Rashi Dhar

Just heard Amazon Prime laid off so many employees, Because they shipped faster and automated things with AI. And this just scares me but at the same time I feel there’s huge gap between how managerial side is thinking about AI v/s the Reality of AI: Because: 1. AI writes the code but fails at quality, I need to spend time to get good final version 2. Doing LLDs with AI is too hectic, it skips what it doesn’t understand or comes up with solution which isn’t optimised 3. HLDs aren’t even AIs cup of tea yet This just about coding part, there’s lot of more that we do as engineers. So, is it managers illusion which is taking away job ? But whatever it is someone needs to wake these companies to reality and show how important engineers are for the company. Done with listening AI can replace engineers.

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Right now, it’s not just H1B holders under pressure, the ones really feeling the burn are 𝘀𝘁𝘂𝗱𝗲𝗻𝘁𝘀, 𝗳𝗿𝗲𝘀𝗵 𝗴𝗿𝗮𝗱𝘂𝗮𝘁𝗲𝘀 𝗮𝗻𝗱 𝗮𝗹𝗹 𝘁𝗵𝗲𝘀𝗲 𝗨𝗦 𝗳𝗼𝗹𝗸𝘀 𝗮𝗿𝗲 𝗰𝗼𝗺𝗶𝗻𝗴 𝗯𝗮𝗰𝗸 𝘁𝗼 𝗲𝗮𝘁 𝗜𝗻𝗱𝗶𝗮𝗻 𝗜𝗧 𝗷𝗼𝗯𝘀. So, the bigger hit is actually on:  • Students studying here in US with crazy loan amounts on their head.  • Folks who graduated but still don’t have a job. Many students are already stressed about getting a job, and now this adds on. Dealing with rent, family pressure, loan interest, and living expenses all at once, so just imagine. And don’t think this stops in the US, it’s gonna bounce back to India too. The first impact? 𝗜𝗻𝗱𝗶𝗮𝗻 𝗜𝗧. Bcz the people heading back from the US will be looking at those jobs, and let’s be real, they already have an edge. They’ve worked with 𝗠𝗔𝗔𝗡𝗚/𝗙𝗔𝗔𝗡𝗚, on 𝗿𝗲𝗮𝗹 𝗽𝗿𝗼𝗱𝘂𝗰𝘁-𝗯𝗮𝘀𝗲𝗱 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀, working on 𝗹𝗮𝘁𝗲𝘀𝘁 𝘁𝗲𝗰𝗵 𝗹𝗶𝗸𝗲 𝗔𝗜/𝗠𝗟. Sure, India has a lot of potential and a big gap to fill in tech. But the real question is, 𝗮𝗿𝗲 𝘄𝗲 𝗿𝗲𝗮𝗱𝘆 𝘁𝗼 𝘁𝗮𝗸𝗲 𝗶𝗻 𝘁𝗵𝗶𝘀 𝘄𝗮𝘃𝗲 𝗼𝗳 𝗿𝗲𝘁𝘂𝗿𝗻𝗲𝗲𝘀? Truth is, 𝘁𝗵𝗲𝘆’𝗹𝗹 𝗲𝗮𝘀𝗶𝗹𝘆 𝗼𝘂𝘁𝗰𝗼𝗺𝗽𝗲𝘁𝗲 𝗮 𝗹𝗼𝘁 𝗼𝗳 𝘁𝗵𝗲 𝗲𝘅𝗶𝘀𝘁𝗶𝗻𝗴 𝘁𝗮𝗹𝗲𝗻𝘁 𝗶𝗻 𝗜𝗻𝗱𝗶𝗮. Why? Bcz most niche technologies emerge first in the US and only later get outsourced to Indian firms like Infosys or TCS. 𝗦𝗼𝗺𝗲 𝗵𝗮𝗿𝘀𝗵 𝗿𝗲𝗮𝗹𝗶𝘁𝗶𝗲𝘀:  • 𝗗𝗼𝗻’𝘁 𝗿𝘂𝘀𝗵 𝘁𝗼 𝘁𝗵𝗲 𝗨𝗦 𝗿𝗶𝗴𝗵𝘁 𝗻𝗼𝘄. There are already too many students stuck in the job hunt, some graduated, some still studying, and many more on the way.  • The numbers are insane, 𝗼𝘃𝗲𝗿 𝟭.𝟭𝟮 𝗺𝗶𝗹𝗹𝗶𝗼𝗻 international students in the US (2023-24, record high), 𝟯𝟯𝟭𝗸+ from India—𝟮𝟯% jump in a year. Another report puts Indians at 𝟰𝟮𝟬𝗸 (𝟮𝟳% of all students). Massive pressure on the market.  • A big reason? 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝗶𝗲𝘀 𝘄𝗮𝗶𝘃𝗲𝗱 𝗚𝗥𝗘 𝗮𝗻𝗱 𝗧𝗢𝗘𝗙𝗟/𝗜𝗘𝗟𝗧𝗦 during covid, which made it way easier for people to get in US, even if they weren’t really ready for the competitive job market here.  • On top of that, 𝘀𝗶𝗻𝗰𝗲 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 𝗯𝗹𝗲𝘄 𝘂𝗽 𝗶𝗻 𝟮𝟬𝟮𝟯, 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗲𝘃𝗲𝗿𝘆𝘄𝗵𝗲𝗿𝗲 𝗰𝗵𝗮𝗻𝗴𝗲𝗱 𝗰𝗼𝘂𝗿𝘀𝗲 𝗮𝗻𝗱 𝘄𝗲𝗻𝘁 𝗮𝗹𝗹-𝗶𝗻 𝗼𝗻 𝗔𝗜. Now tell me, 𝗵𝗼𝘄 𝗺𝗮𝗻𝘆 𝘂𝗻𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝗶𝗲𝘀 𝗶𝗻 𝗜𝗻𝗱𝗶𝗮 𝗮𝗿𝗲 𝘀𝗲𝗿𝗶𝗼𝘂𝘀𝗹𝘆 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗔𝗜/𝗠𝗟-𝘀𝗸𝗶𝗹𝗹𝗲𝗱 𝗴𝗿𝗮𝗱𝘀? And even if they are, 𝗵𝗼𝘄 𝗺𝗮𝗻𝘆 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗮𝗿𝗲 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝗼𝗻 𝗻𝗶𝗰𝗵𝗲 𝗔𝗜 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗶𝗻 𝗵𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲, 𝗳𝗶𝗻𝗮𝗻𝗰𝗲, 𝗼𝗿 𝗼𝘁𝗵𝗲𝗿 𝘀𝗲𝗰𝘁𝗼𝗿𝘀 on their own? Not many. So yeah, 𝘁𝗵𝗶𝘀 𝘄𝗮𝘃𝗲 𝗶𝘀 𝗴𝗼𝗶𝗻𝗴 𝘁𝗼 𝗵𝗶𝘁 𝗜𝗻𝗱𝗶𝗮𝗻 𝗜𝗧 𝘁𝗵𝗲 𝗵𝗮𝗿𝗱𝗲𝘀𝘁. And if anyone in India is feeling happy about US layoffs or immigration issues, 𝗷𝘂𝘀𝘁 𝗯𝗲 𝗰𝗮𝗿𝗲𝗳𝘂𝗹, 𝗯𝗰𝘇 𝗶𝗳 𝘁𝗵𝗲𝘀𝗲 𝗨𝗦 𝗳𝗼𝗹𝗸𝘀 𝗮𝗿𝗲 𝗰𝗼𝗺𝗶𝗻𝗴 𝗯𝗮𝗰𝗸... 𝗮𝗻𝗱 𝘁𝗵𝗲𝘆’𝗿𝗲 𝗰𝗼𝗺𝗶𝗻𝗴 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗷𝗼𝗯𝘀.

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$100,000 for an H-1B visa. What does this mean for Indians? Every Engineer dreams of studying in the US and secure a job in the Silicon Valley. But Trump’s new H-1B fee changes that. Students may be forced to return earlier than planned because companies will now think twice before sponsoring visas. But you don’t need to go to US to earn in dollars. With the right skills and network you can earn in dollars while living in India. And this H-1B visa fee might change the hiring process in MNC’s too. Remote jobs will now get a spotlight. So here are some of the best portals where Indian engineers can land US remote jobs (links to all portals are in comments) ✅ Flexiple: Curated freelance network for top engineers and designers. ✅ Turing (dot) com: AI-powered platform that matches Indian developers with US companies. ✅ We Work Remotely: One of the oldest and most trusted boards for global remote roles. ✅ AngelList Talent (Wellfound): Startup-first hiring platform, many remote roles listed by US founders. ✅ RemoteOK: Job board focused on tech and product roles across geographies. ✅ Contra: Great for independent creators, developers, and freelancers targeting US startups. In 2025, opportunity isn’t tied to one country. It follows skills that prove their worth anywhere.

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If you are an SF engineer at $150K, you are likely Level I. Your H‑1B chances just dropped by 48%. The new H‑1B Cap Weighted Selection Final Rule is here. Random lottery is gone. Wage‑weighted selection is in. Data. Under the old system, everyone sat at about 35% odds. 118K out of 336K registrations in FY26. No advantage for higher wage levels. The new rule rewrites those odds: → Level I: 15.29% → Level II: 30.58% → Level III: 45.87% → Level IV: 61.16% For Level I, odds drop nearly in half. For Level IV, odds more than double. A $150K SF engineer is often Level I on OES. → Early career people, Lower salary but important roles, they get hit the hardest here. There is more. Registrations must now include: → OEWS wage level → SOC code → Work location Later the petition must prove that wage level was correct on the day of registration. USCIS can deny, revoke, or block amendments if it looks like you changed terms to game the lottery. The logic from the government is clear. → Higher pay, higher odds. → Your economic value now shapes your chance to stay. If you are an employer, start planning before the next window. If you are a worker, learn your wage level and study other paths, not only H‑1B. I will keep sharing data as more details come out. Hi! My name is Sumit Gupta, I share data, AI, immigration updates and make you a smarter immigrant!

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Ever wonder why we never went back to waving at street cabs after using Uber, Lyft or Ola - even with surge pricing, delays, or app glitches? That’s Delta 4 Theory, and discovering it through Kunal Shah completely rewired how I think about product value. While diving deep on ride-hailing vs traditional cabs, I realized something simple but powerful: great products don’t just improve experiences - they make the old way feel impossible. Delta 4 gave me the clarity to cut through noisy hypotheses and focus on what truly drives product-market fit: irreversibility, deep user benefit, and even loyalty in the face of flaws. If your users can’t imagine life before your product, you’re onto something. #PMF #productmanagement #innovation #Leadership #SaaS

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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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Two major news from US this week that Indians should care about: 1/ Amazon’s 14000 layoffs (which is not because AI is efficient but because its costly) 2/ Walmart putting a pause on all H1B sponsored hiring While the $100K H1B fee update exempted all students in US from paying $100K for the H1B lottery, things are still turbulent. Layoffs are not cooling off and companies are still wary of sponsoring H1B visas. This is why many people who went to US in last 2-3 years (before policies worsened) are feeling stuck there. They took huge loans with a bet that is not playing out the way they hoped. Does it mean their US education is going to be a waste? Not really. You can still leverage it to build a global career but you need to be smart about how and where to use it. That is what I discuss in today’s YouTube video - 7 concrete strategies for Indians feeling stuck in the US! What they can do to safeguard their interests? (YouTube link for full video mentioned in the first comment) PS. Join 75,000 people who watch our weekly long analysis and tips on the YT channel (Scholar Strategy TV) if you haven't 🙏 #scholarstrategy #24northstar

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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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At 3 AM on a Wednesday, Amazon needed me. I wasn’t even on that team anymore. Back when I was an SDE at Amazon, a Sev-2 had hit one of our services. Many customers were impacted. The team had been chasing it for hours. Still, they called me. I rolled out of bed, opened my laptop, and joined the bridge. The tricky part was that the fault started a week earlier. A code change had shipped behind a flag, traffic ramped slowly, and the failure signature was noisy. Logs were chatty in all the wrong places and silent where we needed them most.Dashboards showed errors, but no clear line to the root. We did unglamorous things. We wrote a minute-by-minute timeline. We diffed deployments, configs, and flags across regions. We checked queue backlogs and retry storms. We sampled logs with new filters. We bisected the traffic ramp. We rolled back the smallest safe thing first. Then we added one log, in one hot path, and the picture snapped into focus. By sunrise, we had the root cause and a fix. The impact was contained. Customers recovered. What stayed with me was not the fix. It was why they called. They did not call because I am smarter. They called because, over months, I had done the boring work. I wrote runbooks when no one asked. I paired with juniors on their first on-call. I shared context so others could make decisions without me. I showed up when it was not my ticket. Trust is not built in one night. It is built in all the small days that come before it. Ownership is simple. If it affects your customer, it is your problem. Org charts are for payroll, not for 3 AM. If you are early in your career, remember this: → Document the weird corners when you discover them. → Add one useful log where it hurts, not twenty where it is easy. → Keep a living checklist for rollback, not a static wiki page. → Teach one person the thing you just learned. → Read your dashboards when things are calm, not only when they are red. At 3 AM, you will not rise to the occasion. You will fall to the level of your habits. Build the right ones while the house is quiet. That’s how you earn trust that lasts beyond teams and ownership that outlives org charts. A few days later, this moment turned into an accolade from the team..  a reminder that trust is built long before it’s tested :)

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