Nikunj Jain

History has a pattern most people ignore. When two brothers commit to one mission, outsized things tend to happen. These two just put India on the global AI product map. Mukund Jha and Madhav Jha didn’t start Emergent to “democratize coding.” They started it because app building was fundamentally broken. Especially for people building real businesses, not weekend side projects. Most people think building apps is hard because they’re non-technical. That’s a convenient myth. The real problem is the process. Too many tools. Too many handoffs. Too many systems that work fine until real users show up. And that’s exactly when founders can’t afford failure. So instead of hype decks and loud launches, these two brothers did something rare. They went quiet. No buzz. No flashy demos. No “AI will change everything” noise. Just months of grinding through the system, removing friction where it actually breaks. What came out of that effort became Emergent. Now here’s the part that should make you pause 👇 In just 7 months: 5M+ builders 6M+ real apps powered $50M ARR Users across 190 countries That kind of growth doesn’t come from hype. It comes from reliability. Which is why serious investors noticed. Early backing from Google AI Futures Fund, Gossamer Ventures, and SoftBank, followed by a $70M Series B. Not because it sounded exciting. But because it didn’t break when real users arrived. While most AI app builders stop at prototypes, Emergent goes end-to-end: Backend. Frontend. Database. Cloud. Payments. Deployment. Production-ready from day one. So what does this mean for the Indian Tech & IT landscape? This is the real shift 👇 1️⃣ India moves from services to software leverage When app creation becomes this fast and reliable, Indian founders can own products and IP, not just deliver projects. 2️⃣ Pressure on traditional IT models Billing hours and large dev teams won’t disappear overnight, but their dominance will shrink. Outcome-driven software beats manpower-driven software. 3️⃣ Faster startup cycles Founders no longer wait months to validate ideas. They can go from idea → production → users in days, not quarters. 4️⃣ Talent expectations will change The new edge won’t be “knows React or Java.” It’ll be: can you design systems, workflows, and outcomes? 5️⃣ India becomes a serious global builder, not just an executor Tools like this quietly shift India’s role from “cost-efficient tech” → product-first innovation hub. This isn’t just a startup success story. It’s a signal. Software is shifting from: "writing code to describing outcomes" And the teams that win won’t be the loudest. They’ll be the ones whose products don’t fail at scale. 👇 Let’s open this up Do you think tools like Emergent will disrupt Indian IT services or force them to reinvent themselves faster than ever? #IndianTech #IndiaAI #ArtificialIntelligence #AIProducts #StartupIndia #ProductManagement #ProductManager #ProductStrategy #AIInProduct #TechLeadership

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35 practical thoughts on system design Core Principles 1. Every system is a trade-off -> you never get speed, cost, and simplicity all at once. 2. Latency compounds -> every millisecond added across layers adds up to user pain. 3. Scalability ≠ performance -> one handles growth, the other handles speed. 4. Read vs. write paths -> scaling each requires completely different strategies. 5. Design for change, not perfection -> requirements will shift. Databases & Storage 6. Indexes are levers -> high-selectivity columns are worth indexing, low ones often aren’t. 7. Replication helps reads, partitioning helps writes -> don’t confuse the two. 8. Dual writes are a lie -> without coordination, you will see drift. 9. Event stores > queues (sometimes) -> better traceability, worse simplicity. 10. Cache invalidation is still the hardest problem -> freshness vs. performance is the eternal fight. Reliability & Consistency 11. Idempotency saves you -> retries without it will hurt. 12. Fail fast, fail loud -> silent failures sink systems. 13. Eventual consistency is a feature -> not a bug, but only if the business allows it. 14. Conflict resolution in active-active is business logic, not infra. 15. Durability isn’t free -> syncing across regions has cost and latency. Architecture Patterns 16. Microservices are an org structure, not a tech goal. 17. Monolith first, modular second, microservices last -> don’t jump too early. 18. Choreography scales, orchestration simplifies -> pick based on team maturity. 19. Serverless buys you focus, costs you control. 20. Queues don’t remove work, they smooth it. Observability & Operations 21. Tracing > logging -> logs tell you “what”, traces tell you “why”. 22. Metrics rot without ownership -> measure what someone actually uses. 23. Retries without backoff = DDoS on yourself. 24. Dead-letter queues aren’t optional -> every system has poison messages. 25. Levers > knobs -> design quick kill switches and controls to contain blast radius. Performance & Cost 26. Optimize the hot path, not the cold path. 27. Most bottlenecks live in the database, not the code. 28. Horizontal scaling beats vertical scaling ->until coordination kills it. 29. Warm caches mask bad queries -> measure against cold starts too. 30. The cheapest resource is disk, the most expensive is time. People & Process 31. The best architecture dies without documentation. 32. System design reviews aren’t about diagrams, they’re about trade-offs. 33. Small PRs are for speed, big PRs are for context -> balance both. 34. A senior engineer’s role in design is asking the uncomfortable “what if”. 35. No design survives first contact with production -> but good ones bend instead of break. Want to sharpen your instincts with daily practice? Check out joinenginuity.com One system design question every day, with streaks, badges, and leaderboards, and a dashboard to keep you sharp. Enjoy the rabbit hole!

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₹5,000 crores. 3.6 crore checkouts. 1 discount engine. It started with a simple question in 2022: How do we build a moat? The Indian e-commerce battlefield doesn't forgive mediocrity. When we started shopflo, we studied the landscape obsessively. Fierce competitors. Merchants with endless options. Everyone chasing the same holy grail: conversion rate. We asked: In a price sensitive market, what's the one lever Indian D2C brands pull most? Discounts. But Shopify was missing basic capabilities for India: 0. Capped discounts ("20% off, max ₹500") 1. User targeting (new vs returning customers) 2. Payment offers (extra 10% on UPI) 3. Complex cart rules (buy from 2 collections, unlock discount) That gap became our entry point. 3.5 years later: 💰 ₹5,000+ crore GMV processed 🎟️ 100 crore+ unique coupon codes generated 🧾 3.6 crore+ orders with shopflo discounts 💯 95%+ merchants rely on it daily The scale surprises me: 📈 1 brand processed ₹850 crore GMV through our coupons 🎯 Another ran 46 lakh+ discount applications 🏷️ A single brand created 30,000+ unique codes 📦 Our largest "Discount Set" generated 80 lakh coupons in one click Fun fact: 1 merchant kept pushing: "I need to generate lakhs of unique codes at once." We called it Discount Sets. Today, 190+ merchants have created 2,500+ discount sets. That one feature, born from a single merchant's demand, became one of our biggest moats. Their edge cases become your competitive advantage. What I'm most proud of: In 12+ years of building software, this is the most complex system I've ever architected. ⚡ Scale - millions of validations/day, 22ms avg response 🧠 Complex logic - infinite rule combinations across cart, user, payment dimensions 🏗️ Data structures - matching coupons against dynamic cart states 📐 Dynamic programming - optimal discount combination when stacking offers When a user applies 3 coupons expecting the "best deal," we're solving optimization in real-time. Cart items, eligibility rules, stacking policies; all in 22 milliseconds. That's not a CRUD app. That's computer science in production. The lesson: Sometimes the moat isn't flashy. It's the boring, gnarly, "just make discounts work" problem, solved so deeply that merchants can't imagine life without it. ₹5,000 crore of GMV later, that's what we built. Ankit Jayanthi Pratik Vinit Hemanth SAMEER

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If Surge Pricing Is Accepted Everywhere… Why Not Value Teachers the Same Way? We accept higher pay for complexity everywhere. Senior lawyers charge more for complex cases. Specialist doctors charge more for high-risk surgeries. Consultants earn lakhs for strategy. Tech architects earn more than junior coders. Nobody calls it greed. We call it expertise. But when it comes to teachers, society becomes sentimental. “Teaching is noble.” “Teachers shape the nation.” “Teachers are like God.” Beautiful words. But respect without compensation is exploitation wrapped in poetry. A teacher doesn’t just teach a subject. They shape thinking, confidence, discipline, emotional intelligence, and career direction. One sentence can change a life. One bad experience can damage it for years. And teaching today is not simple delivery. Handling 45 students. Managing board pressures. Preparing for NEET/JEE. Dealing with anxious parents. Creating digital content. Counselling teenagers in crisis. That’s not “just teaching.” That’s strategist + psychologist + mentor + evaluator + content creator — in one role. Complexity exists in education too. Coaching rank holders. Supporting slow learners. Designing curriculum. Building future-ready systems. If complexity deserves higher pay in medicine and law, why not in education? Here’s the hard truth: If we want world-class students, we must attract top 5% talent into classrooms. And top talent does not choose low-paying professions. Right now, brilliant graduates choose corporate jobs — not because they don’t respect teaching, but because society rewards corporations financially and teachers emotionally. You cannot build a world-class nation on gratitude alone. Yes, Agreed! Teaching is a service. But so is medicine. So is law. So is governance. Calling it service should not mean underpaid, overworked, and financially insecure. When teachers are stressed..... They burn out; They take excessive tuitions; They leave academia; Standards fall; And.....society complains. Higher pay doesn’t just reward teachers. It raises entry standards, accountability, professionalism, research, and student outcomes. A surgeon saves one life. A teacher shapes thousands over decades. Teaching has compound interest impact on society. If we understand ROI in finance, we must understand ROI in education. Underpay teachers — you attract mediocrity. Value teachers — you attract excellence. If we are willing to pay surge pricing for convenience, urgency, and entertainment… Surely shaping a human mind deserves premium value. Teaching is not charity. Teaching is nation-building. And.... nation-builders should never be underpaid!!! #ValueTeachers #RespectTeachers #NationBuilders

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Rapido – Please Explain This Logic This screenshot clearly shows “Bad Performance – 1 Order Cancelled.” But the real question is: What if the customer cancelled the ride? If a customer books a ride and then: • Does not arrive at the pickup point • Switches off their phone • Or cancels the ride themselves Why is the captain still getting “Bad Performance”? The captain spends time, fuel, and effort to reach the pickup location. If the customer cancels or becomes unreachable, how is that the captain’s mistake? Yet the system still shows it as captain cancellation, which directly affects: • Order allocation • Earnings • Captain performance rating • Motivation of captains who are honestly working This is not fair for captains. Simple logic: If the customer cancels → it should be counted as customer cancellation. If the captain cancels → then it should affect captain performance. We hope Rapido will review this system and fix it, because fairness is important for the people who keep the platform running. #Rapido #FairSystem #CaptainVoice #RespectCaptains #GigWorkers #uberdrivers #uber

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Product Requirement Documents (PRDs) are being challenged by prompting… but not just any prompts. ‘Prompt sets’ feel like the missing link between vision and execution of PRDs in the era of LLMs. They learn from circular learning loops and break away from traditional linear steps: requirement ➡️spec➡️build ➡️test ➡️ship. Extremely interesting and actionable post below by Aakash Gupta.

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How do giants like PhonePe or Zerodha handle crores of transactions without crumbling during peak hours? It's not magic; it's smart architecture. The old monolithic way can't keep up. The secret? A well-executed Microservices strategy. But just breaking down your application isn't enough. You need the right playbook. These design patterns are the foundation for building resilient, scalable systems ready for the market. Here are the essential patterns every developer and architect should master: 🔹 **Database Per Service Pattern** The Problem: A single, shared database creates a huge bottleneck and a single point of failure. The Solution: Each microservice owns and manages its own private database. This ensures true independence, loose coupling, and lets each service's data store be scaled separately. 🔹 **API Gateway Pattern** The Problem: How do your mobile and web clients talk to dozens of different backend services efficiently and securely? The Solution: A single, unified entry point that routes requests, handles authentication, rate limiting, and aggregates responses. Think of it as the main security desk and receptionist for your entire system. 🔹 **Circuit Breaker Pattern** The Problem: A failure in one downstream service (e.g., a payment provider) can cascade and bring down your entire application. The Solution: A proxy that monitors for failures. After a certain threshold, the ""circuit"" opens, and requests fail fast instead of waiting and hogging resources. This is crucial for building fault-tolerant systems. 🔹 **Saga Pattern** The Problem: How do you maintain data consistency for a transaction that spans multiple services (like an e-commerce order involving 'Order', 'Payment', and 'Inventory' services)? The Solution: A sequence of local transactions. If any step fails, the saga executes compensating transactions to undo the preceding steps, ensuring data integrity without complex distributed locks. 🔹 **Service Discovery Pattern** The Problem: In a dynamic cloud environment, how does one service find the network location (IP address/port) of another when instances are constantly being created and destroyed? The Solution: A central registry (like a dynamic phonebook) where services register themselves on startup and can look up others. 🔹 **Distributed Tracing (Observability)** The Problem: A single user request travels through 5-10 services. If it's slow or fails, where do you even start debugging? The Solution: Assign a unique Trace ID to each request at the gateway and pass it along to every service it touches. This allows you to visualize the entire request journey and pinpoint bottlenecks instantly. Mastering these patterns is no longer optional; it's a core competency for anyone who wants to build world-class products. #Microservices #SystemDesign #SoftwareArchitecture #DesignPatterns #SoftwareEngineering #CloudNative #DistributedSystems #Backend #Java #SpringBoot #Developer

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Domain expertise won't hurt you at Stripe. But it won't save you either, since you still need to be a strong generalist to pass the PM interview loop. A security-focused candidate interviewed at Stripe. Deep knowledge of infra and compliance. Got grilled by PMs from growth, platform, and revenue. None of them cared about security depth. At most companies, domain depth plus strong execution gets you through. But at stripe, the evaluation is different. In our candidate debriefs, here's the pattern we've seen: 1/ Your panel is distributed across the company ↳ Interviewers come from different product areas ↳ They're scoring you against standardized rubrics ↳ Your depth in security means nothing to the PM from growth 2/ The committee decides without your hiring manager ↳ Decisions happen on written feedback alone ↳ No one advocates for your domain expertise in the room ↳ By the time you're in team matching, you've already passed or failed The concrete failure: candidates who assume their target team will champion them. Sometimes they are not even in the room when the decision happens. What you can do about it: ↳ Build portable frameworks that translate across any interviewer ↳ Structure your answers so someone outside your domain can follow ↳ Align with operating principles any Stripe PM can recognize ↳ Stop over-indexing on domain-specific prep P.S. This is one slice from the Insider Loops Stripe guide. We captured the complete Stripe PM interview playbook at insiderloops(dot)com.

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I see a massive mistake right now. People evaluating India using a Silicon Valley lens. Same benchmarks. Same expectations. Same pattern recognition. It doesn’t work.. In India Distribution looks different. Consumer behavior moves faster. Trust is built differently. And the best founders? They’re not copying US companies. They’re building for how India actually operates. If you’re waiting for it to look familiar. You already missed the deal. 🇮🇳🚀

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𝗜𝗻𝗱𝗶𝗮’𝘀 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗣𝘂𝗯𝗹𝗶𝗰 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 stack has nailed interoperability. But the next test isn’t technical. It’s human. Because the real barrier in Bharat isn’t access to tech. It’s fluency with tech. You can give someone a UPI ID, a consent-based data layer, a prefilled KYC form. But if they don’t understand what they’re authorizing, we haven’t solved inclusion. We’ve just made exclusion quieter. 𝗧𝗵𝗲 𝗺𝗼𝘀𝘁 𝗼𝘃𝗲𝗿𝗹𝗼𝗼𝗸𝗲𝗱 𝗹𝗮𝘆𝗲𝗿 𝗼𝗳 𝗗𝗣𝗜 𝗶𝘀𝗻’𝘁 𝗰𝗼𝗱𝗲. 𝗜𝘁’𝘀 𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗲𝗳𝗳𝗼𝗿𝘁. 𝗜𝗻 𝟮𝟬𝟮𝟱, 𝘄𝗲 𝘀𝗮𝘄 𝘁𝗵𝗶𝘀 𝗮𝗰𝗿𝗼𝘀𝘀 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝘁𝗼𝘂𝗰𝗵𝗽𝗼𝗶𝗻𝘁s : UPI screens with 4 QR options confusion. AA flows with jargon like “FIU” abandoned. eSign journeys fail because users lack clarity about data sharing. What helped? Adding trust cues before design polish. Rewriting flows with field agents, not just Figma. Designing for edge cases elderly users, second-hand phone users, people switching between English and Hindi mid-journey. If 2024-25 was about building rails, 2026 must be about lowering friction on those rails for everyone, not just the digitally fluent. DPI is not done when it works. It’s done when it works for the least confident user in the room. 𝗪𝗵𝗮𝘁’𝘀 𝗼𝗻𝗲 𝗶𝗻𝘃𝗶𝘀𝗶𝗯𝗹𝗲 𝗯𝗮𝗿𝗿𝗶𝗲𝗿 𝘆𝗼𝘂’𝘃𝗲 𝗻𝗼𝘁𝗶𝗰𝗲𝗱 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗼𝘄𝗻 𝗽𝗿𝗼𝗱𝘂𝗰𝘁? 𝗟𝗲𝘁’𝘀 𝗺𝗮𝗸𝗲 𝗮 𝗹𝗶𝘀𝘁. #DigitalPublicInfrastructure #TechForBharat #FintechForAll #InvisibleBarriers #BharatStack #DPI2026 #ProductLeadership  #YogeshWrites #YogeshJadhavInsights

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Idempotency: the most underrated concept in distributed systems Retries are unavoidable in distributed systems. Timeouts and network failures mean the same request can be executed more than once. Any system that assumes exactly once execution is fragile. Consider a payment API. A client charges ₹1,000. The payment succeeds, but the response is lost. The client retries. Without idempotency, the user is charged twice. State transitions must be replay-safe. To prevent this, idempotency must be enforced before the state change. Each request includes a client-generated idempotency key that represents intent (for example, order_id + attempt_id). The service stores this key with a unique constraint and processes the request in a transaction. On retries, it returns the stored result instead of re-executing the charge. A payment should move from CREATED to SUCCESS only once; retries should read the existing state and return it. Most production issues do not come from failures, but from retries without idempotency. #tech #engineering #idempotency #software #transaction

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“Product management is garbage…. …if you have no idea what it is. My SDE friends in startups complain that their product manager is utterly useless because apparently, they add no value to the team. But if you work with a great PM, it will blow your mind to know how they can shape your workflow and efficiency.” I was talking to a friend who has been working in product for 4 years in 2021. He is good at what he does and is articulate as well. But when I asked him what he understands by product management, it was eye-opening. Now that I have experience working as a PM in early to mid-stage startups, I share a very similar opinion. I don’t care what we name a person who has to think of product vision, strategy, product discovery, user journey, value proposition, and market strategy. But if we wish to name that person as product manager, the value they can add to the company is unquestionable. Since 2021, I have been obsessed with the idea of becoming a great PM. In the process, I have watched 100+ hours of Product Management content through Podcasts and videos, read at least 2 dozen peer-reviewed papers, and also read 3 books on the same subject. It took so much time (and Marty Cagan’s content) to truly appreciate the essence of being a PM. No wonder freshers are confused about this role. There are lots of courses and content online but it lacks a first principles approach. Too much jargon. Product Management is all about managing 4 risks. 1) Value risk - does the product deliver value for users? 2) Usability risk - will the users be able to use the product? 3) Feasibility risk - is it technically possible to build the product? 4) Viability risk - even if all the above risks are mitigated, will it make business sense to build the product? Try to answer these above questions and you’re on your way to becoming a PM! If you are trying to switch to Product Management and looking for a community of learners, join this one with 1600+ members - https://lnkd.in/dVMnmJRu

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Forget the Silicon Valley Playbook. The 'Blinkit Effect' is the real secret behind India’s next $50B+ operational explosion. While some look at Blinkit as just a delivery app, VCs look at it as an Operational SEAL Team. From the high-pressure dark stores of 2021 to the disruptive founders of 2025, if you want to see where India’s execution-heavy decacorns are coming from, you look at the Blinkit Effect Here are my 5 VC-level insights into why the "Gurgaon Mafia" is currently rewriting the Indian execution playbook: 👇🏻 1) Prithvi Singh - Gameskraft Most gaming founders focus on "fun." Prithvi used his Blinkit-honed high-concurrency roots to build a platform that handles millions of real-time transactions without a glitch. He leveraged the "Zero-Latency" mindset to solve backend engineering hurdles that slower competitors simply couldn't code their way out of ⚡ 2) Varun Khurana - Otipy Varun realised early on that Agritech isn't a "farming" business; it’s a density problem. By treating every tomato like a time-sensitive delivery packet, he solved the "perishability" trap in farm-to-fork. Otipy’s hyper-local supply chain is a direct product of the 10-minute delivery DNA native to the Blinkit war room 🥦 3) Zaheer Khan - Ayu Health Proving that you can bring "Quick Commerce" rigor to healthcare. Zaheer didn't just focus on "Doctors"; he focused on a systemic pain point: fragmented hospital operations. By bringing "Blinkit-level" logistics to hospital networks, he is systematising an industry known for chaos, demonstrating the "Systemic-First" ambition of the modern Blinkit graduate 🏥 4) Manas Gupta - GobbleCube Mastering "Data-Driven Velocity." Manas took the intimidating world of e-commerce brand analytics and made it as fast as a flash sale. By focusing on real-time SKU-level precision and scalable data frameworks, he successfully navigated the "Insight Gap" for D2C brands that were drowning in slow data 📊 5) Abhishek Upadhyay - CubeAPM The invisible backbone of the 2026 enterprise. By launching an observability platform for complex digital stacks, Abhishek is ensuring that "Zero Downtime" is a reality for the global market. He proved that the Blinkit network doesn't just build apps; they build the infrastructure that keeps the world’s most complex systems running 🧊 The ➡️ Insight: The "Blinkit Effect" isn't about the bike or the bag; it’s about Urgency as a Culture. When your training ground is a 10-minute delivery window, your baseline for "fast" shifts. That psychological edge is what creates a high-velocity, $50B+ ecosystem. ➡️ The 2026 Blueprint: Don't just look for a good idea; look for an Execution Moat. The next unicorn won't just have a great product; it will have a founder who can survive the high-pressure "Blinkit" crucible and scale at 10x speed 📈

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The cost of convenience is most often privacy. More than money itself. Eg. Identity - Aadhaar - Surveillance in wrong hands Travel - DigiYatra - Private company and surveillance possibilities Health - ABHA - Medical record sharing Financial Data - Account Aggregators - Data leakage and concentration Why do you need centralisation of everything? It all leads to only 2 things - surveillance (in the name of trust) and taxation (to add to the burden of common man). The funny thing is, people often don’t really understand what privacy is. Then fall for digital arrest, cyber scams, surveillance like Pegasus spyware, etc.

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System Design interviews don’t reward memorization. They reward clarity. Because today, system design is no longer “for seniors only.” Even SDE1/SDE2 interviews test whether you can think at scale - users, latency, failures, cost, and reliability. That’s exactly what this guide covers. It breaks down 10 must-know system design architectures that show up again and again in interviews - along with the core components, scaling tricks, and the exact “interview gold points” for each system. Instead of random theory, it trains you to explain systems like an engineer. How requests flow, where bottlenecks happen, what components matter most, and what tradeoffs interviewers actually care about. The 10 Designs Covered Inside : 1. It starts with fundamentals like a URL Shortener - then expands into real-world architectures like: 2. A Messaging System (WhatsApp) that handles delivery, retries, ordering, read receipts, and offline users. 3. An Instagram Feed that teaches fanout-on-write vs fanout-on-read and ranking tradeoffs. 4. A Video Streaming system (YouTube/Netflix) that shows the encoding pipeline, chunking, and why CDNs dominate scale. 5. A Ride Sharing system (Uber) built on real-time GPS streams, ETA accuracy, and geospatial indexing. 6. A Web Crawler (Google) designed as a pipeline: discover → fetch → parse → index with deduplication. 7. An E-commerce system (Amazon) showing inventory consistency, idempotent orders, and event-driven flows. 8. A Food Delivery system powered by workflow engines and state machines for reliable tracking. 9. A Spotify architecture where streaming + personalization becomes the retention engine. 10. And a Dropbox file sharing system where metadata + versioning is the real product. If you can explain these 10 clearly, you’re already ahead of most candidates - because these designs cover almost every interview pattern: caching, queues, sharding, consistency, fanout, CDN, state machines, and observability.

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Wakefit gets listed on the exchanges tomorrow. Its IPO documents explicitly disclose tech spend as a % of revenue — 1–1.5% (excluding salaries), which is typical for a modern consumer brand. In FY25, revenue was ~₹1,300 cr, and tech spend was ~₹15 cr (~$1.5M) — the entire tech spend of a ₹1,000+ cr company. A good reminder that for most consumer brands, tech is still about keeping the lights on, not creating an edge. #wakefit #rhp #ipo #tech #analysis

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India’s Blue-Collar Paradox India is facing a unique conundrum. We have a massive pool of 450 million blue-collar workers, and according to a McKinsey report, around 70% of 90 million new jobs by 2030 are expected to be blue-collar roles. → The skilled-worker shortfall widened to 150 million in 2023, up from 138 million in 2020, according to TeamLease. →Infrastructure job postings rose 62% between August 2022–2023, but worker interest grew only 5%, as per Indeed.  →Deloitte's Blue Collar Workforce Trends 2025 report highlights an attrition level of 5-7%, workbench levels of 3%, and frequent absenteeism. High attrition leads to a constant re-hiring pressure. Since recruitment tends to be dependent on contractors/referrals, it also results in untrained hires and extra fees. But, why is India’s workforce lagging in skills? Systemic challenges, inadequate training infrastructure, and socio-economic barriers are some factors to blame.  Blue-collar jobs tend to be informal and workers can lack contracts, job security, access to training or social protection. They rely on on-the-job training rather than standardised programs. Consider this: Only around 4-5% of India’s workforce has received formal vocational or skill training, as compared with 68% in the UK, 75% in Germany, 52% in the US (52%) and 80% in Japan, as per TeamLease. The central government recently flagged 11 gaps in skilling India’s workforce. It highlights an unclear path of progression in vocational learning. Only 4.42% of India's population aged 15-29 have a recognised vocational certificate as compared to 96% in South Korea and 75% in Germany. It also mentions how labour market trends are often not considered while training, which affects work quality and ability. Consequently, only 41% of ITI and 29% of polytechnic graduates are deemed employable. As automation and new technologies reshape industries, there emerges a mismatch between the skills possessed and the ones needed. For instance, 81% of workers in key sectors (like construction) are unskilled, states a Knight Frank-RICS report. The training infrastructure also leaves much to be desired and can include outdated equipment, a shortage of instructors, and weak linkages with industry. Another issue is that upskilling opportunities are not easily accessible for rural and informal workers. There are structural issues to consider as well, including wage inequality, unsafe working conditions, lax enforcement of labour laws, and gender disparity. While existing job-matching platforms focus on connecting employers and workers, they neglect skill development. But without addressing skill gaps, we can only come up with temporary fixes instead of systemic solutions. While these are some of the challenges in upskilling, in the next post, we will be focusing on possible solutions to remedy the gap. Read this and more at https://lnkd.in/gFDcJXF4   #BlueCollar #Labour #Upskilling

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𝗟𝗟𝗠 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝘁𝗼 𝗼𝘂𝗿 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝘁𝘂𝗿𝗻𝗲𝗱 𝗱𝗲𝗯𝘂𝗴𝗴𝗶𝗻𝗴 𝗶𝗻𝘁𝗼 𝗮 𝗻𝗶𝗴𝗵𝘁𝗺𝗮𝗿𝗲. 𝗛𝗲𝗿𝗲'𝘀 𝗵𝗼𝘄 𝘄𝗲 𝗳𝗶𝘅𝗲𝗱 𝗶𝘁 𝗶𝗻 𝟱 𝗱𝗮𝘆𝘀 𝘄𝗶𝘁𝗵 𝘇𝗲𝗿𝗼 𝗻𝗲𝘄 𝘁𝗼𝗼𝗹𝘀. 𝗧𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: A single user request triggers 200+ LLM calls. Which one failed? Where did context break? Our MTTR became as high as 7 hours. As a Startup, we cannot afford to have our reputation at stake. 𝗧𝗵𝗲 𝗦𝘁𝗮𝗿𝘁𝘂𝗽 𝗗𝗶𝗹𝗲𝗺𝗺𝗮: Everyone suggested: LangSmith, LangFuse, Helicone. - Evaluating tools = 2-3 weeks - Every new tool = Learning curve + cost + vendor management - We don't even know what we need yet 𝗙𝗶𝗿𝘀𝘁 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀: What exactly do we need to debug? Input sent, response received, post-processing results, and relationships between 200+ calls. The insight: Not a logging problem. It's a 𝘁𝗿𝗮𝗰𝗶𝗻𝗴 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. Each LLM call is stateless. Our application maintains context. Same applies to observability. 𝗧𝗵𝗲 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: We already had Grafana Loki, Tempo, and OpenTelemetry. The breakthrough: Connect all LLM calls under one parent span. 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 (𝗿𝗲𝗳𝗲𝗿 𝗱𝗶𝗮𝗴𝗿𝗮𝗺): 𝟭. 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗟𝗮𝘆𝗲𝗿 OpenTelemetry emits logs and traces 𝟮. 𝗟𝗼𝗴𝘀 (𝗚𝗿𝗮𝗳𝗮𝗻𝗮 𝗟𝗼𝗸𝗶) Captures each LLM interaction: - trace_id & span_id - Message sent & response received 𝟯. 𝗧𝗿𝗮𝗰𝗲𝘀 (𝗚𝗿𝗮𝗳𝗮𝗻𝗮 𝗧𝗲𝗺𝗽𝗼) Parent span connects all related calls: - "Analyze PDF" → Factor 1 (Profit) → Factor 10 - Each factor → LLM Call 1... Call 20 Shows serial vs parallel execution, timing 𝟰. 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗨𝗜 (𝗚𝗿𝗮𝗳𝗮𝗻𝗮) Query by span_id → Complete journey with all logs 𝗧𝗵𝗲 𝗥𝗲𝘀𝘂𝗹𝘁𝘀: - MTTR: 7 hours → 30 minutes - Timeline: 5 days(2 design + 2 dev + 1 infra) - Cost: $0 in new tools - Complexity: Zero new vendors 𝗞𝗲𝘆 𝗗𝗲𝘀𝗶𝗴𝗻 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀: 𝗪𝗵𝘆 𝗧𝗿𝗮𝗰𝗲𝘀 𝗢𝘃𝗲𝗿 𝗟𝗼𝗴𝘀? Logs show events. Traces show relationships. For 200+ interconnected calls, relationships matter. 𝗪𝗵𝘆 𝗘𝘅𝗶𝘀𝘁𝗶𝗻𝗴 𝗦𝘁𝗮𝗰𝗸 𝗢𝘃𝗲𝗿 𝗡𝗲𝘄 𝗧𝗼𝗼𝗹𝘀? Existing stack solved 80%. New tools solve 100% but cost 5x in time, money, and complexity. 𝗪𝗵𝘆 𝗣𝗮𝗿𝗲𝗻𝘁-𝗖𝗵𝗶𝗹𝗱 𝗦𝗽𝗮𝗻𝘀? Mirrors how our application maintains state. One request → Multiple factors → Multiple LLM calls. 𝗦𝘁𝗮𝗿𝘁𝘂𝗽 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗟𝗲𝘀𝘀𝗼𝗻𝘀: Don't: Add new tools before understanding the problem Do: Define what you need first (saved 3 weeks) Don't: Evaluate 5 tools when you don't know what you need Do: Use existing stack, learn what's missing, choose deliberately Don't: Wait for the "perfect" solution Do: Ship 80% solution in 5 days, iterate based on production issues We'll eventually need specialized AI observability tools. But not today. Tomorrow, we'll know exactly which tool solves which problem. That's 𝗦𝘁𝗮𝗿𝘁𝘂𝗽 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: Choose simple over perfect solution. Solve for 80%. Iterate to 100%.

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DC-DC converter efficiency principles just saved me ₹50,000 in ad spend. Here's how engineering logic crushed marketing waste. Last month my Meta campaigns were bleeding money. ₹2.5 lakh budget. ₹80 cost per lead. Sound familiar? ↳ 𝗧𝗵𝗲 𝗮𝗱 𝘀𝗽𝗲𝗻𝗱 𝘄𝗮𝘀𝘁𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 Your campaigns run like inefficient power supplies. High input. Low output. Maximum heat generation. Most marketers accept 60% efficiency rates. They think conversion loss is normal. Budget goes in at 100% Platform fees eat 15% Wrong audience targeting wastes 25% Poor creative performance loses 20% Bad landing pages kill 15% You end up with 25% actual results. The rest becomes expensive heat. ↳ 𝗕𝘂𝗰𝗸 𝗰𝗼𝗻𝘃𝗲𝗿𝘁𝗲𝗿 𝘄𝗶𝘀𝗱𝗼𝗺 𝗮𝗽𝗽𝗹𝗶𝗲𝗱 Power engineers never accept such waste. Buck converters achieve 95% efficiency rates. They do three things right. ☑ Control switching frequency precisely ☑ Minimize resistance in current path ☑ Reduce energy loss at each transition Your ad campaigns need the same discipline. Every rupee must flow with purpose. Every click must convert with intention. ↳ 𝗖𝗮𝗺𝗽𝗮𝗶𝗴𝗻 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗽𝗮𝗿𝗮𝗹𝗹𝗲𝗹 I treated my Meta ads like a power circuit. Input power equals ad budget. Output power equals qualified leads. Efficiency equals leads per rupee spent. Started measuring conversion at every stage. Impression to click efficiency Click to landing page efficiency Landing page to lead efficiency Lead to customer efficiency Found the biggest energy drain. Wrong audience segments were burning ₹1,200 daily. ↳ 𝗧𝗵𝗲 ₹𝟱𝟬,𝟬𝟬𝟬 𝘀𝗮𝘃𝗶𝗻𝗴𝘀 𝗿𝗲𝘀𝘂𝗹𝘁 Applied power supply optimization logic. Reduced switching losses by tighter targeting. Minimized resistance through better creative flow. Eliminated heat generation via audience precision. 𝗥𝗲𝘀𝘂𝗹𝘁𝘀 𝗮𝗳𝘁𝗲𝗿 𝟯𝟬 𝗱𝗮𝘆𝘀. Cost per lead dropped from ₹80 to ₹35. Same budget. 2.3x more qualified leads. Monthly savings crossed ₹50,000. 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗽𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀 𝘄𝗼𝗿𝗸 𝗶𝗻 𝗺𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝘁𝗼𝗼. You just need to think like a circuit designer. What's the biggest energy drain in your current campaigns?

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