Vignesh Narayanan

𝐑𝐞𝐦𝐨𝐭𝐞 𝐰𝐨𝐫𝐤 𝐦𝐢𝐠𝐡𝐭 𝐠𝐞𝐭 𝐚𝐧 𝐮𝐧𝐞𝐱𝐩𝐞𝐜𝐭𝐞𝐝 𝐛𝐨𝐨𝐬𝐭 𝐟𝐫𝐨𝐦 𝐓𝐫𝐮𝐦𝐩'𝐬 𝐇-𝟏𝐁 𝐯𝐢𝐬𝐚 𝐩𝐨𝐥𝐢𝐜𝐲 𝐜𝐡𝐚𝐧𝐠𝐞𝐬 🌐 The proposed 100x increase in H-1B visa fees reaching $100k per worker could fundamentally reshape how tech companies approach talent acquisition. Instead of paying massive fees for on-site international talent, companies might accelerate 𝑟𝑒𝑚𝑜𝑡𝑒 ℎ𝑖𝑟𝑖𝑛𝑔 𝑔𝑙𝑜𝑏𝑎𝑙𝑙𝑦. The Math is Simple — Current H-1B fees: ~$1-2K per application — Proposed fees: ~$100K per worker — Big Tech response: Likely shift toward remote international talent Already validated by numerous startups: https://lnkd.in/ehsBZZXD The irony? A policy intended to protect local jobs might actually democratize access to top-tier tech positions globally. What's your take on this? Do you anticipate the remote-first future? #RemoteWork #DataCareers #TechTrends #GlobalTalent #DataGovernance`

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Top 10 AI news this week: 1. Microsoft’s Maia Chip Delayed – Microsoft postpones its in-house AI chip “Maia” launch to 2026 due to design and staffing issues. (Reuters) 2. AI Avatars of the Deceased – New tools allow families to create lifelike AI avatars of lost loved ones, raising ethical questions. (TIME Magazine) 3. €70M EU AI Factory – Netherlands to host a massive AI research plant, aimed at transforming agriculture, health, and industry. (Reuters) 4. Tesla Hires Henry Kuang – Tesla recruits ex-GM Cruise exec to lead AI division, fueling its robotaxi and autonomy plans. (Reuters) 5. US Targets Chinese AI – New bipartisan bill seeks to ban Chinese AI tools like DeepSeek from federal use over security risks. (AP News) 6. UK Investigates Google AI – Regulators eye Google’s AI Overviews for potential anti-competitive behavior in search results. (The Guardian, NY Post) 7. Microsoft Pushes Balanced AI Regulation – Chief Scientist Eric Horvitz calls for innovation-friendly AI governance over rigid regulation. (The Guardian) 8. AI-Powered Olympics – 2028 Olympics to use AI in athlete training, judging, and broadcasting for smarter sports tech. (Axios) 9. OpenAI Buys Jony Ive’s Startup – OpenAI acquires Apple legend Jony Ive’s hardware firm for $6.5B to design future AI devices. (Wikipedia, The Information) 10. Gemini Replaces Google Assistant – Gemini AI becomes Google’s default assistant from July 7, handling messages, calls & tasks. (Medium Digest) #AInews #technews #ArtificialIntelligence #DrAIAcademy https://lnkd.in/gKtPMZMS

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🎯 A Hard Truth for Tier-3 College Students & Service-Based Engineers Ever wondered why so many career coaching courses are sold by people from #IIT, #NIT, or with logos like #Amazon, #Google, #Microsoft on their resumes? No hate to them – they’ve worked hard to reach there. ✅ But here’s the reality check: They cracked a top-tier college (huge advantage). They got internships at big tech firms in their 2nd or 3rd year (another advantage). Their college placement drives brought those dream companies to their campus. Result? Their first break was a giant leap – not the struggle most Tier-3 grads or service-based engineers face. So, can they truly relate to: 👉 Sending 200 job applications and getting 0 callbacks? 👉 No campus placements for product roles? 👉 Building everything from scratch without alumni network? Probably not. 💡 Here’s the point: If you’re buying a course, ask yourself – ✅ Am I paying for structured learning (good!) ❌ Or am I buying the dream they are selling (dangerous!) Upskill. Network. Build real projects. That’s what will move the needle – not someone else’s success story. What do you think? Do these “brand name” success stories create unrealistic expectations for Tier-3 engineers? Drop your thoughts 👇 --- #tier3 #college #freshers #servicebased #eyeopener #thoughtoftheday Tata Consultancy Services Amazon Google Microsoft Indian Institute of Technology, Delhi Netaji Subhas University of Technology (NSUT, Formerly NSIT) CGC Landran Alumni Association Chandigarh Group of Colleges CHANDIGARH UNIVERSITY

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🚀 Breaking: India is now a global powerhouse in Generative AI! At a recent international seminar in Lucknow, Stephen Ezell (VP of Global Innovation Policy, ITIF) declared that India has officially taken the #2 spot in the world for generative AI implementation, trailing only the U.S.! 🇺🇸 This milestone reflects India's rapidly growing influence in areas like content creation, productivity tools, and advanced AI applications. Ezell also pointed out that India accounts for 25% of global semiconductor industry integration—a key enabler of this transformation . From smart chatbots in fintech disrupting customer support to AI-driven credit scoring and multilingual assistants, India is turning AI from buzzword into real-world value. Fintech firms in Bangalore have slashed support costs, sped up KYC processing, and reconciled hundreds of millions of transactions using GenAI-powered tools . Want the latest AI & tech buzz delivered daily? Join our Telegram channel for curated news: https://lnkd.in/dzCAMw3h Prefer real-time discussions with fellow GenAI explorers? Join the GenAI Forge WhatsApp channel: https://lnkd.in/d3g9KYyQ Or hop into the WhatsApp community group for deep dives & networking: https://lnkd.in/dwUQ5ivT Let’s build the story—AI happening here, happening now. Join us!

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Big News for Indian Tech Pros: The Game is Changing – And We're Here to Navigate It! The recent $100K H1-B visa fee is more than just a policy change; it's a powerful signal. For many Indian IT professionals, the path to global opportunities is evolving, making our own vibrant tech ecosystem more central than ever before. But this isn't a setback. It's an unprecedented opportunity to build incredibly lucrative, impactful, and fulfilling careers right here in India! That's why I'm thrilled to launch my 5-day LinkedIn series: "The 5-Day Blueprint for a Lucrative Tech Career in India." Each day, we'll dive deep into one of the most in-demand, high-paying tech domains that are absolutely thriving in the Indian market. 🚀 Day 1: The AI/ML Revolution - Building the Future, Here and Now! 🚀 If there's one field dominating discussions and driving innovation globally, it's Artificial Intelligence and Machine Learning. And guess what? India is rapidly becoming a global powerhouse for AI innovation and talent. From automating complex processes to developing cutting-edge predictive models and revolutionizing customer experiences, AI/ML is at the heart of every major industry transformation. Indian companies, both startups and giants, are heavily investing, creating an explosion of opportunities for skilled professionals. ✨ Key Roles to Master: 1️⃣ AI/ML Engineer: The architects and builders of intelligent systems. 2️⃣ Data Scientist: The insights generators, turning raw data into strategic decisions. 🛠️ Core Technologies to Own: 1️⃣ Python: The language of choice for AI/ML, with powerful libraries like TensorFlow, PyTorch, and Scikit-learn. 2️⃣ Strong understanding of statistics, linear algebra, and data structures. 3️⃣ Experience with cloud AI services (AWS Sagemaker, Azure ML, Google AI Platform). The demand for these skills in India is not just high; it's exploding. This is where the future is being built, and you can be at the forefront! ❓ What are your thoughts? Do you believe AI/ML will be the most lucrative field in Indian tech in the next 5 years, especially with the shifting global landscape? Share your insights and predictions below! #TechJobsIndia #CareerInTech #IndianIT #FutureOfWork #SkillUp #LinkedInSeries #ITJobs #AIML #ArtificialIntelligence #MachineLearning #DataScience #Python #TensorFlow #IndiaTech #Opportunities"

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Microsoft's H-1B Visa Applications Questioned Amid Mass "AI Based" Layoffs Compilation of news: - Microsoft lays off 10,000s of employees. - Justification is AI. - Microsoft requested 14000+ more H-1B workers this year. Compilation of comments: - The "subsidies" from low FED interest rates and section 174 ended 1.1.2022. - If a CEO says the board of directors that he overtired, then he is seen as incompetent. But not if AI is the reason. - Microsoft is under public pressure why it swaps domestic labour with foreign H1-B workers. https://lnkd.in/e4YYfqDW

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We used to hear about “Quiet Firing.” But currently on top of it what’s emerging now feels far more calculated, something quieter… and colder. A kind of “Silent Layoff.” Not an announcement. Not a sudden decision. But a slow, structured process. You start noticing it in subtle ways: 1. Performance conversations become more about numbers than impact 2. Metrics begin to define your worth PRs, story points, output 3. “Improvement plans” appear, sometimes independent of real performance 4. Documentation increases… conversations decrease And before you realize it, the outcome is already decided. The hardest part? You’re not just navigating your own growth anymore. You’re navigating a system of comparison. Sometimes, it quietly turns peers into competition. And work into constant validation. This isn’t about one company or one policy. It’s about a broader shift in how performance is measured and perceived. Where visibility can outweigh value. And consistency isn’t always enough. And somewhere in all of this, a bigger question remains: Are we optimizing for performance… or slowly normalizing pressure as culture? Because behind every metric, there’s a human being trying to build something meaningful not just measurable. Personally, I believe in taking on challenges. But not at the cost of burning yourself just to create numbers. #WorkCulture #Tech #Careers #Perspective #MentalHealth

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I rarely agree with President Trump, but his order to curtail the abused H1b program is a long overdue correction. In the 1990s, I showed my friend John a paper by Norm Matloff warning about the harmful effects of the H1b program on American tech workers. The program gave today's tech oligarchs a cheap, compliant, pliable overseas work force at the expense of Americans. This wasn't about getting the "best and brightest". This was about turning a professional, American workforce into one where older workers became expendable and those that remained saw job conditions, opportunities and benefits gradually deteriorate. Rather than undercut American workers, we should nurture and develop our own country's intellectual talent. Too often, we cut education or career tech funding, disadvantaging promising but underprivileged students. Fledgling U.S. businesses and ideas are ignored. All the while countries, like China, who DO see value in supporting a knowledge based economy are able to easily surpass the U.S. in EVs, green energy, and soon AI and automation. I see lots of Chicken Littles claiming this move will lead to a collapse of the tech industry. But the most vocal are those that have financially benefitted from the exploitation and misery of U.S. tech workers.

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very well written blog on how Zepto used self hosted llama with RAG for improving result relevancy by spell correction of multilingual/malformed search queries https://lnkd.in/gaYnz79Q LLM summary: The Problem: Users frequently type misspelled or vernacular queries using English lettering (e.g., "kothimbir" for "coriander"), leading to poor search results and dropped sessions. Existing language models struggle with such inputs. The Solution: MVP with Llama3: Zepto started with Meta's Llama3-8B model, self-hosting it on Databricks to ensure scalability and cost control, moving away from external APIs. Instruct Fine-Tuning: They heavily relied on prompt engineering and system instruction design to teach the LLM specific behaviors. This involved: Role-specific system messages ("You are a spell corrector that understands multilingual inputs"). Few-shot examples for diverse input-output pairs. Stepwise prompting (detect, correct, translate). Structured JSON outputs for seamless integration. This improved accuracy without expensive full model fine-tuning. Retrieval Augmented Generation (RAG): To address the issue of the LLM "correcting" brand names and to improve efficiency, Zepto implemented RAG. Architecture: User queries are converted into embeddings, matched against product embeddings in a Vector DB, and the retrieved contextual information (product titles, brand names, spelling variants) is dynamically injected into the LLM's prompt. why RAG: Robustness to Noisy Inputs: Semantic retrieval handles severe misspellings. Brand Awareness: Only query-relevant brands are injected, preventing incorrect "corrections." Prompt Efficiency & Latency Reduction: Reduced token count (30-40% smaller prompts) and improved inference latency (~18s faster per batch). Dynamic Learning Capability: Automatically adapts to new products or brands in the catalog without retraining. Complementary Approach: Zepto also incorporates implicit user feedback by monitoring query reformulations (e.g., "banan chips" to "banana chips") within a short time window. This helps auto-learn new misspelling variants and enrich training datasets. Impact and Results: The RAG-enhanced LLM system now acts as a domain-aware, multilingual spell corrector. This led to a 7.5% increase in conversion rates for the impacted queries, significantly improving user experience, search relevance, and business metrics.

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How LLMs Work Is Not Magic Anymore A few weeks back I had taken a deep dive into the world of Large Language Models (LLMs) — and it’s been truly fascinating to unpack what goes on under the hood of models like GPT, Claude, and others. What once felt like “magic” now feels like brilliant engineering and elegant math. Through studying the Transformer architecture, I’ve developed a clear understanding of how these models actually think and learn to generate coherent, human-like text. Here’s what I’ve explored in detail 👇 🔹 Encoder–Decoder Architecture (Seq2Seq Models) – Understanding how data flows through the encoder to create context-rich representations, and how the decoder turns that into meaningful output. 🔹 Tokenization & Embeddings – How text is broken down into tokens and represented as dense vectors in a high-dimensional space. 🔹 Self-Attention Mechanism – The core innovation that allows models to understand relationships between words across long contexts. 🔹 Transformers – The building blocks of modern LLMs, replacing recurrence with attention for parallelism and scalability. 🔹 ReAct Framework – Combining reasoning and action to enhance the model’s problem-solving and decision-making capabilities. 🔹 Mixture of Experts (MoE) – The concept of dynamically routing tasks to specialized “expert” subnetworks to improve efficiency and performance. The deeper I go, the more I appreciate how systematic, scalable, and elegant these architectures are. ✨ What’s most exciting is that LLMs are no longer just black boxes to me — I can now understand and explain the “why” behind their behavior. #AI #MachineLearning #LLMs #Transformers #DeepLearning #NLP #ArtificialIntelligence #LearningJourney

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Ever wondered how #Amazon 's #BarRaisers decide who gets #hired? I’ve been on both sides of that table — as a candidate and as a hiring manager — and here’s what most people don’t realize 👇 Amazon’s Bar Raisers aren’t just checking if you’re smart. They’re testing if you think like an Amazonian. Here’s what they look for: 💡 1️⃣ Clarity in Ambiguity When the question is fuzzy or open-ended, do you freeze... or do you structure it? Bar Raisers love candidates who dive deep, make trade-offs, and reason out loud. Because that’s what real ownership looks like on the job. 🧠 2️⃣ Curiosity in Action Smart isn’t “knowing it all.” It’s knowing what to learn and how fast. They look for people who stay curious and adapt when the ground shifts. 🧩 3️⃣ Depth Over Breadth Are you just executing tasks… or shaping them? Surface-level answers signal compliance. Depth signals backbone — a key Amazon principle. 💬 Reality check: You don’t need perfect answers to impress a Bar Raiser. You need structured thinking, clear ownership, and curiosity under pressure. 💡 Takeaway: Bar Raisers don’t pick the smartest person in the room — they pick the one who can stay calm, think deeply, and own the problem. 👉 Have you ever had an Amazon interview that felt more like a debate than a Q&A? That was probably a Bar Raiser round. #Amazon #Hiring #LeadershipPrinciples #CareerGrowth #Interviews

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Is the golden era of pure AI research at Meta officially ending? The departure of VP Jitendra Malik to Amazon Robotics signals a massive shift in the AI landscape. For years, FAIR (Fundamental AI Research) was the gold standard for academic freedom within Big Tech. But as the focus shifts aggressively toward commercial products, the culture is changing. Here is what this exodus tells us about the current state of AI: 📍 The Pivot is Real Meta is moving from "what is possible?" to "what is profitable?" The aggressive push for commercial AI products is reportedly leaving pure researchers feeling sidelined. 📍 The Talent War is Shifting Malik isn't just leaving; he is joining Amazon’s robotics efforts. This suggests the next frontier isn't just LLMs, but embodied AI and physical automation. 📍 The "Brain Drain" Effect With reports citing former employees describing the division as "dying a slow death" and following other high-profile exits, retaining top-tier research talent requires more than just high salaries—it requires a mission they believe in. Think of this like a university lab being turned into a factory floor. While the factory produces immediate value, the explorers who thrive on discovery often pack their bags when the goal becomes pure efficiency. The lesson for tech leaders and developers? We are entering the era of Applied AI. The companies that win won't just be the ones doing the best research; they will be the ones who can successfully transition that research into tangible products without alienating the minds that built it. If you are in AI, now is the time to bridge the gap between theory and deployment. Do you think Big Tech can still sustain pure research labs, or is everything destined for productization? Let me know in the comments. 👇 #AI #TechNews #Robotics #Leadership #Meta #Amazon

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Agents don’t just need more context, they need memory that is organized, reliable, and grounded in evidence. Excited to share our latest Contextual AI post on this. #RAG #AgentMemory #ContextEngineering

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Nancy Chu shared her learning with us in our last community session and summarized them here in substack article. Key takeaways for me were: - Product Sense is non-negotiable in the age of AI — not just for PMs, but for engineers as well. - Tools like Vibe (Lovable/V0) or Velocity (Claude Code) are meant to demonstrate your product sense and execution ability — not just your ability to build. - As you move beyond L6, you’re expected to consistently demonstrate strong product sense, execution excellence, and leadership across all interview rounds. - Past L6 (especially in AI roles): Interview questions are less abstract (e.g., “design a fridge for the blind”) and more grounded in what you have actually shipped. Even at MAANG-level companies, discussions center more on real-AI impact from your CV and ownership. Pls watch, read or listen- but don't miss. https://lnkd.in/gGVziRzQ

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Stop chasing the AI hype, start chasing the Logic. 📊 We see the flashy tweets and the superhero-like promises of AI. But beneath the "Iron Man" exterior of every AI model lies a core made of pure Mathematics and Statistics. If you want to be a true Data Scientist, you can't skip the fundamentals. Linear Algebra, Calculus, and Probability are the real superpowers. Without them? "Toh tum data scientist banne ka sapna bhul jao." Check out my latest breakdown of the AI hype vs. reality: https://lnkd.in/dv--Ux-a

Meaningful post from Richardson M. Explained succinctly. With the rise in Artificial Intelligence’s software engineering competency Software Engineers will become Software Editors and Architects

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There is a quiet habit in many organizations that I believe we should rethink. Calling engineers "resources". At first glance, it may sound harmless. But words shape culture, and the difference between an engineer and a resource is deeper than terminology. A resource is something you allocate. A resource is interchangeable. A resource is planned in spreadsheets and capacity charts. But an engineer is something entirely different. An engineer is a problem solver. An engineer designs systems that didn’t exist before. An engineer takes ambiguity, constraints, and trade-offs and turns them into working reality. You cannot allocate creativity. You cannot schedule curiosity. You cannot spreadsheet innovation. When we call people "resources", we unintentionally reduce them to units of output. When we call them engineers, we acknowledge their judgment, craft, and ownership. The difference shows up in how teams behave. When people are treated like resources: • Work is assigned. • Thinking is minimised. • Ownership is diluted. When people are treated like engineers: • Problems are shared. • Ideas are encouraged. • Systems improve. Great companies are not built by managing resources efficiently. They are built by empowering engineers to think, question, and create. Language matters because culture follows language. Maybe the shift is simple: Instead of asking, "Do we have enough resources?" Ask, "Do we have the right engineers solving the right problems?" Small change in words. Massive change in mindset. #eBay #EngineeringCulture #Leadership #TechLeadership #SoftwareEngineering

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