Ashish Singhi

Go Performance Tuning at Scale: Zepto’s pprof Journey (Part 1) At Zepto, performance isn’t just a priority — it’s an obsession. The best part about making systems more efficient is that it not only boosts speed but also drives real cost savings. In this blog, Akash Agarwal shares our journey of performance tuning — the challenges we tackled, the wins we unlocked, and how the process didn’t just improve our systems, but also shaped the way our engineers think about building scalable, efficient systems. Blog link: https://lnkd.in/gtzdtmGg #ZeptoTechXpress #PerformanceTuning Shivanshu Kumar Tiwari Ashish Bhatt Sumit Gupta Aditya Haridas Menon Harshal Khodifad Abhinav Nimesh Uday Solanki Devansh Chaudhary Devansh Goswami Sumit Ojha Vivek Gover Prashant Giri Balram Vishwakarma Shubham Prashar Anoop Singh Mayank Agarwal Nikhil Mittal

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We once had a backend incident that, on paper, should've been a major outage. But nothing happened. Users kept browsing, ordering, and moving on with their day. Our fallbacks quietly took over. Under our old metrics, this would've counted as downtime. But was it really? That question changed how we think about uptime at Meesho. We stopped measuring system health and started measuring user outcomes - can the customer complete their order? Can the seller fulfill it? Simple shift. Massive impact on how we engineer, invest, and own reliability. https://lnkd.in/gqDnb9q3 Siddharth Gupta Amlan Sekhar Das

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“Elastic has become PepsiCo’s gold standard for telemetry ingestion, correlation, and resolution velocity. It now sets performance baselines across our observability ecosystem.” — Vinod Chilakalapudi, Director - Observability at One PepsiCo Operations Center See how PepsiCo standardized with Elastic to achieve Full Stack Observability, cutting incident resolution times by 30% and hardware costs by up to 25% annually: https://gag.gl/uQmBXk

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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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Something unsettling happened recently that stayed with me. Our cook’s young son was hospitalised back home in India. The medical expenses quickly climbed to nearly ₹60,000 per day. What made it harder to process was that he earns in Dirhams and could have comfortably afforded basic health insurance. The real issue was awareness. He had never seriously considered it. I have seen the same pattern earlier in Bangalore with our domestic workers. A single medical emergency was enough to erase savings built over a decade. Not because they were reckless, but because no one ever explained how protection works. Most families in lower-income groups aren’t aware that protection exists at a price they can manage. Government schemes feel distant. Private insurance sounds like a luxury. Yet basic family floater plans from insurers like New India Assurance, National Insurance, or Oriental Insurance can cover ₹3-5 lakh for roughly ₹8,000-12,000 a year. That’s less than a single day’s hospital bill, and often less than what families end up borrowing when crisis strikes. This is not about blame. It is an access and education gap. Many of us are in a position to help bridge it. We understand insurance, paperwork, and basic processes that feel overwhelming to others. What we can do is start conversations. Explain why health insurance matters. Help shortlist a plan that fits their budget. Most importantly, show them how to actually use it when something goes wrong. This is about dignity and preparedness for families we work with, and for the broader community we are all part of. If you employ domestic staff, consider taking one small step this year. Help one family get covered and understand how to use that cover. If you’ve done this, or seen what actually works, I’d genuinely like to learn. What approach made the difference?

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I just published a detailed deep dive on designing a Commerce Intelligence Platform for India’s fragmented quick-commerce ecosystem. Brands today sell across Zepto, Blinkit, Flipkart Minutes, Amazon — but struggle to answer: • What is my true category market size? • What is my brand share vs competitors? • Why did my organic rank drop? • Which new launches are gaining momentum? This article covers: ✔ Full database schema (entity → fact → mart layers) ✔ Rank-based market size estimation using Zipf’s Law ✔ Sponsored vs organic separation logic ✔ Tier-based accuracy system (100% accuracy for top SKUs) ✔ Microservices architecture ✔ LLM integration for classification, entity resolution & root-cause analysis ✔ SQL queries for every major business insight It’s a complete master architecture — end to end. Would love feedback from product, data, and commerce folks. #Ecommerce #QuickCommerce #SystemDesign #DataEngineering #AI #ProductArchitecture

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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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Growing up in India, where the default expectation was often to pursue a career in medicine, a wise man once joked: "If everyone becomes a doctor, who will be the patient?" I can't help but wonder if a similar logic applies: if everyone is a builder, who is left to be the consumer? :) #fridayfun #tech

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I recently ordered fish on FreshToHome in Hyderabad. The delivery was seamless — but what really intrigued me was the technology orchestration behind a highly perishable, real-world supply chain. So I went deeper to understand how AI, data, and IoT actually make this work. Here’s what stood out: 1) AI-driven sourcing & pricing: FreshToHome doesn’t rely on traditional mandis or manual negotiations. Instead, they run a proprietary, AI-backed commodities exchange that connects fishermen directly to demand. At the core is algorithmic price discovery driven by: • Historical demand by city and SKU • Seasonality of catch and consumption • Current pipeline inventory • Lead-time constraints and spoilage risk These signals are used to recommend bid prices and procurement volumes — continuously optimizing cost, fairness, and freshness. 2) Predictive demand planning (before you order): Fresh fish is a zero-buffer problem. AI forecasts city-level and hub-level demand using: • Past order behavior • Day-of-week and seasonal trends • SKU-specific consumption patterns By the time you place an order in Hyderabad, procurement decisions were already made hours earlier. 3) IoT-based cold-chain observability Cold chain here is not assumed — it’s instrumented. IoT sensors track: • Temperature (0–4°C) • Transit time • Route adherence Any deviation is flagged in real time. Freshness becomes a measured SLA, not a promise. 4) Data-driven quality & safety checks: Before fish enters the city supply: • Each batch goes through extensive safety and quality checks • Results are logged and tied to batch-level metadata Quality is treated as a data attribute, enabling traceability and fast isolation if issues arise. 5) Logistics as a controlled execution layer: Only after all the above decisioning does logistics execute: • Overnight refrigerated transport to Hyderabad • Short-duration storage at city cold hubs • Hyperlocal last-mile delivery under cold conditions Logistics executes decisions — it doesn’t make them. The real engineering challenges (technical debt) • Intermittent connectivity at coastal edges • Tight freshness windows with no room for model error • Data consistency across procurement, quality, and delivery • Human-tech interfaces for fishermen and ops teams This is applied AI in the physical world — where mistakes spoil inventory, not dashboards. Big takeaway FreshToHome isn’t a food company using tech. It’s a technology-led distributed decision system operating on fish instead of packets. Some of the most interesting AI systems today aren’t in labs — they’re quietly running supply chains at 3 AM, inside refrigerated trucks. #AI #AppliedAI #IoT #SupplyChainTech #SystemDesign #EngineeringLeadership #ColdChain #FreshToHome #Operations #TechnologyLeadership #EngineeringLeadership #AppliedAI #AIEngineering #SupplyChainTech #LogisticsTech #SystemDesign #IndustrialAI #DigitalTransformation

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FINALLY!! In addition to the push from government, wall street, and customers, there is now a push from employees too!! In a bold move, over 1,000 Amazon (India) employees are raising concerns about the company's rapid AI expansion. They're calling for sustainable practices and ethical guidelines to protect jobs and the environment. (You can read more about their demands here: https://lnkd.in/gxrnRZKs) It's crucial for tech giants to balance innovation with responsibility. As we embrace AI, let's ensure it's a force for good, not a source of harm. What are your thoughts on this?

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People inside these companies have been saying this for years. It’s a numbers game. In Hindi: “number banane hain.” In English: “what visibility will I get out of this?” Once that becomes the dominant incentive, everything else becomes secondary — including engineering excellence. So leadership talks about quality. Decks talk about quality. Keynotes talk about quality. But day-to-day behavior optimizes for optics. And then we land here: Microsoft “wants to rebuild trust in Windows 11” after years of obvious usability and reliability issues. Which is basically a modern replay of Vista. Next come the numbers: “We fixed X bugs.” “We improved Y metrics.” “We rebuilt trust.” Everyone knows this dance. When positioning beats problem-solving, Rahul doesn’t lose because he’s weaker. The system loses because it systematically rewards Amit’s skillset over actual outcomes. And then we act surprised when products rot.

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About a month ago, I had the opportunity to speak at Redis Delhi, alongside some eminent industry leaders — Suman Guha (Tata CLiQ / Tata Digital), Murali Mohan Chakravarthy (Entain), and Rhythm Goyal (Monotype) My session ran for ~25 minutes and focused on how Redis has evolved far beyond its original role as an in-memory cache, and how teams are using it today to solve modern, large-scale problems. We discussed: - Vector search and its role in modern search systems - Bloom filters for efficiently checking user existence at scale (probability at scale!) - Sorted Sets as a practical primitive for rate limiting - Why in-memory systems like Redis became popular for agentic workflows instead of disk storage, aka GPU runtime costs What stood out the most was the audience—an enthusiastic, well-curated mix of experienced engineers and young, fast-growing talent. The questions and discussions made it a genuinely engaging session. It’s always a pleasure learning and collaborating with Jatin Gupta, Omkar Konnur, Justine Hermoso, and Rakhilaa Rajasheker. Thanks to the Redis Delhi team for having me and everyone who joined 🙌

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Artificial Intelligence, H1B Visa & The Test Of Accountability  I have been thinking about the brouhaha over the H-1B visa and believe a deeper look at Indian IT is in order. The sector’s under fire on two fronts:  →Due to the hike in H1B visa fees to $100,000. →From the disruption by artificial intelligence. Indians make up 70% of H1B visa recipients, with China trailing behind at 12%. The new fees surpass 60% of Indian workers’ annual salaries in the US, as they draw lower median wages, hitting us more than global peers. Indian IT companies could also see a drop in profit margin.  Besides that, AI is threatening to eliminate entry-level jobs. OpenAI CEO Sam Altman said that he's confident customer service jobs will be among the first to go, along with those who do routine coding tasks. It’s disheartening that highly skilled professionals — from developers to creators — who help build the system are under threat. They are stuck in bloated teams and face delays as they await endless approvals. While they bring value, creativity, and problem-solving skills — essential to enable differentiation in products and services — they are struggling to prove their worth. The service-model mindset is partly to blame. The role of the 'knowledge worker' has been diluted, from someone who applied expertise in solving complex problems and innovating to merely executing tasks. By including people in the decision-making chain who add no value but facilitate communication, we end up hindering agility. Corporate organisations are filled with pointless mid-management roles that reduce accountability. It’s possibly the result of an experiment gone wrong or an inability to fix the basics. Anthropologist David Graeber used the term "bullshit jobs" to describe such redundant positions in his 2018 book. In the era of AI, when information can be aggregated and distributed without intervention, it’s a wonder why we retain such roles. The lack of accountability that drives the current top-down system has to be fixed. So far, the skilled went abroad to become system builders, while India was left with mid-level staff. Now, we need to reframe India as a launchpad. Educational institutions, startups, and R&D labs must turn co-creators and lead skilled talent to use AI as a catalyst for creation. We need to set clear expectations for tech workers, provide resources and autonomy, give feedback, and establish a culture of ownership. The NEP 2020, which aims to integrate application-based learning within mainstream education, could prove to be a game-changer for early years, but companies also need to create pathways and opportunities for innovation at home. It’s only through such interventions that we can change the mindset that the only path to success lies abroad. So, yes, we do need to rethink some roles. But it’s not that of a highly skilled tech professional. #ArtificialIntelligence #H1BVisa #Software #IT #Technology

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I came across a fascinating article written by famous Gaurav Sen that breaks down how Netflix tackled one of the toughest problems in large-scale systems — connection churn and uniform traffic distribution using an approach called Backend Subsetting, built on top of consistent hashing. At hyperscale, traditional consistent hashing and random assignment aren’t enough. If you hash every request to all backend servers, adding or removing a server can force millions of connection reassignments — leading to instability and performance spikes. Netflix’s solution was elegant and practical: 👉 Subsetting: Instead of spreading every request over the entire pool of servers, traffic is divided into multiple subsets (or shards). Each client is mapped to a specific subset, and load balancing happens within that smaller group. Why this matters: - Reduced connection churn by ~10× → far fewer open connections and less resource waste on the API gateway. LinkedIn - When a server is added or removed, only that subset’s traffic is affected — not the whole system. LinkedIn - Upgrades and failures become predictable and boring events, not outages. Bottom line: Load balancing in distributed systems isn’t just about fairness — it’s about predictable scaling, minimizing churn, and isolating failure blast radius. The “backend subsetting” idea helps isolate impact and keeps traffic stable even during dynamic changes. If you’re building highly available systems, this is a neat pattern to understand — and an even nicer reminder that sometimes the best algorithm is the one that plays well with real-world failure modes. Here is the link of the article for interested folks ! https://lnkd.in/gxJTvdYP #DistributedSystems #SystemDesign #LoadBalancing #Scalability #NetflixTech #Architecture #TechLeadership

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🤖 Swiggy integrates #MCP to enable #AI-native ordering 🍔 Swiggy becomes one of the first Indian consumer platforms to adopt Model Context Protocol (MCP) across food delivery, #quickcommerce, and dining out. 🧠 Users can now discover and place orders directly from AI tools like OpenAI ChatGPT, Anthropic Claude, and Google Gemini using natural language — without opening the app. ⚡ Instamart becomes the first quick-commerce platform globally to support MCP, enabling AI-driven access to 40,000+ SKUs in real time. 🔗 MCP acts as a secure, open bridge between LLMs and live systems, allowing AI models to interact with catalogs, availability, and workflows — not just static data. 🏗️ This marks a shift from surface-level AI features to deep infra-level AI integration, where apps become services callable by AI agents. 🇮🇳 After fintech and logistics, Indian consumer internet companies are now building AI-native foundations. 🚀 Commerce is quietly moving from apps → APIs → agents. https://lnkd.in/gTBquHk8

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Proud to help power conversational AI at scale for JioHotstar — transforming content discovery into real-time, personalized engagement for millions across India. https://lnkd.in/gjnaZDmA

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🏠 It’s 2025 — and buying a home with white money still feels like a challenge. Over the past few weeks, I’ve been exploring home options in Gurgaon — and what’s truly disturbing is how openly many sellers, including small developers and investors, ask for 30–40% payment in cash. We’re not talking small amounts — often over ₹1 crore — discussed casually, without hesitation or fear. Unaccounted cash doesn’t just hurt individual buyers; it distorts the entire housing ecosystem. It inflates prices, reduces formal credit flow, and erodes trust in developers. Even transparent builders find it hard to compete with cash-driven pricing. A recent report highlighted how even professionals with 100% white income struggle to buy homes without facing pressure for off-record payments. I personally don’t want to support this practice. I earn my salary in white, taxes are deducted at source, and every rupee is accounted for. Converting that into untraceable cash just doesn’t feel right — financially or ethically. This isn’t a Gurgaon problem; it’s a national issue. To fix it, we need policy and market alignment: ✅ Revise circle rates regularly to match actual market prices ✅ Align capital gains taxation to realistic valuations ✅ Incentivize full-disclosure transactions (lower stamp duties or simplified digital paperwork) ✅ Mandate digital payments beyond certain thresholds More than a decade ago, the Ministry of Finance identified real estate as a high-risk sector for unaccounted money. Today, with Aadhaar, PAN, and digital registries in place, the infrastructure for transparency already exists. What’s missing is consistent enforcement and the right incentives. Transparency shouldn’t be the exception — it should be the baseline. #RealEstate #Transparency #Housing #Gurgaon #India #Policy #BlackMoney #Governance #FinancialIntegrity #UrbanIndia

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