Deadline shifts. Meeting structure changes. Some were big. Launch calls. Escalations. Resource tradeoffs. Risk acceptance. And here’s what I learned: Indecision is usually more expensive than a wrong decision. Amazon forced me to build a decision framework…not rely on instinct alone. Here’s how I approached it: 1/ I asked: Is this a one-way door or a two-way door? ↳ A one-way door decision is hard to reverse ↳ Long-term architectural changes ↳ Big resourcing bets ↳ Major customer commitments These required deep dive, multiple perspectives, and slower motion. ↳ A two-way door decision is reversible ↳ Pilot launches ↳ Process changes ↳ Messaging experiments These required speed. Most people treat two-way doors like one-way doors. That’s how velocity dies. 2/ I defined the real risk before acting ↳ What’s the worst-case outcome? ↳ How recoverable is it? ↳ What’s the cost of waiting? Often the cost of delay was greater than the cost of being imperfect 3/ I gathered just enough signal…not perfect clarity ↳ 70% confidence was often enough ↳ The remaining 30% came from moving ↳ Over-analysis was a hidden tax on momentum 4/ I wrote my thinking down ↳ Framing the decision in writing exposed weak assumptions ↳ It also made it easier for others to pressure test it ↳ Writing sharpened judgment 5/ I owned the outcome…even when it missed ↳ If a decision didn’t land, I didn’t blame the inputs ↳ I asked: What did I miss? What pattern did I ignore? ↳ Decisions compound…so does accountability The biggest shift Amazon gave me was this: Decisiveness is a leadership trait. But structured decisiveness is a competitive advantage. 📬 I write weekly about clarity, ownership, and high-stakes execution in The Weekly Sync: 👉 https://lnkd.in/e6qAwEFc What’s one decision you’ve been overthinking that might actually be a two-way door?

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𝗠𝗼𝘀𝘁 𝗶𝗻𝘁𝗲𝗿𝗻𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝘀𝘁𝘂𝗱𝗲𝗻𝘁𝘀 𝘁𝗵𝗶𝗻𝗸 𝗛-𝟭𝗕 𝘀𝗽𝗼𝗻𝘀𝗼𝗿𝘀𝗵𝗶𝗽 𝗰𝗼𝗺𝗲𝘀 𝗳𝗿𝗼𝗺 𝗮 𝗳𝗲𝘄 𝗯𝗶𝗴 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀. Google. Amazon. Microsoft. So everyone applies there. And ends up competing with thousands of other candidates. But that’s not how the market actually works. A large portion of H-1B visas come from 𝘀𝗺𝗮𝗹𝗹𝗲𝗿 𝗮𝗻𝗱 𝗺𝗶𝗱-𝘀𝗶𝘇𝗲𝗱 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 that most students never even look at. That’s where the gap is. It’s not always your resume. It’s often where you’re applying. When everyone targets the same companies, competition becomes extreme by default. The candidates who break through usually do something different. They look where fewer people are looking. 𝗔𝘁 𝗛𝗶𝗱𝗮𝗻𝗶 𝗧𝗲𝗰𝗵, 𝘄𝗲 𝗵𝗲𝗹𝗽 𝗰𝗮𝗻𝗱𝗶𝗱𝗮𝘁𝗲𝘀 𝗶𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝗮𝗻𝗱 𝗿𝗲𝗮𝗰𝗵 𝘁𝗵𝗲𝘀𝗲 𝗼𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝗶𝗲𝘀 𝗶𝗻𝘀𝘁𝗲𝗮𝗱 𝗼𝗳 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝗻𝗴 𝗶𝗻 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗰𝗿𝗼𝘄𝗱𝗲𝗱 𝗰𝗼𝗿𝗻𝗲𝗿 𝗼𝗳 𝘁𝗵𝗲 𝗺𝗮𝗿𝗸𝗲𝘁. Question: Are you mostly applying to big tech companies or exploring smaller employers as well? 👇 #F1 #H1B #OPT #HidaniTech #InternationalStudents #USJobs #Hiring #Freshers #HR #CareerGrowth

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I do not really post a lot about tech stuff, but maybe I can start. Here is a paper I read recently. ROSE: Robust caches for Amazon product search [https://lnkd.in/gPhbgHHQ] Thanks to Arpit Bhayani's paper shelf recommendations. The insights from this are really fascinating for the search world and how Amazon always knows how to find what you are looking for even if you have clumsy fingers like mine. For example if you are looking for shoes, you might type something like: “nike shoe,” “nike shoes,” “nikes shooes.” A normal cache treats these as different, so it gets big and slow, and you miss the cache a lot. Amazon’s ROSE fixes this by caching the intent, not the exact spelling. What ROSE essentially is, is a typo/variant-tolerant cache for product search that groups similar queries into the same “bucket,” so most lookups hit fast without growing memory. It’s deployed in Amazon Search and improved both latency (single-digit ms for most traffic) and business metrics. How this works is even more fascinating. 1. Locality-Sensitive Hashing: Similar queries collide in the same bucket. • Lexical-preserving hashing (character n-grams + minhash) for typos/variants. • Product-type-preserving hashing (weighted minhash) to keep the category intent (e.g., “dishwasher” vs. “dishwasher parts”). 2. Reservoir sampling: caps each bucket so memory stays constant even as queries grow. (Super convenient, and one of the many engineering gotcha's people might miss) 3. Count-based k-selection: avoids pairwise similarity inside buckets; just count collisions across hash tables → near constant-time retrieval. Where we can apply some of these concepts with Gen-AI (which is in many flavors essentially a search problem with extra steps)- Semantic prompt cache: Group paraphrased prompts to reuse an answer/logits/KV cache even when text isn’t identical. Can be helpful in saving your tokens and $. RAG request cache: map similar user questions to one retrieval plan to cut vector searches + reranking cost. [I remember Azure AI search having a RAG cache, but I am not sure how that worked] Tool/agent call cache: Dedupe near-duplicate function/tool invocations (e.g., same API call phrased differently). Eval & feedback loops: Bucket similar generations or errors to reuse critiques/patches. Support/search front-ends to LLMs: Typo-tolerant, intent-stable pre-rewrites before hitting the model, ensure we only use tokens when absolutely needed.

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Big Tech just told H-1B holders — Come back before Sunday or risk $100K per head! 🚨 Trump just announced a $100K annual fee for every H-1B visa. Microsoft, Amazon, JP Morgan have sent internal letters asking all H-1B/H-4 employees outside the U.S. to return before Sept 21, 12:01 AM ET. 💭 What does this mean for Indian talent? -->Cost of hiring H-1B workers will skyrocket. -->Many companies may freeze or slow down H-1B hiring. -->This could lead to a surge in remote work opportunities and offshore roles — hiring Indian developers to work from India instead of relocating them to the U.S. -->Big Tech may challenge this move legally, but in the short term, there’s a lot of uncertainty for those outside the U.S. 🌏 My take: This could be a turning point. Indian IT talent may see more remote, high-paying roles as companies look to avoid massive visa costs — but it might also limit chances for on-site opportunities in the U.S. Day-22 #365days #H1B #RemoteWork #TechIndustry #Immigration

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Hate is the biggest form of flattery. During my time at Amazon, there was almost always someone unhappy with me — a manager, a peer, or a junior engineer. Not because I was difficult, but because I chose to do the right thing instead of taking sides. Early in my career, I tried hard to avoid offending anyone. Over time, I realized this: I’m not paid to keep everyone comfortable. I’m paid to think clearly, act fairly, and do what’s right for the team and the customer. This doesn’t mean ignoring alignment or team chemistry. It means being willing to push back, say no, or even stop something when necessary. If this resonates, here’s what to do: 1️⃣ Optimize for being right, not being liked 2️⃣ Disagree respectfully, but don’t stay silent 3️⃣ Seek consensus without diluting good decisions 4️⃣ Be willing to push back or say no 5️⃣ Accept that doing the right thing will upset someone Real impact rarely comes from playing it safe.

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