Palak Anmol

Grateful to be part of AWS She Builds Tech as an AWS Insights Speaker. ☁️ Had the opportunity to contribute an article on designing an end-to-end serverless data pipeline on AWS - covering the journey from raw data to actionable insights using services like S3, Glue DataBrew, SageMaker, and QuickSight. Do give it a read if you’re exploring data engineering, machine learning, or serverless analytics on AWS. #AWSSheBuildsTech #WomenInTech #AWSCommunity #DataEngineering #MachineLearning #Serverless #Cloud AWS UG for Women In Tech India

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🚀 India just quietly triggered one of the biggest tailwinds for AI Product Careers - and many PMs might not have noticed yet. In the last two weeks, three announcements landed that will directly accelerate AI product building in India: 1) ₹1 Lakh Crore DeepTech RDI Fund (Govt. of India) A national ₹1,00,000 crore innovation fund will support AI, robotics, quantum & advanced tech - including risk capital at early stages. This means a new wave of AI-first products will get funded, and they’ll need PMs who can convert technology into usable customer value. 2) Karnataka Startup Policy 2025–2030 (₹518 Cr for 25,000 Startups) The policy aims to support 25,000 tech startups (10,000 outside Bengaluru) with priority on AI, blockchain, space tech, and quantum. This signals more 0→1 product roles across India -not just in Bangalore. 3) Nvidia joins India Deep Tech Alliance Backers now include Nvidia + venture partners to catalyse Indian deep-tech ecosystems with not just capital but also computational and GTM support. 💡 What this means: India is about to over-fund AI ideas and under-supply PMs who can turn those ideas into real products. If you’re early in your career, this is the moment to: pick a problem space you care about understand where AI creates 10× value over status quo build & ship velocity instead of perfection The next generation of strong PMs won’t be defined by: “how well they execute Jira” but “how well they convert tech → customer value → revenue” That window just opened. #AIProductManagement #Productmanagementcareers #AI #DeepTech

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You are in a system design interview at Stripe, interviewing for a senior role, and the Senior Engineer interviewing looks at you and goes: “How does a browser know your credit card is fake before you hit 'Submit'?” You might mistake this for a simple frontend validation problem.If so, then its a definite no-hire. It is a layered validation problem in which cheap, deterministic checks occur as early as possible, and expensive, high-risk checks are pushed to the backend. Btw, if you’re preparing for system design interviews, check out our AI Tutor: https://lnkd.in/gcWfR7jW You can: - voice chat about your questions in real-time - get feedback in real-time and improve with these sessions. - learn concepts, practice HLD questions even if you're a complete beginner Back to the topic, here is how I would break it down. [1] First, the browser is not checking all possible card numbers This is the part where most junior engineers fumble, Yes, there are billions of possible 16-digit combinations. But the first check the client does isn’t to ask if your credit card is legit. It asks much simpler questions like: – does this number match a known card issuer pattern – does the length look valid for that issuer – does it pass the checksum rule [2] Issuer and BIN checks happen early The first few digits of a card already tell you a lot. Those prefixes, often called BIN or IIN ranges, identify the network and issuer family. For example, some prefixes map to Visa, some to Mastercard, some to Amex. So when you type the first few digits, the client can already say: – this looks like Visa – expected length is probably 16 – CVV and expiry formatting should follow this path If the prefix is impossible or the length is wrong, the client can reject immediately. [3] Then comes the Luhn check This is the classic checksum used by most card networks. Very simplified: – starting from the right, double every second digit – if doubling gives you 2 digits, subtract 9 – add everything – total should be divisible by 10 You might be like, “ Wait a minute..,Why do this on the client side” Because it’s a quick, low-cost way to filter out obvious invalid numbers before they go up the chain. That gives you 3 wins: – better UX because users get immediate feedback – lower backend load because bad inputs never hit your servers – cleaner fraud and auth pipelines because you do not waste expensive checks on obviously invalid numbers [4] But this is still not  authorization Passing Luhn does not mean the card is . – It does not mean the account is open. – It does not mean the customer has funds. – It does not mean the transaction is safe. The backend still has to do the  work: – tokenization so raw PAN is not handled everywhere – payment processor / network authorization – AVS, CVC, expiry validation – fraud scoring – velocity checks – risk rules – 3DS or step-up authentication where needed

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System Design Part 2 Continuing my System Design Blog Series, this post is a deep dive into the Low-Level Design of WhatsApp. 📱 From handling 2 billion daily messages to ensuring high availability, we’ll walk step by step through how WhatsApp is architected at scale. 🚀 High-level estimate Here is our high-level estimate: TypeEstimateDaily active users: (DAU)50 million Requests per second (RPS) : 24K/s Storage (per day) : ~10.2 TB Storage (10 years) : ~38 PB Bandwidth : ~120 MB/s

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For every Sr Engineer / Principal Engineer you see who seems to have everything figured out ↳ Was a person just as scared on their first day when they joined. For every VP / Director of Engineering you see in an org today ↳ Was a person who once worried they would not survive their probation. For every confident speaker you see presenting roadmaps on stage ↳ Was a person who once shook reading out their own slides in a small meeting. We seem to forget that we are not the only ones freaking out. Everyone is. Yes, at different levels. At different stakes. At different risk factors. But the feeling is the same. You are not behind because you feel scared. You are just earlier in the part of the journey that people don’t post about.

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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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When Claude Code Joined Our Team in the Middle of a Tech Scavenger Hunt Yesterday, our randomly assembled team of four engineers joined an intense tech scavenger hunt at InMobi Encore2025 - packed with coding challenges and technical riddles. The team: Me (Staff Engineer – Frontend) An SDE2 – ML An SDE3 – Backend Another Staff Engineer – Backend One puzzle stopped us cold: two giant parquet datasets (~750K rows each) hiding a riddle about bids, floors, installs, and a “final pattern” buried deep inside. My teammates? Instantly spinning up Databricks, notebooks, pandas. Me? No relevant setup. No DS tools. Just sitting there, watching the clock tick, feeling like dead weight. 💡 The Lightbulb Moment "Why not try Claude Code?" I dropped the datasets and riddle in with few more lines of instructions. And while we were still stuck on the first step, Claude tore through: Filtering down to iOS/Android Checking bid floors Finding the winning ads (render + click + install) Merging on impression_id Revealing a Fibonacci sequence in time_to_install Answer: 34. “Guys, just enter 34.” Suspicious stares. A pause. Then… Correct! That single AI-powered leap saved us 10-15 minutes and flipped the game. From that point on, Claude became our fifth teammate. Takeaways: In tech, the boldest move is often trying what looks unconventional AI can be a genuine partner in problem-solving Resourcefulness > perfect setup And sometimes, success in uncharted territory comes from being willing to try what others dismiss I may not have written a line of pandas, but being open to AI meant I could unlock value my team didn’t expect. PS: We didn’t win the contest, but we came very close — only to be tripped up in the last round that required us to actually run around the office for the answer. Turns out, AI has its limits 😉 #AI #Engineering #ProblemSolving #Teamwork

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To everyone on H-1B feeling anxious right now I know the new order has created a lot of worry. But please — don’t panic. Many news articles and LinkedIn posts are jumping straight to conclusions without really digging into the actual language. The executive order has a lot of “in-between the lines” details, and it will take time for lawyers and experts to interpret what it truly means. For now, many are advising not to travel outside the U.S. until there’s more clarity. And to those posting random takes without expertise — please don’t spread unnecessary fear. It doesn’t help anyone Let’s hold off on reacting to every headline and wait for trusted updates from legal experts. We’ll all get a clearer picture soon. Update : The White House Press Secretary has now clarified a few key points: 1️⃣ This is not an annual fee — it’s a one-time fee that applies only to the petition. 2️⃣ Current H-1B holders outside the U.S. will not be charged $100,000 to re-enter. Their ability to travel remains unchanged. 3️⃣ This applies only to new visas in the next lottery cycle, not renewals or current visa holders. This should help clear some of the confusion. Still, let’s keep relying on official sources and legal experts instead of jumping to conclusions. #h1b #executiveorder

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Last two years I saw three kinds of AI oriented junior and less 3 years experienced software engineers in the market. So many students also thinking same pattern. 1. AI can do everything - These guys actually believe AI will do code for them even though they don't understand coding or programming that will not be a problem and they actually try different AI tools but can not solve complex things properly. ( This is a disaster group for themselves and company) 2. AI replace us very soon - These guys actually upset and depressed by thinking their features in software development career. They think everywhere is layoff so what will happen to us are we really in right career or need to switch ( This is a confused group can not trust themselves, not AI and not even career) 3. AI, o another tool a new challenge - These guys keep learning and using AI as their pet or helping tools, guys are struggling but only this group will survive ( This is smart group who takes challenges as opportunity ) So my friend which one is you? #AI #VibeCoding #SoftwareEngineering #Career #Learning #Challenge #Grow

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Do not over-rely on LLMs to write your code. Seriously. Relying too much on instant answers is the quickest way to forget how to think critically and solve problems on your own. Write the code yourself. Use LLMs to help review, debug or look up unfamiliar APIs. Not as a crutch for every task. Read the docs. Read books. Build things. Break things. Master the fundamentals, there are no real shortcuts. Happy coding 👨‍💻 #softwareengineering #llm

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Current Hiring Trends and Challenges for H-1B Visa Holders In today’s job market, many companies in the U.S. have increasingly narrowed their hiring focus to prioritize U.S. citizens and green card holders. This shift is especially noticeable in mid-to-large enterprises. Additionally, economic uncertainty, budget constraints, and evolving immigration policies have made many employers hesitant to take on the perceived administrative burden or risk associated with sponsoring foreign workers. As a result, qualified professionals on H-1B visas are frequently overlooked, with their resumes not even being considered despite strong skills and experience. What Can H-1B Professionals Do?

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🔥 “𝗬𝗼𝘂 𝗱𝗼𝗻’𝘁 𝗼𝘄𝗻 𝗿𝗲𝘃𝗲𝗻𝘂𝗲.” That one line almost broke me early in my PM career. I developed a '𝗰𝘂𝘀𝘁𝗼𝗺 𝗔𝗜/𝗠𝗟 𝘀𝘆𝘀𝘁𝗲𝗺 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗽𝗿𝗼𝗴𝗿𝗮𝗺' which needed vendor and engineering team and cross-functional owner collaboration. On paper, I was unblocking bottlenecks, driving execution, and pushing delivery forward.The leadership delivered a direct message about operations work when it was time to demonstrate impact by stating “𝗢𝗽𝘀 𝘄𝗼𝗿𝗸 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗺𝗼𝘃𝗲 𝘁𝗵𝗲 𝗻𝗲𝗲𝗱𝗹𝗲.” But one project turned that thinking on its head. 💡 Instead of stopping at technical fixes, I partnered with 𝙛𝙞𝙣𝙖𝙣𝙘𝙚.We dug into the ripple effects:  1. The vendor delays resulted in the company missing the holiday launch window.  2. The optimization of models directly affects the BOM costs which results in higher costs.  3. The rework cycles operated in the background to exhaust budget allocations. The numbers were eye-opening: The company achieved an 18% decrease in rework expenses through process improvements at the vendor level.  • 3 weeks shaved off time-to-market, securing a seasonal launch  • The execution system produces more than $20M in revenue because it links operational activities to business results. That project didn’t just deliver a technical win — it reframed how leadership viewed product execution. Suddenly, “ops” wasn’t background noise; it was a 𝗽𝗿𝗼𝗳𝗶𝘁 𝗹𝗲𝘃𝗲𝗿.The recognition (and promotion) I received came from showing how 𝗰𝘂𝘀𝘁𝗼𝗺 𝗔𝗜/𝗠𝗟 𝘀𝘆𝘀𝘁𝗲𝗺 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 = 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘃𝗮𝗹𝘂𝗲. The real lesson? 👉PMs and TPMs don’t just own delivery; we engineer ROI through trust, partnerships, and execution that protects revenue and margin. I will always be thankful to my coach Shilpa Kulshrestha-Murdering Mediocrity who made me look at delivery metrics from a business perspective. PMs, TPMs and tech leaders who read this should investigate if they have ever felt invisible in terms of business impact. Connect process → efficiency → revenue. That’s where the hidden ROI lives. I have created slots on 𝗧𝗼𝗽𝗺𝗮𝘁𝗲 to assist others in creating their own impact story by converting “behind-the-scenes work” into career-defining ROI. Let’s connect, link: https://lnkd.in/gQN3KvYn

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Sheetal Singh & I have been paring for over a month on some Rust OSS projects: our first submission to the community is Parseltongue - came out of the need for code-noobs like me to understand code at the abstract level of LLDs - as Interface Signature Graphs - which expresses the overall structure of code in just 5% of total code LOC / tokens - or even lesser - so you can reason with the codebase in far lesser context window every time you update the code, within a few milli-seconds you will be able to see the updated ISG as a visualization and can also find the blast radius of the change you intend so you can run it as - a live code daemon - a gitingest txt codebase analyzer (we did it for tokio library) our work is primary inspired by Shreyas Doshi's Product Sense course - which both of us attended and which nudged us to think for ourselves on problems seeking a differentiated experience for our users via the tools / libraries this is an initial draft & we will be applying to delta residency with this idea seek feedback from folks on what we could improve further link to github repo in first comment our vision with Parseltongue: use Interface Signature Graphs to reason large codebases with LLMs in a faster & more accurate manner onbv 🙏

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Utilizing advanced machine learning techniques, I analyzed data from Visa applicants to develop a predictive model aimed at streamlining the visa approval process. This model identifies key factors that significantly influence Visa status, allowing for tailored recommendations on which applicants should be certified or denied. The integration of data-driven insights enhances decision-making efficiency in visa approvals.

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The recent proposal to impose a $100,000 fee on companies for every H1B employee is deeply worrying. For companies, this creates a massive financial burden. It’s not just about new hires — even retaining existing H1B employees could become unsustainable. This will likely push firms to cut back on international talent, no matter how skilled. For Indian engineers, especially those planning to pursue a Master’s in the US, this raises serious questions. Many invest huge amounts of money and years of effort with the hope of building a career in America. But if companies are reluctant to sponsor H1Bs due to such heavy costs, the opportunities for graduates may shrink drastically. This policy doesn’t just affect individuals — it impacts innovation, startups, and industries that rely on global talent. It risks closing the very doors that once made the US the hub of opportunity. #trump #hlb

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Self-code review checklist I wish I had when I was a Jr. Engineer at Amazon: ○ Before I even run the code: – Do I fully understand the input, output, and constraints? –Did I rephrase the problem in my own words? –Did I list edge cases before touching the keyboard? –Did I pick the simplest working solution first? –Am I optimizing only after correctness is proven? ○ While writing the logic: –Are my variable and function names clear without comments? –Is each function solving only one responsibility? –Did I remove hard coded magic numbers? –Are my conditions readable at first glance? –Am I avoiding deep nesting where early returns work better? ○ After the first successful run: –Did I test empty input, single element, and max constraints? –Did I manually test worst case scenarios? –Did I validate both success and failure paths? –Did I remove all commented dead code? –Did I remove all temporary debug logs? ○ Performance and safety checks: –Is any loop unnecessarily nested? –Am I recalculating something that can be cached? –Is there any chance of null pointer or out of bounds access? –Is any blocking call happening on the main thread? –Are all error cases actually handled? ○ Readability and maintainability: –Can another engineer understand this without my explanation? –Are functions short and intention revealing? –Does the file structure feel obvious? –Are my comments explaining why, not just what? –Did I reduce complexity or just rename it? ○ Production readiness: –Are errors logged with enough context to debug later? –Are retries and fallbacks handled where needed? –Are timeouts set for all external calls? –Am I leaking any sensitive data in logs? –Would this survive real world traffic without babysitting? ○ Final gut check: –Would I confidently approve this in a real PR? –Would I be okay debugging this at 2 AM? –Would I understand this code after 6 months? –Would a senior engineer push back on this? –Did I actually improve the code or just rearrange it?

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Amid new H-1B visa curbs getting implemented this year, the major US tech firms-Facebook (Meta), Amazon, Apple, Microsoft, Netflix, and Google (Alphabet) closed 2025, collectively hiring over 32,000 new employees in India. FAAMNG companies reported an 18% YoY uptick in hiring in specialised roles in India, with another 3,000-5,000 active job openings. https://lnkd.in/gynKeext

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One year ago, I left the Bay Area, wrapped up nearly 8 years on US work visas (TN + H1B), and moved back home to Toronto. Living in the states as a skilled immigrant came with a constant hum of uncertainty around visas, but also lead to incredible opportunities early in my career that I don’t know if I would have found elsewhere. Our motivation for moving was primarily for personal reasons, but we asked ourselves many questions before pulling the trigger: > Can we find fulfillment and growth in our careers beyond the first role when we land? > What will our financial picture look like if we made $X? And spent $Y? > What does life outside of work look like? With family? And friends? > Is {healthcare|rental market|the TTC} really as bad as people say it is? For Canadian tech expats who are asking themselves similar questions especially in light of the recent announcements related to H1B, I’m happy to share my experience navigating the change and maybe some tips I learned from the whole process to make it less daunting 🇨🇦 #H1BNews #TorontoTech #LifeInToronto #Canada #CanadiansAbroad #H1B #TechToronto #Engineering

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